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To assess the diagnostic value of adenosine "stress-only" myocardial perfusion MR for ischemia detection as an indicator for coronary angiography in patients without a prior myocardial infarction and a necessity to exclude ischemia. Adenosine perfusion MRI was performed at 1.5 T in 139 patients with a suspicion of ischemia and no prior myocardial infarction. After 3 min of adenosine infusion a perfusion sequence was started. Patients with a perfusion defect were referred to coronary angiography (CAG). Patients with a normal perfusion were enrolled in follow-up. Fourteen out of 139 patients (10.1%) had a perfusion defect indicative of ischemia. These patients underwent a coronary angiogram, which showed complete agreement with the perfusion images. 125 patients with a normal myocardial perfusion entered follow-up (median 672 days, range 333-1287 days). In the first year of follow-up one Major Adverse Coronary Event (MACE) occurred and one patient had new onset chest pain with a confirmed coronary stenosis. Reaching a negative predictive value for MACE of 99.2% and for any coronary event of 98.4%. At 2 year follow-up no additional MACE occurred. Sensitivity of adenosine perfusion MR for MACE is 93.3% and specificity and positive predictive value are 100%. Adenosine myocardial perfusion MR for the detection of myocardial ischemia in a "stress-only" protocol in patients without prior myocardial infarctions, has a high diagnostic accuracy. This fast examination can play an important role in the evaluation of patients without prior myocardial infarctions and a necessity to exclude ischemia. [ABSTRACT FROM AUTHOR]

An abundance of fat stored within the liver, or steatosis, is the beginning of a broad hepatological spectrum, usually referred to as fatty liver disease (FLD). For studies on FLD, quantitative hepatic fat ultrasonography would be an appealing study modality. Objective of this study was to develop a technique for quantifying hepatic fat content by ultrasonography and validate this using proton magnetic resonance spectroscopy ((1)H MRS) as gold standard. Eighteen white volunteers (BMI range 21.0-42.9) were scanned by both ultrasonography and (1)H MRS. Altered ultrasound characteristics, present in the case of FLD, were assessed using a specially developed software program. Various attenuation and textural based indices of FLD were extracted from ultrasound images. Using linear regression analysis, the predictive power of several models (consisting of both attenuation and textural based measures) on log 10-transformed hepatic fat content by (1)H MRS were investigated. The best quantitative model was compared with a qualitative ultrasonography method, as used in clinical care. A model with four ultrasound characteristics could modestly predict the amount of liver fat (adjusted explained variance 43.2%, P = 0.021). Expanding the model to seven ultrasound characteristics increased adjusted explained variance to 60% (P = 0.015), with r = 0.789 (P < 0.001). Comparing this quantitative model with qualitative ultrasonography revealed a significant advantage of the quantitative model in predicting hepatic fat content (P < 0.001). This validation study shows that a combination of computer-assessed ultrasound measures from routine ultrasound images can be used to quantitatively assess hepatic fat content. [ABSTRACT FROM AUTHOR]

MRI, as a completely noninvasive technique, can provide quantitative assessment of perfusion, diffusion, viscoelasticity and metabolism, yielding diverse information about liver function. Furthermore, pathological accumulations of iron and lipids can be quantified. Perfusion MRI with various contrast agents is commonly used for the detection and characterization of focal liver disease and the quantification of blood flow parameters. An extended new application is the evaluation of the therapeutic effect of antiangiogenic drugs on liver tumours. Novel, but already widespread, is a histologically validated relaxometry method using five gradient echo sequences for quantifying liver iron content elevation, a measure of inflammation, liver disease and cancer. Because of the high perfusion fraction in the liver, the apparent diffusion coefficients strongly depend on the gradient factors used in diffusion-weighted MRI. While complicating analysis, this offers the opportunity to study perfusion without contrast injection. Another novel method, MR elastography, has already been established as the only technique able to stage fibrosis or diagnose mild disease. Liver fat content is accurately determined with multivoxel MR spectroscopy (MRS) or by faster MRI methods that are, despite their widespread use, prone to systematic error. Focal liver disease characterisation will be of great benefit once multivoxel methods with fat suppression are implemented in proton MRS, in particular on high-field MR systems providing gains in signal-to-noise ratio and spectral resolution. [ABSTRACT FROM AUTHOR]

