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Background Various limitations have impacted research evaluating reader agreement for Liver Imaging Reporting and Data System (LI-RADS). Purpose To assess reader agreement of LI-RADS in an international multicenter multireader setting using scrollable images. Materials and Methods This retrospective study used deidentified clinical multiphase CT and MRI and reports with at least one untreated observation from six institutions and three countries; only qualifying examinations were submitted. Examination dates were October 2017 to August 2018 at the coordinating center. One untreated observation per examination was randomly selected using observation identifiers, and its clinically assigned features were extracted from the report. The corresponding LI-RADS version 2018 category was computed as a rescored clinical read. Each examination was randomly assigned to two of 43 research readers who independently scored the observation. Agreement for an ordinal modified four-category LI-RADS scale (LR-1, definitely benign; LR-2, probably benign; LR-3, intermediate probability of malignancy; LR-4, probably hepatocellular carcinoma [HCC]; LR-5, definitely HCC; LR-M, probably malignant but not HCC specific; and LR-TIV, tumor in vein) was computed using intraclass correlation coefficients (ICCs). Agreement was also computed for dichotomized malignancy (LR-4, LR-5, LR-M, and LR-TIV), LR-5, and LR-M. Agreement was compared between research-versus-research reads and research-versus-clinical reads. Results The study population consisted of 484 patients (mean age, 62 years ± 10 [SD]; 156 women; 93 CT examinations, 391 MRI examinations). ICCs for ordinal LI-RADS, dichotomized malignancy, LR-5, and LR-M were 0.68 (95% CI: 0.61, 0.73), 0.63 (95% CI: 0.55, 0.70), 0.58 (95% CI: 0.50, 0.66), and 0.46 (95% CI: 0.31, 0.61) respectively. Research-versus-research reader agreement was higher than research-versus-clinical agreement for modified four-category LI-RADS (ICC, 0.68 vs 0.62, respectively; P = .03) and for dichotomized malignancy (ICC, 0.63 vs 0.53, respectively; P = .005), but not for LR-5 (P = .14) or LR-M (P = .94). Conclusion There was moderate agreement for LI-RADS version 2018 overall. For some comparisons, research-versus-research reader agreement was higher than research-versus-clinical reader agreement, indicating differences between the clinical and research environments that warrant further study. © RSNA, 2023 Supplemental material is available for this article. See also the editorials by Johnson and Galgano and Smith in this issue.

Background Perfusion imaging is important to identify a target mismatch in stroke but requires contrast agents and postprocessing software. Purpose To use a deep learning model to predict the hypoperfusion lesion in stroke and identify patients with a target mismatch profile from diffusion-weighted imaging (DWI) and clinical information alone, using perfusion MRI as the reference standard. Materials and Methods Imaging data sets of patients with acute ischemic stroke with baseline perfusion MRI and DWI were retrospectively reviewed from multicenter data available from 2008 to 2019 (Imaging Collaterals in Acute Stroke, Diffusion and Perfusion Imaging Evaluation for Understanding Stroke Evolution 2, and University of California, Los Angeles stroke registry). For perfusion MRI, rapid processing of perfusion and diffusion software automatically segmented the hypoperfusion lesion (time to maximum, ≥6 seconds) and ischemic core (apparent diffusion coefficient [ADC], ≤620 × 10-6 mm2/sec). A three-dimensional U-Net deep learning model was trained using baseline DWI, ADC, National Institutes of Health Stroke Scale score, and stroke symptom sidedness as inputs, with the union of hypoperfusion and ischemic core segmentation serving as the ground truth. Model performance was evaluated using the Dice score coefficient (DSC). Target mismatch classification based on the model was compared with that of the clinical-DWI mismatch approach defined by the DAWN trial by using the McNemar test. Results Overall, 413 patients (mean age, 67 years ± 15 [SD]; 207 men) were included for model development and primary analysis using fivefold cross-validation (247, 83, and 83 patients in the training, validation, and test sets, respectively, for each fold). The model predicted the hypoperfusion lesion with a median DSC of 0.61 (IQR, 0.45-0.71). The model identified patients with target mismatch with a sensitivity of 90% (254 of 283; 95% CI: 86, 93) and specificity of 77% (100 of 130; 95% CI: 69, 83) compared with the clinical-DWI mismatch sensitivity of 50% (140 of 281; 95% CI: 44, 56) and specificity of 89% (116 of 130; 95% CI: 83, 94) (P < .001 for all). Conclusion A three-dimensional U-Net deep learning model predicted the hypoperfusion lesion from diffusion-weighted imaging (DWI) and clinical information and identified patients with a target mismatch profile with higher sensitivity than the clinical-DWI mismatch approach. ClinicalTrials.gov registration nos. NCT02225730, NCT01349946, NCT02586415 © RSNA, 2022 Supplemental material is available for this article. See also the editorial by Kallmes and Rabinstein in this issue.

Since its initial release in 2011, the Liver Imaging Reporting and Data System (LI-RADS) has evolved and expanded in scope. It started as a single algorithm for hepatocellular carcinoma (HCC) diagnosis with CT or MRI with extracellular contrast agents and has grown into a multialgorithm network covering all major liver imaging modalities and contexts of use. Furthermore, it has developed its own lexicon, report templates, and supplementary materials. This article highlights the major achievements of LI-RADS in the past 11 years, including adoption in clinical care and research across the globe, and complete unification of HCC diagnostic systems in the United States. Additionally, the authors discuss current gaps in knowledge, which include challenges in surveillance, diagnostic population definition, perceived complexity, limited sensitivity of LR-5 (definite HCC) category, management implications of indeterminate observations, challenges in reporting, and treatment response assessment following radiation-based therapies and systemic treatments. Finally, the authors discuss future directions, which will focus on mitigating the current challenges and incorporating advanced technologies. Tha authors envision that LI-RADS will ultimately transform into a probability-based system for diagnosis and prognostication of liver cancers that will integrate patient characteristics and quantitative imaging features, while accounting for imaging modality and contrast agent.

Background Several early-phase clinical trials for the treatment of nonalcoholic steatohepatitis (NASH) use liver fat content as measured with the MRI-derived proton density fat fraction (PDFF) for a primary outcome. These trials have shown relative reductions in liver fat content with placebo treatment alone, a phenomenon termed the placebo effect. This phenomenon confounds the results and limits generalizability to future trials. Purpose To quantify the effect of placebo treatment on change in the absolute PDFF value and to identify variables associated with this observed change. Materials and Methods This is a secondary analysis of prospectively collected data from seven early phase clinical trials that included participants with a diagnosis of NASH based on MRI and/or liver biopsy who received placebo treatment. The primary outcome was a greater than or equal to 30% relative reduction in PDFF after placebo treatment. Normalization of PDFF, relative change in alanine aminotransferase (ALT) level, and normalization of ALT level were also examined. An exploratory linear mixed-effects model was used to estimate an overall change in absolute PDFF and to explore parameters associated with this response. Results A total of 187 participants (median age, 52 years [IQR, 43-60 years]; 114 women) who received placebo treatment were evaluated. A greater than or equal to 30% relative reduction in baseline PDFF was seen in 20% of participants after 12 weeks of placebo treatment (10 of 49), 9% of participants after 16 weeks (two of 22), and 28% of participants after 24 weeks (34 of 122). A repeated-measures linear mixed-effects model estimated a decrease of 2.3 units (median relative reduction of 13%) in absolute PDFF values after 24 weeks of placebo treatment (95% CI: 3.2, 1.4; P < .001). Conclusion In this analysis of 187 participants, a clinically relevant decrease in PDFF was observed with placebo treatment. Based on the study model, assuming an absolute PDFF decrease of approximately 3 units (upper limit of 95% CI) to account for this placebo effect in sample size calculations for future clinical trials is suggested. Clinical trial registration nos. NCT01066364, NCT01766713, NCT01963845, NCT02443116, NCT02546609, NCT02316717, and NCT02442687 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Yoon in this issue.

Background There is increasing interest in noncontrast breast MRI alternatives for tumor visualization to increase the accessibility of breast MRI. Purpose To evaluate the feasibility and accuracy of generating simulated contrast-enhanced T1-weighted breast MRI scans from precontrast MRI sequences in biopsy-proven invasive breast cancer with use of deep learning. Materials and Methods Women with invasive breast cancer and a contrast-enhanced breast MRI examination that was performed for initial evaluation of the extent of disease between January 2015 and December 2019 at a single academic institution were retrospectively identified. A three-dimensional, fully convolutional deep neural network simulated contrast-enhanced T1-weighted breast MRI scans from five precontrast sequences (T1-weighted non-fat-suppressed [FS], T1-weighted FS, T2-weighted FS, apparent diffusion coefficient, and diffusion-weighted imaging). For qualitative assessment, four breast radiologists (with 3-15 years of experience) blinded to whether the method of contrast was real or simulated assessed image quality (excellent, acceptable, good, poor, or unacceptable), presence of tumor enhancement, and maximum index mass size by using 22 pairs of real and simulated contrast-enhanced MRI scans. Quantitative comparison was performed using whole-breast similarity and error metrics and Dice coefficient analysis of enhancing tumor overlap. Results Ninety-six MRI examinations in 96 women (mean age, 52 years ± 12 [SD]) were evaluated. The readers assessed all simulated MRI scans as having the appearance of a real MRI scan with tumor enhancement. Index mass sizes on real and simulated MRI scans demonstrated good to excellent agreement (intraclass correlation coefficient, 0.73-0.86; P < .001) without significant differences (mean differences, -0.8 to 0.8 mm; P = .36-.80). Almost all simulated MRI scans (84 of 88 [95%]) were considered of diagnostic quality (ratings of excellent, acceptable, or good). Quantitative analysis demonstrated strong similarity (structural similarity index, 0.88 ± 0.05), low voxel-wise error (symmetric mean absolute percent error, 3.26%), and Dice coefficient of enhancing tumor overlap of 0.75 ± 0.25. Conclusion It is feasible to generate simulated contrast-enhanced breast MRI scans with use of deep learning. Simulated and real contrast-enhanced MRI scans demonstrated comparable tumor sizes, areas of tumor enhancement, and image quality without significant qualitative or quantitative differences. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Slanetz in this issue. An earlier incorrect version appeared online. This article was corrected on January 17, 2023.

Background Point-of-care (POC) MRI is a bedside imaging technology with fewer than five units in clinical use in the United States and a paucity of scientific studies on clinical applications. Purpose To evaluate the clinical and operational impacts of deploying POC MRI in emergency department (ED) and intensive care unit (ICU) patient settings for bedside neuroimaging, including the turnaround time. Materials and Methods In this preliminary retrospective study, all patients in the ED and ICU at a single academic medical center who underwent noncontrast brain MRI from January 2021 to June 2021 were investigated to determine the number of patients who underwent bedside POC MRI. Turnaround time, examination limitations, relevant findings, and potential CT and fixed MRI findings were recorded for patients who underwent POC MRI. Descriptive statistics were used to describe clinical variables. The Mann-Whitney U test was used to compare the turnaround time between POC MRI and fixed MRI examinations. Results Of 638 noncontrast brain MRI examinations, 36 POC MRI examinations were performed in 35 patients (median age, 66 years [IQR, 57-77 years]; 21 women), with one patient undergoing two POC MRI examinations. Of the 36 POC MRI examinations, 13 (36%) occurred in the ED and 23 (64%) in the ICU. There were 12 of 36 (33%) POC MRI examinations interpreted as negative, 14 of 36 (39%) with clinically significant imaging findings, and 10 of 36 (28%) deemed nondiagnostic for reasons such as patient motion. Of 23 diagnostic POC MRI examinations with comparison CT available, three (13%) demonstrated acute infarctions not apparent on CT scans. Of seven diagnostic POC MRI examinations with subsequent fixed MRI examinations, two (29%) demonstrated missed versus interval subcentimeter infarctions, while the remaining demonstrated no change. The median turnaround time of POC MRI was 3.4 hours in the ED and 5.3 hours in the ICU. Conclusion Point-of-care (POC) MRI was performed rapidly in the emergency department and intensive care unit. A few POC MRI examinations demonstrated acute infarctions not apparent at standard-of-care CT examinations. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Anzai and Moy in this issue.