Acute liver failure (ALF)-related encephalopathy was previously characterized by MR spectroscopy of single voxels containing both grey and white matter brain tissue. Quantitative multivoxel MRS was used here to compare grey and white matter brain tissue concentrations of glutamate/glutamine (Glx) and lactate in ALF and associate the results with other liver function parameters. Five pediatric patients with ALF-related encephalopathy and five controls, examined after successful liver transplantation, were examined by brain MRI/MRS. ALF patients had higher Glx and lactate concentrations in brain white matter than controls (Glx + 125%: P < 0.01; lactate + 33%, P < 0.05) and higher Glx in grey matter (Glx + 125%: P < 0.01). Within the group of ALF patients positive correlations were found between grey or white matter lactate concentration and serum ammonia (P < 0.05), and negative correlations between grey or white matter Glx and venous pH (P < 0.001). This is the first study presenting evidence of high Glx levels in both white and grey matter brain tissue in ALF-related encephalopathy. The elevations in CNS Glx and lactate concentrations appear to relate to hepatic detoxification (ammonia, venous pH), rather than to liver parenchymal integrity (aspartate aminotransferase, alanine aminotransferase) or biliary cholestasis (bilirubin, gamma-glutamyl transpeptidase, alkaline phosphatase). [ABSTRACT FROM AUTHOR]

A recently published Dixon-based MRI method for quantifying liver fat content using dual-echo breath-hold gradient echo imaging was validated by phantom experiments and compared with results of biopsy in two patients (Radiology 2005;237:1048-1055). We applied this method in ten healthy volunteers and compared the outcomes with the results of MR spectroscopy (MRS), the gold standard in quantifying liver fat content. Novel was the use of spectroscopic imaging yielding the variations in fat content across the liver rather than a single value obtained by single voxel MRS. Compared with the results of MRS, liver fat content according to MRI was too high in nine subjects (range 3.3-10.7% vs. 0.9-7.7%) and correct in one (21.1 vs. 21.3%). Furthermore, in one of the ten subjects the MRI fat content according to the Dixon-based MRI method was incorrect due to a (100-x) versus x percent lipid content mix-up. The second problem was fixed by a minor adjustment of the MRI algorithm. Despite systematic overestimation of liver fat contents by MRI, Spearman's correlation between the adjusted MRI liver fat contents with MRS was high (r = 0.927, P < 0.001). Even after correction of the algorithm, the problem remaining with the Dixon-based MRI method for the assessment of liver fat content,is that, at the lower end range, liver fat content is systematically overestimated by 4%. [ABSTRACT FROM AUTHOR]