Background MRI-derived proton density fat fraction (PDFF) is an accurate, reliable, and safe biologic marker for use in the noninvasive diagnosis of hepatic steatosis in patients with nonalcoholic fatty liver disease (NAFLD). Because of the cost and limited availability of MRI, it is necessary to develop an accurate method to diagnose NAFLD with potential point-of-care access. Purpose To compare the diagnostic accuracy of the quantitative US (QUS) fat fraction (FF) estimator with that of the controlled attenuation parameter (CAP) in the diagnosis of NAFLD using contemporaneous MRI-derived PDFF as the reference standard. Materials and Methods Participants with or suspected of having NAFLD were prospectively recruited at the NAFLD Research Center between July 2015 and July 2019. All participants underwent MRI-derived PDFF measurement, transient elastography with CAP measurement, and QUS. QUS FF was derived using computed QUS parameters from the acquired radiofrequency US data using a calibrated reference phantom. The area under the receiver operating characteristic curve (AUC) was calculated to assess the accuracy of QUS FF and CAP in the diagnosis of hepatic steatosis (defined as MRI-derived PDFF ≥ 5%). AUCs were compared using the DeLong test. Results A total of 123 participants were included (mean age, 52 years ± 13 [SD]; 67 [54%] women). Of these participants, 100 (81%) had MRI-derived PDFF of 5% or more. QUS FF had a significantly higher AUC for diagnosis of NAFLD than did CAP (0.92 [95% CI: 0.87, 0.98] vs 0.79 [95% CI: 0.67, 0.90], P = .03). QUS FF had a sensitivity of 98% (98 of 100) and a specificity of 48% (11 of 23). CAP had a sensitivity of 87% (87 of 100) and a specificity of 57% (13 of 23). Conclusion The quantitative US fat fraction estimator is more accurate than the controlled attenuation parameter in the diagnosis of hepatic steatosis in patients with or suspected of having nonalcoholic fatty liver disease. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Ito in this issue.

Background Airway mucus plugs in asthma are associated with exacerbation frequency, increased eosinophilia, and reduced lung function. The relationship between mucus plugs and spatially overlapping ventilation abnormalities observed at hyperpolarized gas MRI has not been assessed quantitatively. Purpose To assess regional associations between CT mucus plugs scored by individual bronchopulmonary segment and corresponding measurements of segmental ventilation defect percentage (VDP) at hyperpolarized helium 3 (3He) MRI. Materials and Methods In this secondary analysis of a Health Insurance Portability and Accountability Act-compliant prospective observational cohort, participants in the Severe Asthma Research Program (SARP) III (NCT01760915) between December 2012 and August 2015 underwent hyperpolarized 3He MRI to determine segmental VDP. Segmental mucus plugs at CT were scored by two readers, with segments scored as plugged only if both readers agreed independently. A linear mixed-effects model controlling for interpatient variability was then used to assess differences in VDP in plugged versus plug-free segments. Results Forty-four participants with asthma were assessed (mean age ± standard deviation, 47 years ± 15; 29 women): 19 with mild-to-moderate asthma and 25 with severe asthma. Mucus plugs were observed in 49 total bronchopulmonary segments across eight of 44 patients. Segments containing mucus plugs had a median segmental VDP of 25.9% (25th-75th percentile, 7.3%-38.3%) versus 1.4% (25th-75th percentile, 0.1%-5.2%; P < .001) in plug-free segments. Similarly, the model estimated a segmental VDP of 18.9% (95% CI: 15.7, 22.2) for mucus-plugged segments versus 5.1% (95% CI: 3.3, 7.0) for plug-free segments (P < .001). Participants with one or more mucus plugs had a median whole-lung VDP of 11.1% (25th-75th percentile, 7.1%-18.9%) versus 3.1% (25th-75th percentile, 1.1%-4.4%) in those without plugs (P < .001). Conclusion Airway mucus plugging at CT was associated with reduced ventilation in the same bronchopulmonary segment at hyperpolarized helium 3 MRI, suggesting that mucus plugging may be an important cause of ventilation defects in asthma. © RSNA, 2021 Online supplemental material is available for this article.

Background Four-dimensional (4D) flow MRI has the potential to provide hemodynamic insights for a variety of abdominopelvic vascular diseases, but its clinical utility is currently impaired by background phase error, which can be challenging to correct. Purpose To assess the feasibility of using deep learning to automatically perform image-based background phase error correction in 4D flow MRI and to compare its effectiveness relative to manual image-based correction. Materials and Methods A convenience sample of 139 abdominopelvic 4D flow MRI acquisitions performed between January 2016 and July 2020 was retrospectively collected. Manual phase error correction was performed using dedicated imaging software and served as the reference standard. After reserving 40 examinations for testing, the remaining examinations were randomly divided into training (86% [85 of 99]) and validation (14% [14 of 99]) data sets to train a multichannel three-dimensional U-Net convolutional neural network. Flow measurements were obtained for the infrarenal aorta, common iliac arteries, common iliac veins, and inferior vena cava. Statistical analyses included Pearson correlation, Bland-Altman analysis, and F tests with Bonferroni correction. Results A total of 139 patients (mean age, 47 years ± 14 [standard deviation]; 108 women) were included. Inflow-outflow correlation improved after manual correction (ρ = 0.94, P < .001) compared with that before correction (ρ = 0.50, P < .001). Automated correction showed similar results (ρ = 0.91, P < .001) and demonstrated very strong correlation with manual correction (ρ = 0.98, P < .001). Both correction methods reduced inflow-outflow variance, improving mean difference from -0.14 L/min (95% limits of agreement: -1.61, 1.32) (uncorrected) to 0.05 L/min (95% limits of agreement: -0.32, 0.42) (manually corrected) and 0.05 L/min (95% limits of agreement: -0.38, 0.49) (automatically corrected). There was no significant difference in inflow-outflow variance between manual and automated correction methods (P = .10). Conclusion Deep learning automated phase error correction reduced inflow-outflow bias and variance of volumetric flow measurements in four-dimensional flow MRI, achieving results comparable with manual image-based phase error correction. © RSNA, 2021 See also the editorial by Roldán-Alzate and Grist in this issue.

MRI-based cartilage compositional analysis shows biochemical and microstructural changes at early stages of osteoarthritis before changes become visible with structural MRI sequences and arthroscopy. This could help with early diagnosis, risk assessment, and treatment monitoring of osteoarthritis. Spin-lattice relaxation time constant in rotating frame (T1ρ) and T2 mapping are the MRI techniques best established for assessing cartilage composition. Only T2 mapping is currently commercially available, which is sensitive to water, collagen content, and orientation of collagen fibers, whereas T1ρ is more sensitive to proteoglycan content. Clinical application of cartilage compositional imaging is limited by high variability and suboptimal reproducibility of the biomarkers, which was the motivation for creating the Quantitative Imaging Biomarkers Alliance (QIBA) Profile for cartilage compositional imaging by the Musculoskeletal Biomarkers Committee of the QIBA. The profile aims at providing recommendations to improve reproducibility and to standardize cartilage compositional imaging. The QIBA Profile provides two complementary claims (summary statements of the technical performance of the quantitative imaging biomarkers that are being profiled) regarding the reproducibility of biomarkers. First, cartilage T1ρ and T2 values are measurable at 3.0-T MRI with a within-subject coefficient of variation of 4%-5%. Second, a measured increase or decrease in T1ρ and T2 of 14% or more indicates a minimum detectable change with 95% confidence. If only an increase in T1ρ and T2 values is expected (progressive cartilage degeneration), then an increase of 12% represents a minimum detectable change over time. The QIBA Profile provides recommendations for clinical researchers, clinicians, and industry scientists pertaining to image data acquisition, analysis, and interpretation and assessment procedures for T1ρ and T2 cartilage imaging and test-retest conformance. This special report aims to provide the rationale for the proposed claims, explain the content of the QIBA Profile, and highlight the future needs and developments for MRI-based cartilage compositional imaging for risk prediction, early diagnosis, and treatment monitoring of osteoarthritis.

Background Suppression of background parenchymal enhancement (BPE) is commonly observed after neoadjuvant chemotherapy (NAC) at contrast-enhanced breast MRI. It was hypothesized that nonsuppressed BPE may be associated with inferior response to NAC. Purpose To investigate the relationship between lack of BPE suppression and pathologic response. Materials and Methods A retrospective review was performed for women with menopausal status data who were treated for breast cancer by one of 10 drug arms (standard NAC with or without experimental agents) between May 2010 and November 2016 in the Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis 2, or I-SPY 2 TRIAL (NCT01042379). Patients underwent MRI at four points: before treatment (T0), early treatment (T1), interregimen (T2), and before surgery (T3). BPE was quantitatively measured by using automated fibroglandular tissue segmentation. To test the hypothesis effectively, a subset of examinations with BPE with high-quality segmentation was selected. BPE change from T0 was defined as suppressed or nonsuppressed for each point. The Fisher exact test and the Z tests of proportions with Yates continuity correction were used to examine the relationship between BPE suppression and pathologic complete response (pCR) in hormone receptor (HR)-positive and HR-negative cohorts. Results A total of 3528 MRI scans from 882 patients (mean age, 48 years ± 10 [standard deviation]) were reviewed and the subset of patients with high-quality BPE segmentation was determined (T1, 433 patients; T2, 396 patients; T3, 380 patients). In the HR-positive cohort, an association between lack of BPE suppression and lower pCR rate was detected at T2 (nonsuppressed vs suppressed, 11.8% [six of 51] vs 28.9% [50 of 173]; difference, 17.1% [95% CI: 4.7, 29.5]; P = .02) and T3 (nonsuppressed vs suppressed, 5.3% [two of 38] vs 27.4% [48 of 175]; difference, 22.2% [95% CI: 10.9, 33.5]; P = .003). In the HR-negative cohort, patients with nonsuppressed BPE had lower estimated pCR rate at all points, but the P values for the association were all greater than .05. Conclusions In hormone receptor-positive breast cancer, lack of background parenchymal enhancement suppression may indicate inferior treatment response. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Philpotts in this issue.

Background The value of MRI in pediatric congenital heart disease (CHD) is well recognized; however, the requirement for expert oversight impedes its widespread use. Four-dimensional (4D) multiphase steady-state imaging with contrast enhancement (MUSIC) is a cardiovascular MRI technique that uses ferumoxytol and captures all anatomic features dynamically. Purpose To evaluate multicenter feasibility of 4D MUSIC MRI in pediatric CHD. Materials and Methods In this prospective study, participants with CHD underwent 4D MUSIC MRI at 3.0 T or 1.5 T between 2014 and 2020. From a pool of 460 total studies, an equal number of MRI studies from three sites (n = 60) was chosen for detailed analysis. With use of a five-point scale, the feasibility of 4D MUSIC was scored on the basis of artifacts, image quality, and diagnostic confidence for intracardiac and vascular connections (n = 780). Respiratory motion suppression was assessed by using the signal intensity profile. Bias between 4D MUSIC and two-dimensional (2D) cine imaging was evaluated by using Bland-Altman analysis; 4D MUSIC examination duration was compared with that of the local standard for CHD. Results A total of 206 participants with CHD underwent MRI at 3.0 T, and 254 participants underwent MRI at 1.5 T. Of the 60 MRI examinations chosen for analysis (20 per site; median participant age, 14.4 months [interquartile range, 2.3-49 months]; 33 female participants), 56 (93%) had good or excellent image quality scores across a spectrum of disease complexity (mean score ± standard deviation: 4.3 ± 0.6 for site 1, 4.9 ± 0.3 for site 2, and 4.6 ± 0.7 for site 3; P < .001). Artifact scores were inversely related to image quality (r = -0.88, P < .001) and respiratory motion suppression (P < .001, r = -0.45). Diagnostic confidence was high or definite in 730 of 780 (94%) intracardiac and vascular connections. The correlation between 4D MUSIC and 2D cine ventricular volumes and ejection fraction was high (range of r = 0.72-0.85; P < .001 for all). Compared with local standard MRI, 4D MUSIC reduced the image acquisition time (44 minutes ± 20 vs 12 minutes ± 3, respectively; P < .001). Conclusion Four-dimensional multiphase steady-state imaging with contrast enhancement MRI in pediatric congenital heart disease was feasible in a multicenter setting, shortened the examination time, and simplified the acquisition protocol, independently of disease complexity. Clinical trial registration no. NCT02752191 © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Roest and Lamb in this issue.

Background The third trimester of gestation is a crucial phase of rapid brain development, but little has been reported on the trajectories of cerebral blood flow (CBF) in preterm infants in this period. Purpose To quantify regional CBF in very preterm infants longitudinally across the ex utero third trimester and to determine its relationship with clinical factors associated with brain injury and premature birth. Materials and Methods In this prospective study, very preterm infants were enrolled for three longitudinal MRI scans, and 22 healthy full-term infants were enrolled for one term MRI scan between November 2016 and February 2019. Global and regional CBF in the cortical gray matter, white matter, deep gray matter, and cerebellum were measured using arterial spin labeling with postlabeling delay of 2025 msec at 1.5 T and 3.0 T. Brain injury and clinical risk factors in preterm infants were investigated to determine associations with CBF. Generalized estimating equations were used to account for correlations between repeated measures in the same individual. Results A total of 75 preterm infants (mean postmenstrual age [PMA]: 29.5 weeks ± 2.3 [standard deviation], 34.9 weeks ± 0.8, and 39.3 weeks ± 2.0 for each scan; 43 male infants) and 22 full-term infants (mean PMA, 42.1 weeks ± 2.0; 13 male infants) were evaluated. In preterm infants, global CBF was 11.9 mL/100 g/min ± 0.2 (standard error). All regional CBF increased significantly with advancing PMA (P ≤ .02); the cerebellum demonstrated the most rapid CBF increase and the highest mean CBF. Lower CBF was associated with intraventricular hemorrhage in all regions (P ≤ .05) and with medically managed patent ductus arteriosus in the white matter and deep gray matter (P = .03). Mean CBF of preterm infants at term-equivalent age was significantly higher compared with full-term infants (P ≤ .02). Conclusion Regional cerebral blood flow increased significantly in preterm infants developing in an extrauterine environment across the third trimester and was associated with intraventricular hemorrhage and patent ductus arteriosus. © RSNA, 2021 Online supplemental material is available for this article.

Background For patients with acute ischemic stroke undergoing endovascular mechanical thrombectomy with x-ray angiography, the use of adjuncts to maintain vessel patency, such as stents or antiplatelet medications, can increase risk of periprocedural complications. Criteria for using these adjuncts are not well defined. Purpose To evaluate use of MRI to guide critical decision making by using a combined biplane x-ray neuroangiography 3.0-T MRI suite during acute ischemic stroke intervention. Materials and Methods This retrospective observational study evaluated consecutive patients undergoing endovascular intervention for acute ischemic stroke between July 2019 and May 2020 who underwent either angiography with MRI or angiography alone. Cerebral tissue viability was assessed by using MRI as the reference standard. For statistical analysis, Fisher exact test and Student t test were used to compare groups. Results Of 47 patients undergoing acute stroke intervention, 12 patients (median age, 69 years; interquartile range, 60-77 years; nine men) underwent x-ray angiography with MRI whereas the remaining 35 patients (median age, 80 years; interquartile range, 68-86 years; 22 men) underwent angiography alone. MRI results influenced clinical decision making in one of three ways: whether or not to perform initial or additional mechanical thrombectomy, whether or not to place an intracranial stent, and administration of antithrombotic or blood pressure medications. In this initial experience, decision making during endovascular acute stroke intervention in the combined angiography-MRI suite was better informed at MRI, such that therapy was guided in real time by the viability of the at-risk cerebral tissue. Conclusion Integrating intraprocedural 3.0-T MRI into acute ischemic stroke treatment was feasible and guided decisions of whether or not to continue thrombectomy, to place stents, or to administer antithrombotic medication or provide blood pressure medications. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Lev and Leslie-Mazwi in this issue.

Background Proton density fat fraction (PDFF) estimated by using chemical shift-encoded (CSE) MRI is an accepted imaging biomarker of hepatic steatosis. This work aims to promote standardized use of CSE MRI to estimate PDFF. Purpose To assess the accuracy of CSE MRI methods for estimating PDFF by determining the linearity and range of bias observed in a phantom. Materials and Methods In this prospective study, a commercial phantom with 12 vials of known PDFF values were shipped across nine U.S. centers. The phantom underwent 160 independent MRI examinations on 27 1.5-T and 3.0-T systems from three vendors. Two three-dimensional CSE MRI protocols with minimal T1 bias were included: vendor and standardized. Each vendor's confounder-corrected complex or hybrid magnitude-complex based reconstruction algorithm was used to generate PDFF maps in both protocols. The Siemens reconstruction required a configuration change to correct for water-fat swaps in the phantom. The MRI PDFF values were compared with the known PDFF values by using linear regression with mixed-effects modeling. The 95% CIs were calculated for the regression slope (ie, proportional bias) and intercept (ie, constant bias) and compared with the null hypothesis (slope = 1, intercept = 0). Results Pooled regression slope for estimated PDFF values versus phantom-derived reference PDFF values was 0.97 (95% CI: 0.96, 0.98) in the biologically relevant 0%-47.5% PDFF range. The corresponding pooled intercept was -0.27% (95% CI: -0.50%, -0.05%). Across vendors, slope ranges were 0.86-1.02 (vendor protocols) and 0.97-1.0 (standardized protocol) at 1.5 T and 0.91-1.01 (vendor protocols) and 0.87-1.01 (standardized protocol) at 3.0 T. The intercept ranges (absolute PDFF percentage) were -0.65% to 0.18% (vendor protocols) and -0.69% to -0.17% (standardized protocol) at 1.5 T and -0.48% to 0.10% (vendor protocols) and -0.78% to -0.21% (standardized protocol) at 3.0 T. Conclusion Proton density fat fraction estimation derived from three-dimensional chemical shift-encoded MRI in a commercial phantom was accurate across vendors, imaging centers, and field strengths, with use of the vendors' product acquisition and reconstruction software. © RSNA, 2021 See also the editorial by Dyke in this issue.

Background Cerebrovascular reserve (CVR) may be measured by using an acetazolamide test to clinically evaluate patients with cerebrovascular disease. However, acetazolamide use may be contraindicated and/or undesirable in certain clinical settings. Purpose To predict CVR images generated from acetazolamide vasodilation with a deep learning network by using only images before acetazolamide administration. Materials and Methods Simultaneous oxygen 15 (15O)-labeled water PET/MRI before and after acetazolamide injection were retrospectively analyzed for patients with Moyamoya disease and healthy control participants from April 2017 to May 2019. Inputs to deep learning models were perfusion-based images (arterial spin labeling [ASL]), structural scans (T2 fluid-attenuated inversion-recovery, T1), and brain location. Two models, that is, 15O-labeled water PET cerebral blood flow (CBF) and MRI (PET-plus-MRI model) before acetazolamide administration and only MRI (MRI-only model) before acetazolamide administration, were trained and tested with sixfold cross-validation. The models learned to predict a voxelwise relative CBF change (rΔCBF) map by using rΔCBF measured with PET due to acetazolamide as ground truth. Quantitative analysis included image quality metrics (peak signal-to-noise ratio, root mean square error, and structural similarity index), as well as comparison between the various methods by using correlation and Bland-Altman analyses. Identification of vascular territories with impaired rΔCBF was evaluated by using receiver operating characteristic metrics. Results Thirty-six participants were included: 24 patients with Moyamoya disease (mean age ± standard deviation, 41 years ± 12; 17 women) and 12 age-matched healthy control participants (mean age, 39 years ± 16; nine women). The rΔCBF maps predicted by both deep learning models demonstrated better image quality metrics than did ASL (all P < .001 in patients) and higher correlation coefficient with PET than with ASL (PET-plus-MRI model, 0.704; MRI-only model, 0.690 vs ASL, 0.432; both P < .001 in patients). Both models also achieved high diagnostic performance in identifying territories with impaired rΔCBF (area under receiver operating characteristic curve, 0.95 for PET-plus-MRI model [95% confidence interval: 0.90, 0.99] and 0.95 for MRI-only model [95% confidence interval: 0.91, 0.98]). Conclusion By using only images before acetazolamide administration, PET-plus-MRI and MRI-only deep learning models predicted cerebrovascular reserve images without the need for vasodilator injection. © RSNA, 2020 Online supplemental material is available for this article.

Background Prostate MRI is used widely in clinical care for guiding tissue sampling, active surveillance, and staging. The Prostate Imaging Reporting and Data System (PI-RADS) helps provide a standardized probabilistic approach for identifying clinically significant prostate cancer. Despite widespread use, the variability in performance of prostate MRI across practices remains unknown. Purpose To estimate the positive predictive value (PPV) of PI-RADS for the detection of high-grade prostate cancer across imaging centers. Materials and Methods This retrospective cross-sectional study was compliant with the HIPAA. Twenty-six centers with members in the Society of Abdominal Radiology Prostate Cancer Disease-focused Panel submitted data from men with suspected or biopsy-proven untreated prostate cancer. MRI scans were obtained between January 2015 and April 2018. This was followed with targeted biopsy. Only men with at least one MRI lesion assigned a PI-RADS score of 2-5 were included. Outcome was prostate cancer with Gleason score (GS) greater than or equal to 3+4 (International Society of Urological Pathology grade group ≥2). A mixed-model logistic regression with institution and individuals as random effects was used to estimate overall PPVs. The variability of observed PPV of PI-RADS across imaging centers was described by using the median and interquartile range. Results The authors evaluated 3449 men (mean age, 65 years ± 8 [standard deviation]) with 5082 lesions. Biopsy results showed 1698 cancers with GS greater than or equal to 3+4 (International Society of Urological Pathology grade group ≥2) in 2082 men. Across all centers, the estimated PPV was 35% (95% confidence interval [CI]: 27%, 43%) for a PI-RADS score greater than or equal to 3 and 49% (95% CI: 40%, 58%) for a PI-RADS score greater than or equal to 4. The interquartile ranges of PPV at these same PI-RADS score thresholds were 27%-44% and 27%-48%, respectively. Conclusion The positive predictive value of the Prostate Imaging and Reporting Data System was low and varied widely across centers. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Milot in this issue.