Background: Accurate breast density evaluation allows for more precise risk estimation but suffers from high inter-observer variability., Purpose: To evaluate the feasibility of reducing inter-observer variability of breast density assessment through artificial intelligence (AI) assisted interpretation., Study Type: Retrospective., Population: Six hundred and twenty-one patients without breast prosthesis or reconstructions were randomly divided into training (N = 377), validation (N = 98), and independent test (N = 146) datasets., Field Strength/sequence: 1.5 T and 3.0 T; T1-weighted spectral attenuated inversion recovery., Assessment: Five radiologists independently assessed each scan in the independent test set to establish the inter-observer variability baseline and to reach a reference standard. Deep learning and three radiomics models were developed for three classification tasks: (i) four Breast Imaging-Reporting and Data System (BI-RADS) breast composition categories (A-D), (ii) dense (categories C, D) vs. non-dense (categories A, B), and (iii) extremely dense (category D) vs. moderately dense (categories A-C). The models were tested against the reference standard on the independent test set. AI-assisted interpretation was performed by majority voting between the models and each radiologist's assessment., Statistical Tests: Inter-observer variability was assessed using linear-weighted kappa (κ) statistics. Kappa statistics, accuracy, and area under the receiver operating characteristic curve (AUC) were used to assess models against reference standard., Results: In the independent test set, five readers showed an overall substantial agreement on tasks (i) and (ii), but moderate agreement for task (iii). The best-performing model showed substantial agreement with reference standard for tasks (i) and (ii), but moderate agreement for task (iii). With the assistance of the AI models, almost perfect inter-observer variability was obtained for tasks (i) (mean κ = 0.86), (ii) (mean κ = 0.94), and (iii) (mean κ = 0.94)., Data Conclusion: Deep learning and radiomics models have the potential to help reduce inter-observer variability of breast density assessment., Level of Evidence: 3 TECHNICAL EFFICACY: Stage 1., (© 2023 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Objectives: To compare image quality of diffusion-weighted imaging (DWI) and contrast-enhanced breast MRI (DCE-T1) stratified by the amount of fibroglandular tissue (FGT) as a measure of breast density., Methods: Retrospective, multi-reader, bicentric visual grading analysis study on breast density (A-D) and overall image and fat suppression quality of DWI and DCE-T1, scored on a standard 5-point Likert scale. Cross tabulations and visual grading characteristic (VGC) curves were calculated for fatty breasts (A/B) versus dense breasts (C/D)., Results: Image quality of DWI was higher in the case of increased breast density, with good scores (score 3-5) in 85.9% (D) and 88.4% (C), compared to 61.6% (B) and 53.5% (A). Overall image quality of DWI was in favor of dense breasts (C/D), with an area under the VGC curve of 0.659 (p < 0.001). Quality of DWI and DCE-T1 fat suppression increased with higher breast density, with good scores (score 3-5) for 86.9% and 45.7% of density D, and 90.2% and 42.9% of density C cases, compared to 76.0% and 33.6% for density B and 54.7% and 29.6% for density A (DWI and DCE-T1 respectively)., Conclusions: Dense breasts show excellent fat suppression and substantially higher image quality in DWI images compared with non-dense breasts. These results support the setup of studies exploring DWI-based MR imaging without IV contrast for additional screening of women with dense breasts., Clinical Relevance Statement: Our findings demonstrate that image quality of DWI is robust in women with an increased amount of fibroglandular tissue, technically supporting the feasibility of exploring applications such as screening of women with mammographically dense breasts., Key Points: • Image and fat suppression quality of diffusion-weighted imaging are dependent on the amount of fibroglandular tissue (FGT) which is closely connected to breast density. • Fat suppression quality in diffusion-weighted imaging of the breast is best in women with a high amount of fibroglandular tissue. • High image quality of diffusion-weighted imaging in women with a high amount of FGT in MRI supports that the technical feasibility of DWI can be explored in the additional screening of women with mammographically dense breasts., (© 2023. The Author(s).)

Objectives: To develop a deep learning-based method for contrast-enhanced breast lesion detection in ultrafast screening MRI., Materials and Methods: A total of 837 breast MRI exams of 488 consecutive patients were included. Lesion's location was independently annotated in the maximum intensity projection (MIP) image of the last time-resolved angiography with stochastic trajectories (TWIST) sequence for each individual breast, resulting in 265 lesions (190 benign, 75 malignant) in 163 breasts (133 women). YOLOv5 models were fine-tuned using training sets containing the same number of MIP images with and without lesions. A long short-term memory (LSTM) network was employed to help reduce false positive predictions. The integrated system was then evaluated on test sets containing enriched uninvolved breasts during cross-validation to mimic the performance in a screening scenario., Results: In five-fold cross-validation, the YOLOv5x model showed a sensitivity of 0.95, 0.97, 0.98, and 0.99, with 0.125, 0.25, 0.5, and 1 false positive per breast, respectively. The LSTM network reduced 15.5% of the false positive prediction from the YOLO model, and the positive predictive value was increased from 0.22 to 0.25., Conclusions: A fine-tuned YOLOv5x model can detect breast lesions on ultrafast MRI with high sensitivity in a screening population, and the output of the model could be further refined by an LSTM network to reduce the amount of false positive predictions., Clinical Relevance Statement: The proposed integrated system would make the ultrafast MRI screening process more effective by assisting radiologists in prioritizing suspicious examinations and supporting the diagnostic workup., Key Points: • Deep convolutional neural networks could be utilized to automatically pinpoint breast lesions in screening MRI with high sensitivity. • False positive predictions significantly increased when the detection models were tested on highly unbalanced test sets with more normal scans. • Dynamic enhancement patterns of breast lesions during contrast inflow learned by the long short-term memory networks helped to reduce false positive predictions., (© 2023. The Author(s).)