Background Cardiac MRI is limited by long acquisition times, yet faster acquisition of smaller-matrix images reduces spatial detail. Deep learning (DL) might enable both faster acquisition and higher spatial detail via super-resolution. Purpose To explore the feasibility of using DL to enhance spatial detail from small-matrix MRI acquisitions and evaluate its performance against that of conventional image upscaling methods. Materials and Methods Short-axis cine cardiac MRI examinations performed between January 2012 and December 2018 at one institution were retrospectively collected for algorithm development and testing. Convolutional neural networks (CNNs), a form of DL, were trained to perform super resolution in image space by using synthetically generated low-resolution data. There were 70%, 20%, and 10% of examinations allocated to training, validation, and test sets, respectively. CNNs were compared against bicubic interpolation and Fourier-based zero padding by calculating the structural similarity index (SSIM) between high-resolution ground truth and each upscaling method. Means and standard deviations of the SSIM were reported, and statistical significance was determined by using the Wilcoxon signed-rank test. For evaluation of clinical performance, left ventricular volumes were measured, and statistical significance was determined by using the paired Student t test. Results For CNN training and retrospective analysis, 400 MRI scans from 367 patients (mean age, 48 years ± 18; 214 men) were included. All CNNs outperformed zero padding and bicubic interpolation at upsampling factors from two to 64 (P < .001). CNNs outperformed zero padding on more than 99.2% of slices (9828 of 9907). In addition, 10 patients (mean age, 51 years ± 22; seven men) were prospectively recruited for super-resolution MRI. Super-resolved low-resolution images yielded left ventricular volumes comparable to those from full-resolution images (P > .05), and super-resolved full-resolution images appeared to further enhance anatomic detail. Conclusion Deep learning outperformed conventional upscaling methods and recovered high-frequency spatial information. Although training was performed only on short-axis cardiac MRI examinations, the proposed strategy appeared to improve quality in other imaging planes. © RSNA, 2020 Online supplemental material is available for this article.

Background Although artificial intelligence (AI) shows promise across many aspects of radiology, the use of AI to create differential diagnoses for rare and common diseases at brain MRI has not been demonstrated. Purpose To evaluate an AI system for generation of differential diagnoses at brain MRI compared with radiologists. Materials and Methods This retrospective study tested performance of an AI system for probabilistic diagnosis in patients with 19 common and rare diagnoses at brain MRI acquired between January 2008 and January 2018. The AI system combines data-driven and domain-expertise methodologies, including deep learning and Bayesian networks. First, lesions were detected by using deep learning. Then, 18 quantitative imaging features were extracted by using atlas-based coregistration and segmentation. Third, these image features were combined with five clinical features by using Bayesian inference to develop probability-ranked differential diagnoses. Quantitative feature extraction algorithms and conditional probabilities were fine-tuned on a training set of 86 patients (mean age, 49 years ± 16 [standard deviation]; 53 women). Accuracy was compared with radiology residents, general radiologists, neuroradiology fellows, and academic neuroradiologists by using accuracy of top one, top two, and top three differential diagnoses in 92 independent test set patients (mean age, 47 years ± 18; 52 women). Results For accuracy of top three differential diagnoses, the AI system (91% correct) performed similarly to academic neuroradiologists (86% correct; P = .20), and better than radiology residents (56%; P < .001), general radiologists (57%; P < .001), and neuroradiology fellows (77%; P = .003). The performance of the AI system was not affected by disease prevalence (93% accuracy for common vs 85% for rare diseases; P = .26). Radiologists were more accurate at diagnosing common versus rare diagnoses (78% vs 47% across all radiologists; P < .001). Conclusion An artificial intelligence system for brain MRI approached overall top one, top two, and top three differential diagnoses accuracy of neuroradiologists and exceeded that of less-specialized radiologists. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Zaharchuk in this issue.

Background Radiofrequency ultrasound data from the liver contain rich information about liver microstructure and composition. Deep learning might exploit such information to assess nonalcoholic fatty liver disease (NAFLD). Purpose To develop and evaluate deep learning algorithms that use radiofrequency data for NAFLD assessment, with MRI-derived proton density fat fraction (PDFF) as the reference. Materials and Methods A HIPAA-compliant secondary analysis of a single-center prospective study was performed for adult participants with NAFLD and control participants without liver disease. Participants in the parent study were recruited between February 2012 and March 2014 and underwent same-day US and MRI of the liver. Participants were randomly divided into an equal number of training and test groups. The training group was used to develop two algorithms via cross-validation: a classifier to diagnose NAFLD (MRI PDFF ≥ 5%) and a fat fraction estimator to predict MRI PDFF. Both algorithms used one-dimensional convolutional neural networks. The test group was used to evaluate the classifier for sensitivity, specificity, positive predictive value, negative predictive value, and accuracy and to evaluate the estimator for correlation, bias, limits of agreements, and linearity between predicted fat fraction and MRI PDFF. Results A total of 204 participants were analyzed, 140 had NAFLD (mean age, 52 years ± 14 [standard deviation]; 82 women) and 64 were control participants (mean age, 46 years ± 21; 42 women). In the test group, the classifier provided 96% (95% confidence interval [CI]: 90%, 99%) (98 of 102) accuracy for NAFLD diagnosis (sensitivity, 97% [95% CI: 90%, 100%], 68 of 70; specificity, 94% [95% CI: 79%, 99%], 30 of 32; positive predictive value, 97% [95% CI: 90%, 99%], 68 of 70; negative predictive value, 94% [95% CI: 79%, 98%], 30 of 32). The estimator-predicted fat fraction correlated with MRI PDFF (Pearson r = 0.85). The mean bias was 0.8% (P = .08), and 95% limits of agreement were -7.6% to 9.1%. The predicted fat fraction was linear with an MRI PDFF of 18% or less (r = 0.89, slope = 1.1, intercept = 1.3) and nonlinear with an MRI PDFF greater than 18%. Conclusion Deep learning algorithms using radiofrequency ultrasound data are accurate for diagnosis of nonalcoholic fatty liver disease and hepatic fat fraction quantification when other causes of steatosis are excluded. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Lockhart and Smith in this issue.

Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.

Background Advanced confounder-corrected chemical shift-encoded MRI-derived proton density fat fraction (PDFF) is a leading parameter for fat fraction quantification in nonalcoholic fatty liver disease (NAFLD). Because of the limited availability of this MRI technique, there is a need to develop and validate alternative parameters to assess liver fat. Purpose To assess relationship of quantitative US parameters to MRI PDFF and to develop multivariable quantitative US models to detect hepatic steatosis and quantify hepatic fat. Materials and Methods Adults with known NAFLD or who were suspected of having NAFLD were prospectively recruited between August 2015 and February 2019. Participants underwent quantitative US and chemical shift-encoded MRI liver examinations. Liver biopsies were performed if clinically indicated. The correlation between seven quantitative US parameters and MRI PDFF was evaluated. By using leave-one-out cross validation, two quantitative US multivariable models were evaluated: a classifier to differentiate participants with NAFLD versus participants without NAFLD and a fat fraction estimator. Classifier performance was summarized by area under the receiver operating characteristic curve and area under the precision-recall curve. Fat fraction estimator performance was evaluated by correlation, linearity, and bias. Results Included were 102 participants (mean age, 52 years ± 13 [standard deviation]; 53 women), 78 with NAFLD (MRI PDFF ≥ 5%). A two-variable classifier yielded a cross-validated area under the receiver operating characteristic curve of 0.89 (95% confidence interval: 0.82, 0.96) and an area under the precision-recall curve of 0.96 (95% confidence interval: 0.93, 0.99). The cross-validated fat fraction predicted by a two-variable fat fraction estimator was correlated with MRI PDFF (Spearman ρ = 0.82 [P < .001]; Pearson r = 0.76 [P < .001]). The mean bias was 0.02% (P = .97), and 95% limits of agreement were ±12.0%. The predicted fat fraction was linear with MRI PDFF (R 2 = 0.63; slope, 0.69; intercept, 4.3%) for MRI PDFF of 34% or less. Conclusion A multivariable quantitative US approach yielded excellent correlation with MRI proton density fat fraction for hepatic steatosis assessment in nonalcoholic fatty liver disease. © RSNA, 2020 Online supplemental material is available for this article.

Background Despite advances, blood oxygen level-dependent (BOLD) cardiac MRI for myocardial perfusion is limited by inadequate spatial coverage, imaging speed, multiple breath holds, and imaging artifacts, particularly at 3.0 T. Purpose To develop and validate a robust, contrast agent-unenhanced, free-breathing three-dimensional (3D) cardiac MRI approach for reliably examining changes in myocardial perfusion between rest and adenosine stress. Materials and Methods A heart rate-independent, free-breathing 3D T2 mapping technique at 3.0 T that can be completed within the period of adenosine stress (≤4 minutes) was developed by using computer simulations, ex vivo heart preparations, and dogs. Studies in dogs were performed with and without coronary stenosis and validated with simultaneously acquired nitrogen 13 (13N) ammonia PET perfusion in a clinical PET/MRI system. The MRI approach was also prospectively evaluated in healthy human volunteers (from January 2017 to September 2017). Myocardial BOLD responses (MBRs) between normal and ischemic myocardium were compared with mixed model analysis. Results Dogs (n = 10; weight range, 20-25 kg; mongrel dogs) and healthy human volunteers (n = 10; age range, 22-53 years; seven men) were evaluated. In healthy dogs, T2 MRI at adenosine stress was greater than at rest (mean rest vs stress, 38.7 msec ± 2.5 [standard deviation] vs 45.4 msec ± 3.3, respectively; MBR, 1.19 ± 0.08; both, P < .001). At the same conditions, mean rest versus stress PET perfusion was 1.1 mL/mg/min ± 0.11 versus 2.3 mL/mg/min ± 0.82, respectively (P < .001); myocardial perfusion reserve (MPR) was 2.4 ± 0.82 (P < .001). The BOLD response and PET MPR were positively correlated (R = 0.67; P < .001). In dogs with coronary stenosis, perfusion anomalies were detected on the basis of MBR (normal vs ischemic, 1.09 ± 0.05 vs 1.00 ± 0.04, respectively; P < .001) and MPR (normal vs ischemic, 2.7 ± 0.08 vs 1.7 ± 1.1, respectively; P < .001). Human volunteers showed increased myocardial T2 at stress (rest vs stress, 44.5 msec ± 2.6 vs 49.0 msec ± 5.5, respectively; P = .004; MBR, 1.1 msec ± 8.08). Conclusion This three-dimensional cardiac blood oxygen level-dependent (BOLD) MRI approach overcame key limitations associated with conventional cardiac BOLD MRI by enabling whole-heart coverage within the standard duration of adenosine infusion, and increased the magnitude and reliability of BOLD contrast, which may be performed without requiring breath holds. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Almeida in this issue.