Isocitrate dehydrogenase (IDH) mutation status is an important biomarker in the glioma-defining subtype and corresponding prognosis. This study proposes a straightforward method for 2-hydroxyglutarate (2-HG) quantification by MR spectroscopy for IDH mutation status detection and directly compares in vivo 2-HG MR spectroscopy with ex vivo 2-HG concentration measured in resected tumor tissue. Eleven patients with suspected lower-grade glioma (ten IDH1; one IDHwt) were prospectively included. Preoperatively, 3T point-resolved spectroscopy (PRESS) was acquired; 2-HG was measured as the percentage elevation of Glx3 (the sum of 2-HG and Glx) compared to Glx4. IDH mutation status was assessed by immunochemistry or direct sequencing. The ex vivo 2-HG concentration was determined in surgically obtained tissue specimens using gas chromatography-mass spectrometry. Pearson correlation was used for assessing the correlation between in vivo MR spectroscopy and ex vivo 2-HG concentration. MR spectroscopy was positive for 2-HG in eight patients, all of whom had IDH1 tumors. A strong correlation (r = 0.80, p = 0.003) between 2-HG MR spectroscopy and the ex vivo 2-HG concentration was found. This study shows in vivo 2-HG MR spectroscopy can non-invasively determine IDH status in glioma and demonstrates a strong correlation with ex vivo 2-HG concentration in patients with lower-grade glioma.

Objective: Reduced FOV-diffusion-weighted imaging (rFOV-DWI) allows for acquisition of a tissue region without back-folding, and may have better fat suppression than conventional DWI imaging (c-DWI). The aim was to compare the ADCs obtained with c-DWI bilateral-breast imaging with single-breast rFOV-DWI., Materials and Methods: Breasts of 38 patients were scanned at 3 T. The mean ADC values obtained for 38 lesions, and fibro-glandular (N = 35) and adipose (N = 38) tissue ROIs were compared between c-DWI and higher-resolution rFOV-DWI (Wilcoxon rank test). Also, the ADCs were compared between the two acquisitions for an oil-only phantom and a combined water/oil phantom. Furthermore, ghost artifacts were assessed., Results: No significant difference in mean ADC was found between the acquisitions for lesions (c-DWI: 1.08 × 10 -3 mm 2 /s, rFOV-DWI: 1.13 × 10 -3 mm 2 /s) and fibro-glandular tissue. For adipose tissue, the ADC using rFOV-DWI (0.31 × 10 -3 mm 2 /s) was significantly higher than c-DWI (0.16 × 10 -3 mm 2 /s). For the oil-only phantom, no difference in ADC was found. However, for the water/oil phantom, the ADC of oil was significantly higher with rFOV-DWI compared to c-DWI., Discussion: Although ghost artifacts were observed for both acquisitions, they appeared to have a greater impact for rFOV-DWI. However, no differences in mean lesions' ADC values were found, and therefore this study suggests that rFOV can be used diagnostically for single-breast DWI imaging., (© 2022. The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB).)

Purpose: To investigate the influence of the sodium (Na) reference tube location in a birdcage coil on the quantification of Na in the calf muscle. Two correction methods were also evaluated., Method: Eight (4 × 20 mM, 4 × 30 mM Na) reference tubes were placed along the inner surface of the coil and one (30 mM Na) tube more centrally near the tibia. In two volunteers, four repeated UTE scans were acquired. In six calf muscles, the Na concentration was calculated based on each reference tube. Flip angle mapping of a homogenous Na phantom was used for correcting intensity values. Alternatively, a normalized intensity map was used for correcting the in vivo signal intensities. Results were given as range or SD of Na concentration measurements over the reference tubes., Results: For calf Na measurements, there was limited space for positioning reference tubes away from coil B 1 inhomogeneity. In both volunteers, the Na quantification depended greatly on the reference tube used with a range of up to 10 mM. The central tube location gave a Na quantification close to the mean of the other tubes. The flip angle and normalized signal intensity phantom-based correction methods decreased the quantification variation from 14.9% to 5.0% and 10.4% to 2.7%, respectively. Both correction methods had little influence (< 2.3%) on quantification based on the central tube., Conclusion: Despite use of a birdcage coil, location of the reference tube had a great impact on Na quantification in the calf muscles. Although both correction methods did reduce this variation, placing the reference tube more centrally was found to give the most reliable results., (© 2023 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Objectives: To investigate the feasibility of automatically identifying normal scans in ultrafast breast MRI with artificial intelligence (AI) to increase efficiency and reduce workload., Methods: In this retrospective analysis, 837 breast MRI examinations performed on 438 women from April 2016 to October 2019 were included. The left and right breasts in each examination were labelled normal (without suspicious lesions) or abnormal (with suspicious lesions) based on final interpretation. Maximum intensity projection (MIP) images of each breast were then used to train a deep learning model. A high sensitivity threshold was calculated based on the detection trade - off (DET) curve on the validation set. The performance of the model was evaluated by receiver operating characteristic analysis of the independent test set. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with the high sensitivity threshold were calculated., Results: The independent test set consisted of 178 examinations of 149 patients (mean age, 44 years ± 14 [standard deviation]). The trained model achieved an AUC of 0.81 (95% CI: 0.75-0.88) on the independent test set. Applying a threshold of 0.25 yielded a sensitivity of 98% (95% CI: 90%; 100%), an NPV of 98% (95% CI: 89%; 100%), a workload reduction of 15.7%, and a scan time reduction of 16.6%., Conclusion: This deep learning model has a high potential to help identify normal scans in ultrafast breast MRI and thereby reduce radiologists' workload and scan time., Key Points: • Deep learning in TWIST may eliminate the necessity of additional sequences for identifying normal breasts during MRI screening. • Workload and scanning time reductions of 15.7% and 16.6%, respectively, could be achieved with the cost of 1 (1 of 55) false negative prediction., (© 2022. The Author(s).)