Background Impaired brain development in fetuses with congenital heart disease (CHD) may result from inadequate cerebral oxygen supply in utero. Purpose To test whether fetal cerebral oxygenation can be increased by maternal oxygen administration, effects of maternal hyperoxia on blood oxygenation of the placenta and fetal brain were examined by using blood oxygenation level-dependent (BOLD) functional MRI. Materials and Methods In this prospective study, BOLD MRI was performed in 86 fetuses (56 healthy fetuses and 30 fetuses diagnosed with CHD) between 22 and 39 weeks gestational age (GA) from May 2015 to December 2017, with the following study design: phase I, 2-minute resting state at baseline (room air); phase II, 6-minute maternal hyperoxia with 100% oxygen; and phase III, 5.6-minute return to resting state. After motion correction, the signals were averaged over the placenta and fetal brain and converted to the change in R2* (ΔR2*). Fetuses with CHD were categorized into those with a single ventricle (SV) or two ventricles (TVs) and those with aortic obstruction (AO) or non-AO. Data were analyzed by using generalized linear mixed models controlling for GA and sex. Results Placental ΔR2* increased during maternal hyperoxia in healthy fetuses and fetuses with CHD, but it was higher in SV CHD (mean ΔR2*, 1.3 sec-1 ± 0.1 [standard error; P < .01], 1.9 sec-1 ± 0.2 [P < .01], and 1.0 sec-1 ± 0.3 [P < .01], respectively, for control fetuses, fetuses with SV CHD, and fetuses with TV CHD). Placental ΔR2* during maternal hyperoxia changed with GA in healthy control fetuses and fetuses with SV or AO CHD (ΔR2* per week, 0.1 sec-1 ± 0 [P < .01], 0.2 sec-1 ± 0 [P = .01], and 0.2 sec-1 ± 0 [P = .01], respectively), but not in fetuses with CHD and TV or non-AO. Fetal brain ΔR2* was constant across all phases in healthy control fetuses and fetuses with TV CHD but increased during maternal hyperoxia in fetuses with SV or AO CHD (mean ΔR2*, 0.7 sec-1 ± 0.2 [P = .01] and 0.5 sec-1 ± 0.2 [P = .02], respectively). Conclusion Six minutes of maternal hyperoxia increased placental oxygenation in healthy fetuses and fetuses with congenital heart disease, and it selectively increased cerebral blood oxygenation in fetuses with single ventricle or aortic obstruction. © RSNA, 2019 Online supplemental material is available for this article.

Background Ferumoxytol is approved for use in the treatment of iron deficiency anemia, but it can serve as an alternative to gadolinium-based contrast agents. On the basis of postmarketing surveillance data, the Food and Drug Administration issued a black box warning regarding the risks of rare but serious acute hypersensitivity reactions during fast high-dose injection (510 mg iron in 17 seconds) for therapeutic use. Whereas single-center safety data for diagnostic use have been positive, multicenter data are lacking. Purpose To report multicenter safety data for off-label diagnostic ferumoxytol use. Materials and Methods The multicenter ferumoxytol MRI registry was established as an open-label nonrandomized surveillance databank without industry involvement. Each center monitored all ferumoxytol administrations, classified adverse events (AEs) using the National Cancer Institute Common Terminology Criteria for Adverse Events (grade 1-5), and assessed the relationship of AEs to ferumoxytol administration. AEs related to or possibly related to ferumoxytol injection were considered adverse reactions. The core laboratory adjudicated the AEs and classified them with the American College of Radiology (ACR) classification. Analysis of variance was used to compare vital signs. Results Between January 2003 and October 2018, 3215 patients (median age, 58 years; range, 1 day to 96 years; 1897 male patients) received 4240 ferumoxytol injections for MRI. Ferumoxytol dose ranged from 1 to 11 mg per kilogram of body weight (≤510 mg iron; rate ≤45 mg iron/sec). There were no systematic changes in vital signs after ferumoxytol administration (P > .05). No severe, life-threatening, or fatal AEs occurred. Eighty-three (1.9%) of 4240 AEs were related or possibly related to ferumoxytol infusions (75 mild [1.8%], eight moderate [0.2%]). Thirty-one AEs were classified as allergiclike reactions using ACR criteria but were consistent with minor infusion reactions observed with parenteral iron. Conclusion Diagnostic ferumoxytol use was well tolerated, associated with no serious adverse events, and implicated in few adverse reactions. Registry results indicate a positive safety profile for ferumoxytol use in MRI. © RSNA, 2019 Online supplemental material is available for this article.

Although 7-T MRI has recently received approval for use in clinical patient care, there are distinct safety issues associated with this relatively high magnetic field. Forces on metallic implants and radiofrequency power deposition and heating are safety considerations at 7 T. Patient bioeffects such as vertigo, dizziness, false feelings of motion, nausea, nystagmus, magnetophosphenes, and electrogustatory effects are more common and potentially more pronounced at 7 T than at lower field strengths. Herein the authors review safety issues associated with 7-T MRI. The rationale for safety concerns at this field strength are discussed as well as potential approaches to mitigate risk to patients and health care professionals.

Background There is lack of consensus regarding the use of breast MRI for routine surveillance for second breast cancer events in women with a personal history of breast cancer. Purpose To compare performance of surveillance mammography with breast MRI. Materials and Methods This observational cohort study used prospectively collected data and included 13 266 women age 18 years and older (mean age, 60 years ± 13) with stage 0-III breast cancer who underwent 33 938 mammographic examinations and 2506 breast MRI examinations from 2005 to 2012 in the Breast Cancer Surveillance Consortium. Women were categorized into two groups: mammography alone (n = 11 745) or breast MRI (n = 1521). Performance measures were calculated by using end-of-day assessment and occurrence of second breast cancer events within 1 year of imaging. Logistic regression was used to compare performance for breast MRI versus mammography alone, adjusting for women, examination, and primary breast cancer characteristics. Analysis was conducted on a per-examination basis. Results Breast MRI was associated with younger age at diagnosis, chemotherapy, and higher education and income. Raw performance measures for breast MRI versus mammography were as follows, respectively: cancer detection rates, 10.8 (95% confidence interval [CI]: 6.7, 14.8) versus 8.2 (95% CI: 7.3, 9.2) per 1000 examinations; sensitivity, 61.4% (27 of 44; 95% CI: 46.5%, 76.2%) versus 70.3% (279 of 397; 95% CI: 65.8%, 74.8%); and biopsy rate, 10.1% (253 of 2506; 95% CI: 8.9%, 11.3%) versus 4.0% (1343 of 33 938; 95% CI: 3.7%, 4.2%). In multivariable models, breast MRI was associated with higher biopsy rate (odds ratio [OR], 2.2; 95% CI: 1.9, 2.7; P < .001) and cancer detection rate (OR, 1.7; 95% CI: 1.1, 2.7; P = .03) than mammography alone. However, there were no differences in sensitivity (OR, 1.1; 95% CI: 0.4, 2.9; P = .84) or interval cancer rate (OR, 1.1; 95% CI: 0.6, 2.2; P = .70). Conclusion Comparison of the performance of surveillance breast MRI with mammography must account for patient characteristics. Whereas breast MRI leads to higher biopsy and cancer detection rates, there were no significant differences in sensitivity or interval cancers compared with mammography. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Newell in this issue.

Cystic renal cell carcinoma (RCC) is almost certainly overdiagnosed and overtreated. Efforts to diagnose and treat RCC at a curable stage result in many benign neoplasms and indolent cancers being resected without clear benefit. This is especially true for cystic masses, which compared with solid masses are more likely to be benign and, when malignant, less aggressive. For more than 30 years, the Bosniak classification has been used to stratify the risk of malignancy in cystic renal masses. Although it is widely used and still effective, the classification does not formally incorporate masses identified at MRI or US or masses that are incompletely characterized but are highly likely to be benign, and it is affected by interreader variability and variable reported malignancy rates. The Bosniak classification system cannot fully differentiate aggressive from indolent cancers and results in many benign masses being resected. This proposed update to the Bosniak classification addresses some of these shortcomings. The primary modifications incorporate MRI, establish definitions for previously vague imaging terms, and enable a greater proportion of masses to enter lower-risk classes. Although the update will require validation, it aims to expand the number of cystic masses to which the Bosniak classification can be applied while improving its precision and accuracy for the likelihood of cancer in each class.

Background Cortical lesions develop early in multiple sclerosis (MS) and play a major role in disease progression. MRI at 7.0 T shows high sensitivity for detection of cortical lesions as well as better spatial resolution and signal-to-noise ratio compared with lower field strengths. Purpose To longitudinally characterize (a) the development and evolution of cortical lesions in multiple sclerosis across the cortical width, sulci, and gyri; (b) their relation with white matter lesion accrual; and (c) the contribution of 7.0-T cortical and white matter lesion load and cortical thickness to neurologic disability. Materials and Methods Twenty participants with relapsing-remitting MS and 13 with secondary progressive MS, along with 10 age-matched healthy controls, were prospectively recruited from 2010 to 2016 to acquire, in two imaging sessions (mean interval, 1.5 years), 7.0-T MRI T2*-weighted gradient-echo images (0.33 × 0.33 × 1.0 mm3) for cortical and white matter lesion segmentation and 3.0-T T1-weighted images for cortical surface reconstruction and cortical thickness estimation. Cortical lesions were sampled through the cortex to quantify cortical lesion distribution. The Expanded Disability Status Scale (EDSS) was used to assess neurologic disability. Nonparametric statistics assessed differences between and within groups in MRI metrics of cortical and white matter lesion burden; regression analysis explored associations of disability with MRI metrics. Results Twenty-five of 31 (81%) participants developed new cortical lesions per year (intracortical, 1.3 ± 1.7 vs leukocortical, 0.7 ± 1.9; P = .04), surpassing white matter lesion accrual (cortical, 2.0 ± 2.8 vs white matter, 0.7 ± 0.6; P = .01). In contrast to white matter lesions, cortical lesion accrual was greater in participants with secondary progressive MS than with relapsing-remitting MS (3.6 lesions/year ± 4.2 vs 1.1 lesions/year ± 0.9, respectively; P = .03) and preferentially localized in sulci. Total cortical lesion volume independently predicted baseline EDSS (β = 1.5, P < .001) and EDSS changes at follow-up (β = 0.5, P = .003). Conclusion Cortical lesions predominantly develop intracortically and within sulci, suggesting an inflammatory cerebrospinal fluid-mediated lesion pathogenesis. Cortical lesion accumulation was prominent at 7.0 T and independently predicted neurologic disability progression. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Filippi and Rocca in this issue.

Hyperpolarized (HP) carbon 13 (13C) MRI is an emerging molecular imaging method that allows rapid, noninvasive, and pathway-specific investigation of dynamic metabolic and physiologic processes that were previously inaccessible to imaging. This technique has enabled real-time in vivo investigations of metabolism that are central to a variety of diseases, including cancer, cardiovascular disease, and metabolic diseases of the liver and kidney. This review provides an overview of the methods of hyperpolarization and 13C probes investigated to date in preclinical models of disease. The article then discusses the progress that has been made in translating this technology for clinical investigation. In particular, the potential roles and emerging clinical applications of HP [1-13C]pyruvate MRI will be highlighted. The future directions to enable the adoption of this technology to advance the basic understanding of metabolism, to improve disease diagnosis, and to accelerate treatment assessment are also detailed.