Objectives: This study aimed at evaluating left ventricular myocardial pixel-wise T2* using two truncation methods for different iron deposition T2* ranges and comparison of segmental T2* in different coronary artery territories., Material and Methods: Bright blood multi-gradient echo data of 30 patients were quantified by pixel-wise monoexponential T2* fitting with its R 2 and SNR truncation. T2* was analyzed at different iron classifications. At low iron classification, T2* values were also analyzed by coronary artery territories., Results: The right coronary artery has a significantly higher T2* value than the other coronary artery territories. No significant difference was found in classifying severe iron by the two truncation methods in any myocardial region, whereas in moderate iron, it is only apparent at septal segments. The R 2 truncation produces a significantly higher T2* value than the SNR method when low iron is indicated., Conclusion: Clear T2* differentiation between the three coronary territories by the two truncation methods is demonstrated. The two truncation methods can be used interchangeably in classifying severe and moderate iron deposition at the recommended septal region. However, in patients with low iron indication, different results by the two truncation methods can mislead the investigation of early iron level progression., (© 2022. The Author(s).)

Purpose: To investigate the feasibility of using deep learning methods to differentiate benign from malignant breast lesions in ultrafast MRI with both temporal and spatial information., Methods: A total of 173 single breasts of 122 women (151 examinations) with lesions above 5 mm were retrospectively included. A total of 109 out of 173 lesions were benign. Maximum intensity projection (MIP) images were generated from each of the 14 contrast-enhanced T1-weighted acquisitions in the ultrafast MRI scan. A 2D convolutional neural network (CNN) and a long short-term memory (LSTM) network were employed to extract morphological and temporal features, respectively. The 2D CNN model was trained with the MIPs from the last four acquisitions to ensure the visibility of the lesions, while the LSTM model took MIPs of an entire scan as input. The performance of each model and their combination were evaluated with 100-times repeated stratified four-fold cross-validation. Those models were then compared with models developed with standard DCE-MRI which followed the same data split., Results: In the differentiation between benign and malignant lesions, the ultrafast MRI-based 2D CNN achieved a mean AUC of 0.81 ± 0.06, and the LSTM network achieved a mean AUC of 0.78 ± 0.07; their combination showed a mean AUC of 0.83 ± 0.06 in the cross-validation. The mean AUC values were significantly higher for ultrafast MRI-based models than standard DCE-MRI-based models., Conclusion: Deep learning models developed with ultrafast breast MRI achieved higher performances than standard DCE-MRI for malignancy discrimination. The improved AUC values of the combined models indicate an added value of temporal information extracted by the LSTM model in breast lesion characterization.