Background Screen-printed MRI coil technology may reduce the need for bulky and heavy housing of coil electronics and may provide a better fit to patient anatomy to improve coil performance. Purpose To assess the performance and caregiver and clinician acceptance of a pediatric-sized screen-printed flexible MRI coil array as compared with conventional coil technology. Materials and Methods A pediatric-sized 12-channel coil array was designed by using a screen-printing process. Element coupling and phantom signal-to-noise ratio (SNR) were assessed. Subjects were scanned by using the pediatric printed array between September and November 2017; results were compared with three age- and sex-matched historical control subjects by using a commercial 32-channel cardiac array at 3 T. Caregiver acceptance was assessed by asking nurses, technologists, anesthesiologists, and subjects or parents to rate their coil preference. Diagnostic quality of the images was evaluated by using a Likert scale (5 = high image quality, 1 = nondiagnostic). Image SNR was evaluated and compared. Results Twenty study participants were evaluated with the screen-printed coil (age range, 2 days to 12 years; 11 male and nine female subjects). Loaded pediatric phantom testing yielded similar noise covariance matrices and only slightly degraded SNR for the printed coil as compared with the commercial coil. The caregiver acceptance survey yielded a mean score of 4.1 ± 0.6 (scale: 1, preferred the commercial coil; 5, preferred the printed coil). Diagnostic quality score was 4.5 ± 0.6. Mean image SNR was 54 ± 49 (paraspinal muscle), 78 ± 51 (abdominal wall muscle), and 59 ± 35 (psoas) for the printed coil, as compared with 64 ± 55, 65 ± 48, and 57 ± 43, respectively, for the commercial coil; these SNR differences were not statistically significant (P = .26). Conclusion A flexible screen-printed pediatric MRI receive coil yields adequate signal-to-noise ratio in phantoms and pediatric study participants, with similar image quality but higher preference by subjects and their caregivers when compared with a conventional MRI coil. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Lamb in this issue.

Purpose To longitudinally monitor liver fat before and after bariatric surgery by using quantitative chemical shift-encoded (CSE) MRI and to compare with changes in body mass index (BMI), weight, and waist circumference (WC). Materials and Methods For this prospective study, which was approved by the internal review board, a total of 126 participants with obesity who were undergoing evaluation for bariatric surgery with preoperative very low calorie diet (VLCD) were recruited from June 27, 2010, through May 5, 2015. Written informed consent was obtained from all participants. Participants underwent CSE MRI measuring liver proton density fat fraction (PDFF) before VLCD (2-3 weeks before surgery), after VLCD (1-3 days before surgery), and 1, 3, and 6-10 months following surgery. Linear regression was used to estimate rates of change of PDFF (ΔPDFF) and body anthropometrics. Initial PDFF (PDFF0), initial anthropometrics, and anthropometric rates of change were evaluated as predictors of ΔPDFF. Mixed-effects regression was used to estimate time to normalization of PDFF. Results Fifty participants (mean age, 51.0 years; age range, 27-70 years), including 43 women (mean age, 50.8 years; age range, 27-70 years) and seven men (mean age, 51.7 years; age range, 36-62 years), with mean PDFF0 ± standard deviation of 18.1% ± 8.6 and mean BMI0 of 44.9 kg/m2 ± 6.5 completed the study. By 6-10 months following surgery, mean PDFF decreased to 4.9% ± 3.4 and mean BMI decreased to 34.5 kg/m2 ± 5.4. Mean estimated time to PDFF normalization was 22.5 weeks ± 11.5. PDFF0 was the only strong predictor for both ΔPDFF and time to PDFF normalization. No body anthropometric correlated with either outcome. Conclusion Average liver proton density fat fraction (PDFF) decreased to normal (< 5%) by 6-10 months following surgery, with mean time to normalization of approximately 5 months. Initial PDFF was a strong predictor of both rate of change of PDFF and time to normalization. Body anthropometrics did not predict either outcome. Online supplemental material is available for this article. © RSNA, 2018.

Purpose To investigate ferumoxytol-enhanced MRI as a noninvasive imaging biomarker of macrophages in adults with high-grade gliomas. Materials and Methods In this prospective study, adults with high-grade gliomas were enrolled between July 2015 and July 2017. Each participant was administered intravenous ferumoxytol (5 mg/kg) and underwent 3.0-T MRI 24 hours later. Two sites in each tumor were selected for intraoperative sampling on the basis of the degree of ferumoxytol-induced signal change. Susceptibility and the relaxation rates R2* (1/T2*) and R2 (1/T2) were obtained by region-of-interest analysis by using the respective postprocessed maps. Each sample was stained with Prussian blue, CD68, CD163, and glial fibrillary acidic protein. Pearson correlation and linear mixed models were performed to assess the relationship between imaging measurements and number of 400× magnification high-power fields with iron-containing macrophages. Results Ten adults (four male participants [mean age, 65 years ± 9 {standard deviation}; age range, 57-74 years] and six female participants [mean age, 53 years ± 12 years; age range, 32-65 years]; mean age of all participants, 58 years ± 12 [age range, 32-74 years]) with high-grade gliomas were included. Significant positive correlations were found between susceptibility, R2*, and R2' and the number of high-power fields with CD163-positive (r range, 0.64-0.71; P < .01) and CD68-positive (r range, 0.55-0.57; P value range, .01-.02) iron-containing macrophages. No significant correlation was found between R2 and CD163-positive (r = 0.33; P = .16) and CD68-positive (r = 0.24; P = .32) iron-containing macrophages. Similar significance results were obtained with linear mixed models. At histopathologic analysis, iron particles were found only in macrophages; none was found in glial fibrillary acidic protein-positive tumor cells. Conclusion MRI measurements of susceptibility, R2*, and R2' (R2* - R2) obtained after ferumoxytol administration correlate with iron-containing macrophage concentration, and this shows their potential as quantitative imaging markers of macrophages in malignant gliomas. © RSNA, 2018 Online supplemental material is available for this article.

Purpose To compare the diagnostic accuracy of gallium 68 (68Ga)-labeled prostate-specific membrane antigen (PSMA)-11 PET/MRI with that of multiparametric MRI in the detection of prostate cancer. Materials and Methods The authors performed a retrospective study of men with biopsy-proven prostate cancer who underwent simultaneous 68Ga-PSMA-11 PET/MRI before radical prostatectomy between December 2015 and June 2017. The reference standard was whole-mount pathologic examination. Readers were blinded to radiologic and pathologic findings. Tumor localization was based on 30 anatomic regions. Region-specific sensitivity and specificity were calculated for PET/MRI and multiparametric MRI by using raw stringent and alternative neighboring approaches. Maximum standardized uptake value (SUVmax) in the tumor and Prostate Imaging Reporting and Data System (PI-RADS) version 2 grade were compared with tumor Gleason score. Generalized estimating equations were used to estimate population-averaged sensitivity and specificity and to determine the association between tumor characteristics and SUVmax or PI-RADS score. Results Thirty-two men (median age, 68 years; interquartile range: 62-71 years) were imaged. The region-specific sensitivities of PET/MRI and multiparametric MRI were 74% (95% confidence interval [CI]: 70%, 77%) and 50% (95% CI: 45%, 0.54%), respectively, with the alternative neighboring approach (P < .001 for both) and 73% (95% CI: 68%, 79%) and 69% (95% CI: 62%, 75%), respectively, with the population-averaged generalized estimating equation (P = .04). Region-specific specificity of PET/MRI was similar to that of multiparametric MRI with the alternative neighboring approach (88% [95% CI: 85%, 91%] vs 90% [95% CI: 87%, 92%], P = .99) and in population-averaged estimates (70% [95% CI: 64%, 76%] vs 70% [95% CI: 64%, 75%], P = .99). SUVmax was associated with a Gleason score of 7 and higher (odds ratio: 1.71 [95% CI: 1.27, 2.31], P < .001). Conclusion The sensitivity of gallium 68-labeled prostate-specific membrane antigen-11 PET/MRI in the detection of prostate cancer is better than that of multiparametric MRI. © RSNA, 2018 See also the editorial by Civelek in this issue.

Purpose To evaluate whether patients with neurofibromatosis type 1 (NF1)-a multisystem neurodevelopmental disorder with myriad imaging manifestations, including focal transient myelin vacuolization within the deep gray nuclei, brainstem, and cerebellum-exhibit differences in cortical and subcortical structures, particularly in subcortical regions where these abnormalities manifest. Materials and Methods In this retrospective study, by using clinically obtained three-dimensional T1-weighted MR images and established image analysis methods, 10 intracranial volume-corrected subcortical and 34 cortical regions of interest (ROIs) were quantitatively assessed in 32 patients with NF1 and 245 age- and sex-matched healthy control subjects. By using linear models, ROI cortical thicknesses and volumes were compared between patients with NF1 and control subjects, as a function of age. With hierarchic cluster analysis and partial correlations, differences in the pattern of association between cortical and subcortical ROI volumes in patients with NF1 and control subjects were also evaluated. Results Patients with NF1 exhibited larger subcortical volumes and thicker cortices of select regions, particularly the hippocampi, amygdalae, cerebellar white matter, ventral diencephalon, thalami, and occipital cortices. For the thalami and pallida and 22 cortical ROIs in patients with NF1, a significant inverse association between volume and age was found, suggesting that volumes decrease with increasing age. Moreover, compared with those in control subjects, ROIs in patients with NF1 exhibited a distinct pattern of clustering and partial correlations. Discussion Neurofibromatosis type 1 is characterized by larger subcortical volumes and thicker cortices of select structures. Most apparent within the hippocampi, amygdalae, cerebellar white matter, ventral diencephalon, thalami and occipital cortices, these neurofibromatosis type 1-associated volumetric changes may, in part, be age dependent. © RSNA, 2018 Online supplemental material is available for this article.

Purpose To analyze how automatic segmentation translates in accuracy and precision to morphology and relaxometry compared with manual segmentation and increases the speed and accuracy of the work flow that uses quantitative magnetic resonance (MR) imaging to study knee degenerative diseases such as osteoarthritis (OA). Materials and Methods This retrospective study involved the analysis of 638 MR imaging volumes from two data cohorts acquired at 3.0 T: (a) spoiled gradient-recalled acquisition in the steady state T1ρ-weighted images and (b) three-dimensional (3D) double-echo steady-state (DESS) images. A deep learning model based on the U-Net convolutional network architecture was developed to perform automatic segmentation. Cartilage and meniscus compartments were manually segmented by skilled technicians and radiologists for comparison. Performance of the automatic segmentation was evaluated on Dice coefficient overlap with the manual segmentation, as well as by the automatic segmentations' ability to quantify, in a longitudinally repeatable way, relaxometry and morphology. Results The models produced strong Dice coefficients, particularly for 3D-DESS images, ranging between 0.770 and 0.878 in the cartilage compartments to 0.809 and 0.753 for the lateral meniscus and medial meniscus, respectively. The models averaged 5 seconds to generate the automatic segmentations. Average correlations between manual and automatic quantification of T1ρ and T2 values were 0.8233 and 0.8603, respectively, and 0.9349 and 0.9384 for volume and thickness, respectively. Longitudinal precision of the automatic method was comparable with that of the manual one. Conclusion U-Net demonstrates efficacy and precision in quickly generating accurate segmentations that can be used to extract relaxation times and morphologic characterization and values that can be used in the monitoring and diagnosis of OA. © RSNA, 2018 Online supplemental material is available for this article.