Objectives: In breast diffusion weighted imaging (DWI) protocol standardization, it is recently shown that no breast tumor tissue selection (BTTS) method outperformed the others. The purpose of this study is to analyze the feasibility of three fixed-size breast tumor tissue selection (BTTS) methods based on the reproducibility, accuracy and time-measurement in comparison to the largest oval and manual delineation in breast diffusion weighted imaging data., Methods: This study is performed with a consecutive dataset of 116 breast lesions (98 malignant) of at least 1.0 cm, scanned in accordance with the EUSOBI breast DWI working group recommendations. Reproducibility of the maximum size manual (BTTS1) and of the maximal size round/oval (BTTS2) methods were compared with three smaller fixed-size circular BTTS methods in the middle of each lesion (BTTS3, 0.12 cm3 volume) and at lowest apparent diffusion coefficient (ADC) (BTTS4, 0.12 cm3; BTTS5, 0.24 cm3). Mean ADC values, intraclass-correlation-coefficients (ICCs), area under the curve (AUC) and measurement times (sec) of the 5 BTTS methods were assessed by two observers., Results: Excellent inter- and intra-observer agreement was found for any BTTS (with ICC 0.88-0.92 and 0.92-0.94, respectively). Significant difference in ADCmean between any pair of BTTS methods was shown (p = <0.001-0.009), except for BTTS2 vs. BTTS3 for observer 1 (p = 0.10). AUCs were comparable between BTTS methods, with highest AUC for BTTS2 (0.89-0.91) and lowest for BTTS4 (0.76-0.85). However, as an indicator of clinical feasibility, BTTS2-3 showed shortest measurement times (10-15 sec) compared to BTTS1, 4-5 (19-39 sec)., Conclusion: The performance of fixed-size BTTS methods, as a potential tool for clinical decision making, shows equal AUC but shorter ADC measurement time compared to manual or oval whole lesion measurements. The advantage of a fixed size BTTS method is the excellent reproducibility. A central fixed breast tumor tissue volume of 0.12 cm3 is the most feasible method for use in clinical practice., Competing Interests: The authors have declared that no competing interests exist.

Cardiac T 2 * mapping is a noninvasive MRI method that is used to identify myocardial iron accumulation in several iron storage diseases such as hereditary hemochromatosis, sickle cell disease, and β-thalassemia major. The method has improved over the years in terms of MR acquisition, focus on relative artifact-free myocardium regions, and T 2 * quantification. Several improvement factors involved include blood pool signal suppression, the reproducibility of T 2 * measurement as affected by scanner hardware, and acquisition software. Regarding the T 2 * quantification, improvement factors include the applied curve-fitting method with or without truncation of the signals acquired at longer echo times and whether or not T 2 * measurement focuses on multiple segmental regions or the midventricular septum only. Although already widely applied in clinical practice, data processing still differs between centers, contributing to measurement outcome variations. State of the art T 2 * measurement involves pixelwise quantification providing better spatial iron loading information than region of interest-based quantification. Improvements have been proposed, such as on MR acquisition for free-breathing mapping, the generation of fast mapping, noise reduction, automatic myocardial contour delineation, and different T 2 * quantification methods. This review deals with the pro and cons of different methods used to quantify T 2 * and generate T 2 * maps. The purpose is to recommend a combination of MR acquisition and T 2 * mapping quantification techniques for reliable outcomes in measuring and follow-up of myocardial iron overload. The clinical application of cardiac T 2 * mapping for iron overload's early detection, monitoring, and treatment is addressed. The prospects of T 2 * mapping combined with different MR acquisition methods, such as cardiac T 1 mapping, are also described. Level of Evidence: 4 Technical Efficacy Stage: 5 J. Magn. Reson. Imaging 2019., (© 2019 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.)