Purpose To assess the diagnostic performance and interreader agreement of a standardized diagnostic algorithm in determining the histologic type of small (≤4 cm) renal masses (SRMs) with multiparametric magnetic resonance (MR) imaging. Materials and Methods This single-center retrospective HIPAA-compliant institutional review board-approved study included 103 patients with 109 SRMs resected between December 2011 and July 2015. The requirement for informed consent was waived. Presurgical renal MR images were reviewed by seven radiologists with diverse experience. Eleven MR imaging features were assessed, and a standardized diagnostic algorithm was used to determine the most likely histologic diagnosis, which was compared with histopathology results after surgery. Interreader variability was tested with the Cohen κ statistic. Regression models using MR imaging features were used to predict the histopathologic diagnosis with 5% significance level. Results Clear cell renal cell carcinoma (RCC) and papillary RCC were diagnosed, with sensitivities of 85% (47 of 55) and 80% (20 of 25), respectively, and specificities of 76% (41 of 54) and 94% (79 of 84), respectively. Interreader agreement was moderate to substantial (clear cell RCC, κ = 0.58; papillary RCC, κ = 0.73). Signal intensity (SI) of the lesion on T2-weighted MR images and degree of contrast enhancement (CE) during the corticomedullary phase were independent predictors of clear cell RCC (SI odds ratio [OR]: 3.19; 95% confidence interval [CI]: 1.4, 7.1; P = .003; CE OR, 4.45; 95% CI: 1.8, 10.8; P < .001) and papillary RCC (CE OR, 0.053; 95% CI: 0.02, 0.2; P < .001), and both had substantial interreader agreement (SI, κ = 0.69; CE, κ = 0.71). Poorer performance was observed for chromophobe histology, oncocytomas, and minimal fat angiomyolipomas, (sensitivity range, 14%-67%; specificity range, 97%-99%), with fair to moderate interreader agreement (κ range = 0.23-0.43). Segmental enhancement inversion was an independent predictor of oncocytomas (OR, 16.21; 95% CI: 1.0, 275.4; P = .049), with moderate interreader agreement (κ = 0.49). Conclusion The proposed standardized MR imaging-based diagnostic algorithm had diagnostic accuracy of 81% (88 of 109) and 91% (99 of 109) in the diagnosis of clear cell RCC and papillary RCC, respectively, while achieving moderate to substantial interreader agreement among seven radiologists. © RSNA, 2018 Online supplemental material is available for this article.

Purpose To evaluate the effect of background parenchymal enhancement (BPE) on breast magnetic resonance (MR) imaging interpretive performance in a large multi-institutional cohort with independent analysis of screening and diagnostic MR studies. Materials and Methods Analysis of 3770 breast MR studies was conducted. Examinations were performed in 2958 women at six participating facilities in the San Francisco Bay Area from January 2010 to October 2012. Findings were recorded prospectively in the San Francisco Mammography Registry. Performance measures were compared between studies with low BPE (mild or minimal) and those with high BPE (moderate or marked) by using binomial tests of proportions. Results Of 1726 MR imaging studies in the screening group, 1301 were classified as having low BPE and 425 were classified as having high BPE (75% vs 25%, respectively; P < .001). Of 2044 MR imaging studies in the diagnostic group, 1443 were classified as having low BPE and 601 were classified as having high BPE (71% vs 29%, respectively; P < .001). For low versus high BPE groups at screening, abnormal interpretation rate was 157 of 1301 versus 111 of 424 (12% vs 26%, P < .001); biopsy recommendation rate was 85 of 1301 versus 54 of 424 (7% vs 13%, P < .001); and specificity was 89% (95% confidence interval [CI]: 87, 91) versus 75% (95% CI: 71, 80) (P = .01). For the low versus high BPE groups at diagnostic MR imaging, biopsy recommendation rate was 325 of 1443 versus 195 of 601 (23% vs 32%, P < .001); and specificity was 86% (95% CI: 84, 88) versus 75% (95% CI: 74, 82) (P < .001). There were no significant differences between studies with low versus high BPE in sensitivity for screening (76% [95% CI: 55, 91] vs 83% [95% CI: 52, 98]; P = .94) or diagnostic (93% [95% CI: 87, 97] vs 96% [95% CI: 87, 99]; P = .69) MR imaging, nor were there significant differences in cancer detection rate per 1000 patients between the low BPE versus high BPE groups for screening (15 per 1000 vs 24 per 1000, P = .30) or diagnostic (78 per 1000 vs 85 per 1000, P = .64) MR imaging. Conclusion Relative to MR studies with minimal or mild BPE, those with moderate or marked BPE were associated with higher abnormal interpretation and biopsy rates and lower specificity, with no difference in cancer detection rate. © RSNA, 2017 Online supplemental material is available for this article.

Purpose To determine the linearity, bias, and precision of hepatic proton density fat fraction (PDFF) measurements by using magnetic resonance (MR) imaging across different field strengths, imager manufacturers, and reconstruction methods. Materials and Methods This meta-analysis was performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A systematic literature search identified studies that evaluated the linearity and/or bias of hepatic PDFF measurements by using MR imaging (hereafter, MR imaging-PDFF) against PDFF measurements by using colocalized MR spectroscopy (hereafter, MR spectroscopy-PDFF) or the precision of MR imaging-PDFF. The quality of each study was evaluated by using the Quality Assessment of Studies of Diagnostic Accuracy 2 tool. De-identified original data sets from the selected studies were pooled. Linearity was evaluated by using linear regression between MR imaging-PDFF and MR spectroscopy-PDFF measurements. Bias, defined as the mean difference between MR imaging-PDFF and MR spectroscopy-PDFF measurements, was evaluated by using Bland-Altman analysis. Precision, defined as the agreement between repeated MR imaging-PDFF measurements, was evaluated by using a linear mixed-effects model, with field strength, imager manufacturer, reconstruction method, and region of interest as random effects. Results Twenty-three studies (1679 participants) were selected for linearity and bias analyses and 11 studies (425 participants) were selected for precision analyses. MR imaging-PDFF was linear with MR spectroscopy-PDFF (R2 = 0.96). Regression slope (0.97; P < .001) and mean Bland-Altman bias (-0.13%; 95% limits of agreement: -3.95%, 3.40%) indicated minimal underestimation by using MR imaging-PDFF. MR imaging-PDFF was precise at the region-of-interest level, with repeatability and reproducibility coefficients of 2.99% and 4.12%, respectively. Field strength, imager manufacturer, and reconstruction method each had minimal effects on reproducibility. Conclusion MR imaging-PDFF has excellent linearity, bias, and precision across different field strengths, imager manufacturers, and reconstruction methods. © RSNA, 2017 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on October 2, 2017.

Purpose To determine in a large multicenter multireader setting the interreader reliability of Liver Imaging Reporting and Data System (LI-RADS) version 2014 categories, the major imaging features seen with computed tomography (CT) and magnetic resonance (MR) imaging, and the potential effect of reader demographics on agreement with a preselected nonconsecutive image set. Materials and Methods Institutional review board approval was obtained, and patient consent was waived for this retrospective study. Ten image sets, comprising 38-40 unique studies (equal number of CT and MR imaging studies, uniformly distributed LI-RADS categories), were randomly allocated to readers. Images were acquired in unenhanced and standard contrast material-enhanced phases, with observation diameter and growth data provided. Readers completed a demographic survey, assigned LI-RADS version 2014 categories, and assessed major features. Intraclass correlation coefficient (ICC) assessed with mixed-model regression analyses was the metric for interreader reliability of assigning categories and major features. Results A total of 113 readers evaluated 380 image sets. ICC of final LI-RADS category assignment was 0.67 (95% confidence interval [CI]: 0.61, 0.71) for CT and 0.73 (95% CI: 0.68, 0.77) for MR imaging. ICC was 0.87 (95% CI: 0.84, 0.90) for arterial phase hyperenhancement, 0.85 (95% CI: 0.81, 0.88) for washout appearance, and 0.84 (95% CI: 0.80, 0.87) for capsule appearance. ICC was not significantly affected by liver expertise, LI-RADS familiarity, or years of postresidency practice (ICC range, 0.69-0.70; ICC difference, 0.003-0.01 [95% CI: -0.003 to -0.01, 0.004-0.02]. ICC was borderline higher for private practice readers than for academic readers (ICC difference, 0.009; 95% CI: 0.000, 0.021). Conclusion ICC is good for final LI-RADS categorization and high for major feature characterization, with minimal reader demographic effect. Of note, our results using selected image sets from nonconsecutive examinations are not necessarily comparable with those of prior studies that used consecutive examination series. © RSNA, 2017.

The Liver Imaging Reporting and Data System (LI-RADS) standardizes the interpretation, reporting, and data collection for imaging examinations in patients at risk for hepatocellular carcinoma (HCC). It assigns category codes reflecting relative probability of HCC to imaging-detected liver observations based on major and ancillary imaging features. LI-RADS also includes imaging features suggesting malignancy other than HCC. Supported and endorsed by the American College of Radiology (ACR), the system has been developed by a committee of radiologists, hepatologists, pathologists, surgeons, lexicon experts, and ACR staff, with input from the American Association for the Study of Liver Diseases and the Organ Procurement Transplantation Network/United Network for Organ Sharing. Development of LI-RADS has been based on literature review, expert opinion, rounds of testing and iteration, and feedback from users. This article summarizes and assesses the quality of evidence supporting each LI-RADS major feature for diagnosis of HCC, as well as of the LI-RADS imaging features suggesting malignancy other than HCC. Based on the evidence, recommendations are provided for or against their continued inclusion in LI-RADS. © RSNA, 2017 Online supplemental material is available for this article.

Purpose To compare screening magnetic resonance (MR) imaging performance in the Breast Cancer Surveillance Consortium (BCSC) with Breast Imaging Reporting and Data System (BI-RADS) benchmarks. Materials and Methods This study was approved by the institutional review board and compliant with HIPAA and included BCSC screening MR examinations collected between 2005 and 2013 from 5343 women (8387 MR examinations) linked to regional Surveillance, Epidemiology, and End Results program registries, state tumor registries, and pathologic information databases that identified breast cancer cases and tumor characteristics. Clinical, demographic, and imaging characteristics were assessed. Performance measures were calculated according to BI-RADS fifth edition and included cancer detection rate (CDR), positive predictive value of biopsy recommendation (PPV2), sensitivity, and specificity. Results The median patient age was 52 years; 52% of MR examinations were performed in women with a first-degree family history of breast cancer, 46% in women with a personal history of breast cancer, and 15% in women with both risk factors. Screening MR imaging depicted 146 cancers, and 35 interval cancers were identified (181 total-54 in situ, 125 invasive, and two status unknown). The CDR was 17 per 1000 screening examinations (95% confidence interval [CI]: 15, 20 per 1000 screening examinations; BI-RADS benchmark, 20-30 per 1000 screening examinations). PPV2 was 19% (95% CI: 16%, 22%; benchmark, 15%). Sensitivity was 81% (95% CI: 75%, 86%; benchmark, >80%), and specificity was 83% (95% CI: 82%, 84%; benchmark, 85%-90%). The median tumor size of invasive cancers was 10 mm; 88% were node negative. Conclusion The interpretative performance of screening MR imaging in the BCSC meets most BI-RADS benchmarks and approaches benchmark levels for remaining measures. Clinical practice performance data can inform ongoing benchmark development and help identify areas for quality improvement. © RSNA, 2017.

Purpose To determine the relationship of patellofemoral joint alignment and trochlear morphology to superolateral Hoffa fat pad (SHFP) edema on magnetic resonance (MR) images in older adults with or at risk for osteoarthritis of the knee. Materials and Methods Institutional review board approval and written informed consent were obtained from all subjects. The Multicenter Osteoarthritis Study is a prospective cohort study of older adults with or at risk for osteoarthritis of the knee. Subjects were recruited from Birmingham, Alabama, and Iowa City, Iowa. In this cross-sectional study, patellofemoral joint alignment (bisect offset, patellar tilt angle, and Insall-Salvati ratio), trochlear morphology (sulcus angle, lateral and medial trochlear inclination, and trochlear angle) and SHFP edema were assessed on MR images of the knee. Measures of alignment and morphology were divided into quartiles, and SHFP was determined to be present or absent. Separate logistic regression models were used to determine the relationship of each measure of alignment and morphology to the presence of SHFP edema, with adjustments for age, sex, and body mass index. Results SHFP edema was present in 152 (13.4%) of the 1134 knees that were included. When compared with knees with measurements in the lowest quartile, knees with measurements in the highest quartile for trochlear angle, bisect offset, and Insall-Salvati ratios were 1.6 (95% confidence interval [CI]: 1.0, 2.6), 2.3 (95% CI: 1.3, 4.0), and 8.9 (95% CI: 4.7, 16.9) times more likely to show SHFP edema, respectively. No relationship was found between other measures and SHFP edema. Conclusion A more anterior trochlear facet, a more laterally displaced patella, and knees with patella alta were significantly associated with SHFP edema on MR images in subjects with or at risk for osteoarthritis of the knee. © RSNA, 2017.

Purpose To determine if adrenal calcifications seen at computed tomography (CT) are associated with familial cerebral cavernous malformations (fCCMs) in carriers of the CCM1 Common Hispanic Mutation. Materials and Methods This study was approved by the institutional review board. The authors retrospectively reviewed abdominal CT scans in 38 patients with fCCM, 38 unaffected age- and sex-matched control subjects, and 13 patients with sporadic, nonfamilial cerebral cavernous malformation (CCM). The size, number, and laterality of calcifications and the morphologic characteristics of the adrenal gland were recorded. Brain lesion count was recorded from brain magnetic resonance (MR) imaging in patients with fCCM. The prevalence of adrenal calcifications in patients with fCCM was compared with that in unaffected control subjects and those with sporadic CCM by using the Fisher exact test. Additional analyses were performed to determine whether age and brain lesion count were associated with adrenal findings in patients with fCCM. Results Small focal calcifications (SFCs) (≤5 mm) were seen in one or both adrenal glands in 19 of the 38 patients with fCCM (50%), compared with 0 of the 38 unaffected control subjects (P < .001) and 0 of the 13 subjects with sporadic CCM (P = .001). Adrenal calcifications in patients with fCCM were more frequently left sided, with 17 of 19 patients having more SFCs in the left adrenal gland than the right adrenal gland and 50 of the 61 observed SFCs (82%) found in the left adrenal gland. No subjects had SFCs on the right side only. In patients with fCCM, the presence of SFCs showed a positive correlation with age (P < .001) and number of brain lesions (P < .001). Conclusion Adrenal calcifications identified on CT scans are common in patients with fCCM and may be a clinically silent manifestation of disease. © RSNA, 2017.

Purpose To investigate the performance of flourine 18 (18F) fluorocholine (FCH) positron emission tomography (PET)/magnetic resonance (MR) imaging in patients with hyperparathyroidism and nonlocalized disease who have negative or inconclusive results at ultrasonography (US) and technetium 99m (99mTc) sestamibi scintigraphy. Materials and Methods This study was approved by the institutional review board. Between May and December 2015, 10 patients (mean age, 70.4 years; range, 58-82 years) with biochemical primary hyperparathyroidism and inconclusive results at US and 99mTc sestamibi scintigraphy were prospectively enrolled. All patients gave informed consent. Directly after administration of 3 MBq/kg of FCH, PET imaging was performed, followed by T1- and T2-weighted MR imaging before and after gadolinium enhancement. Intraoperative localization and histologic results were the reference standard for calculating sensitivity and positive predictive value. The Wilcoxon rank test was used to calculate the mean difference in maximum standardized uptake value (SUVmax) between abnormal parathyroid uptake and physiologic thyroid uptake. The Wilcoxon rank-sum test was performed. Results MR imaging alone showed true-positive lesions in five patients and a false-positive lesion in one patient. FCH PET/MR imaging allowed correct localization of nine of 10 adenomas (90% sensitivity), without any false-positive results (100% positive predictive value). One patient had four-gland hyperplasia, of which three hyperplastic glands were not localized. The median SUVmax of the nine preoperatively identified adenomas was 4.9 (interquartile range, 2.45-7.35), which was significantly higher than the SUV, 2.7 (interquartile range, 1.6-3.8), of the thyroid (P = .008). Conclusion FCH PET/MR imaging allowed localization of adenomas with high accuracy when conventional imaging results were inconclusive and provided detailed anatomic information. More patients must be examined to confirm our initial results, and the accuracy of FCH PET/MR imaging for localization of glands in patients with four-gland hyperplasia remains to be investigated. © RSNA, 2017.

Purpose To investigate the association of weight loss with progression of cartilage changes at magnetic resonance (MR) imaging over 48 months in overweight and obese participants compared with participants of stable weight. Materials and Methods The institutional review boards of the four participating centers approved this HIPAA-compliant study. Included were (a) 640 participants (mean age, 62.9 years ± 9.1 [standard deviation]; 398 women) who were overweight or obese (body mass index cutpoints of 25 and 30 kg/m2, respectively) from the Osteoarthritis Initiative, with risk factors for osteoarthritis or mild to moderate radiographic findings of osteoarthritis, categorized into groups with (a) weight loss of more than 10% (n = 82), (b) weight loss of 5%-10% (n = 238), or (c) stable weight (n = 320) over 48 months. Participants were frequency-matched for age, sex, baseline body mass index, and Kellgren-Lawrence score. Two radiologists assessed cartilage and meniscus defects on right knee 3-T MR images at baseline and 48 months by using the modified Whole-Organ Magnetic Resonance Imaging Score (WORMS). Progression of the subscores was compared between the weight loss groups by using multivariable logistic regression models. Results Over 48 months, adjusted mean increase of cartilage WORMS was significantly smaller in the 5%-10% weight loss group (1.6; 95% confidence interval [CI]: 1.3, 1.9; P = .002) and even smaller in the group with more than 10% weight loss (1.0; 95% CI: 0.6, 1.4; P = .001) when compared with the stable weight group (2.3; 95% CI: 2.0, 2.7). Moreover, percentage of weight change was significantly associated with increase in cartilage WORMS (β = 0.2; 95% CI: 0.02, 0.4; P = .007). Conclusion Participants who lost weight over 48 months showed significantly lower cartilage degeneration, as assessed with MR imaging; rates of progression were lower with greater weight loss. © RSNA, 2017.

Purpose To assess the underlying genomic variation of prostate gland microenvironments of patients with prostate adenocarcinoma in the context of colocalized multiparametric magnetic resonance (MR) imaging and histopathologic assessment of normal and abnormal regions by using whole-exome sequencing. Materials and Methods Six patients with prostate adenocarcinoma who underwent robotic prostatectomy with whole-mount preservation of the prostate were identified, which enabled spatial mapping between preoperative multiparametric MR imaging and the gland. Four regions of interest were identified within each gland, including regions found to be normal and abnormal via histopathologic analysis. Whole-exome DNA sequencing (>50 times coverage) was performed on each of these spatially targeted regions. Radiogenomic analysis of imaging and mutation data were performed with hierarchical clustering, phylogenetic analysis, and principal component analysis. Results Radiogenomic multiparametric MR imaging and whole-exome spatial characterization in six patients with prostate adenocarcinoma (three patients, Gleason score of 3 + 4; and three patients, Gleason score of 4 + 5) was performed across 23 spatially distinct regions. Hierarchical clustering separated histopathologic analysis-proven high-grade lesions from the normal regions, and this reflected concordance between multiparametric MR imaging and resultant histopathologic analysis in all patients. Seventy-seven mutations involving 29 cancer-associated genes across the 23 spatially distinct prostate samples were identified. There was no significant difference in mutation load in cancer-associated genes between regions that were proven to be normal via histopathologic analysis (34 mutations per sample ± 19), mildly suspicious via multiparametric MR imaging (37 mutations per sample ± 21), intermediately suspicious via multiparametric MR imaging (31 mutations per sample ± 15), and high-grade cancer (33 mutations per sample ± 18) (P = .30). Principal component analysis resolved samples from different patients and further classified samples (regardless of histopathologic status) from prostate glands with Gleason score 3 + 4 versus 4 + 5 samples. Conclusion Multiregion spatial multiparametric MR imaging and whole-exome radiogenomic analysis of prostate glands with adenocarcinoma shows a continuum of mutations across regions that were found via histologic analysis to be high grade and normal. © RSNA, 2017 Online supplemental material is available for this article.

Purpose To determine the diagnostic yield of in-bore 3-T magnetic resonance (MR) imaging-guided prostate biopsy and stratify performance according to Prostate Imaging Reporting and Data System (PI-RADS) versions 1 and 2. Materials and Methods This study was HIPAA compliant and institution review board approved. In-bore 3-T MR-guided prostate biopsy was performed in 134 targets in 106 men who (a) had not previously undergone prostate biopsy, (b) had prior negative biopsy findings with increased prostate-specific antigen (PSA) level, or (c) had a prior history of prostate cancer with increasing PSA level. Clinical, diagnostic 3-T MR imaging was performed with in-bore guided prostate biopsy, and pathology data were collected. The diagnostic yields of MR-guided biopsy per patient and target were analyzed, and differences between biopsy targets with negative and positive findings were determined. Results of logistic regression and areas under the curve were compared between PI-RADS versions 1 and 2. Results Prostate cancer was detected in 63 of 106 patients (59.4%) and in 72 of 134 targets (53.7%) with 3-T MR imaging. Forty-nine of 72 targets (68.0%) had clinically significant cancer (Gleason score ≥ 7). One complication occurred (urosepsis, 0.9%). Patients who had positive target findings had lower apparent diffusion coefficient values (875 × 10-6 mm2/sec vs 1111 × 10-6 mm2/sec, respectively; P < .01), smaller prostate volume (47.2 cm3 vs 75.4 cm3, respectively; P < .01), higher PSA density (0.16 vs 0.10, respectively; P < .01), and higher proportion of PI-RADS version 2 category 3-5 scores when compared with patients with negative target findings. MR targets with PI-RADS version 2 category 2, 3, 4, and 5 scores had a positive diagnostic yield of three of 23 (13.0%), six of 31 (19.4%), 39 of 50 (78.0%), and 24 of 29 (82.8%) targets, respectively. No differences were detected in areas under the curve for PI-RADS version 2 versus 1. Conclusion In-bore 3-T MR-guided biopsy is safe and effective for prostate cancer diagnosis when stratified according to PI-RADS versions 1 and 2. ©RSNA, 2016.