Background: Several methods for tumor delineation are used in literature on breast diffusion weighted imaging (DWI) to measure the apparent diffusion coefficient (ADC). However, in the process of reaching consensus on breast DWI scanning protocol, image analysis and interpretation, still no standardized optimal breast tumor tissue selection (BTTS) method exists. Therefore, the purpose of this study is to assess the impact of BTTS methods on ADC in the discrimination of benign from malignant breast lesions in DWI in terms of sensitivity, specificity and area under the curve (AUC)., Methods and Findings: In this systematic review and meta-analysis, adhering to the PRISMA statement, 61 studies, with 65 study subsets, in females with benign or malignant primary breast lesions (6291 lesions) were assessed. Studies on DWI, quantified by ADC, scanned on 1.5 and 3.0 Tesla and using b-values 0/50 and ≥ 800 s/mm2 were included. PubMed and EMBASE were searched for studies up to 23-10-2019 (n = 2897). Data were pooled based on four BTTS methods (by definition of measured region of interest, ROI): BTTS1: whole breast tumor tissue selection, BTTS2: subtracted whole breast tumor tissue selection, BTTS3: circular breast tumor tissue selection and BTTS4: lowest diffusion breast tumor tissue selection. BTTS methods 2 and 3 excluded necrotic, cystic and hemorrhagic areas. Pooled sensitivity, specificity and AUC of the BTTS methods were calculated. Heterogeneity was explored using the inconsistency index (I2) and considering covariables: field strength, lowest b-value, image of BTTS selection, pre-or post-contrast DWI, slice thickness and ADC threshold. Pooled sensitivity, specificity and AUC were: 0.82 (0.72-0.89), 0.79 (0.65-0.89), 0.88 (0.85-0.90) for BTTS1; 0.91 (0.89-0.93), 0.84 (0.80-0.87), 0.94 (0.91-0.96) for BTTS2; 0.89 (0.86-0.92), 0.90 (0.85-0.93), 0.95 (0.93-0.96) for BTTS3 and 0.90 (0.86-0.93), 0.84 (0.81-0.87), 0.86 (0.82-0.88) for BTTS4, respectively. Significant heterogeneity was found between studies (I2 = 95)., Conclusions: None of the breast tissue selection (BTTS) methodologies outperformed in differentiating benign from malignant breast lesions. The high heterogeneity of ADC data acquisition demands further standardization, such as DWI acquisition parameters and tumor tissue selection to substantially increase the reliability of DWI of the breast., Competing Interests: The authors have declared that no competing interests exist.

Objective: To compare the robustness of native T1 mapping using mean and median pixel-wise quantification methods., Methods: Fifty-seven consecutive patients without overt signs of heart failure were examined in clinical routine for suspicion of cardiomyopathy. MRI included the acquisition of native T1 maps by a motion-corrected modified Look-Locker inversion recovery sequence at 1.5 T. Heart function status according to four established volumetric left ventricular (LV) cardio MRI parameter thresholds was used for retrospective separation into subgroups of normal (n = 26) or abnormal heart function (n = 31). Statistical normality of pixel-wise T1 was tested on each myocardial segment and mean and median segmental T1 values were assessed., Results: Segments with normally distributed pixel-wise T1 (57/58%) showed no difference between mean and median quantification in either patient group, while differences were highly significant (p < 0.001) for the respective 43/42% non-normally distributed segments. Heart function differentiation between two patient groups was significant in 14 myocardial segments (p < 0.001-0.040) by median quantification compared with six (p < 0.001-0.042) by using the mean. The differences by median quantification were observed between the native T1 values of the three coronary artery territories of normal heart function patients (p = 0.023) and insignificantly in the abnormal patients (p = 0.053)., Conclusion: Median quantification increases the robustness of myocardial native T1 definition, regardless of statistical normality of the data. Compared with the currently prevailing method of mean quantification, differentiation between LV segments and coronary artery territories is better and allows for earlier detection of heart function impairment., Key Points: • Median pixel-wise quantification of native T1 maps is robust and can be applied regardless of the statistical distribution of data points. • Median quantification is more sensitive to early heart function abnormality compared with mean quantification. • The new method yields significant native T1 value differentiation between the three coronary artery territories.

Objective: The purpose of this study was to correlate brain metabolism assessed shortly after therapeutic hyperthermia by 1 H magnetic resonance spectroscopy (MRS), with neurodevelopmental outcome., Methods: At the age of 6.0±1.8days, brain metabolites of 35 term asphyxiated newborns, treated with therapeutic hypothermia, were quantified by multivoxel proton MRS of a volume cranial to the corpus callosum, containing both gray and white matter. At the age of 30months the Bayley Scale of Infant Development-III was performed., Results: Infants that died had lower gray matter NAA levels than infants that survived (P=0.005). In surviving infants (28 of 35) there was a trend of negative correlation between gray matter choline levels and gross motor outcome (r=-0.45). In the white matter, choline correlated negatively with fine motor skills (r=-0.40), and creatine positively with gross motor skills (r=0.58, P=0.02). There was no relationship between lactate levels and outcome., Conclusion: MRS of asphyxiated neonates treated by therapeutic hypothermia can serve as predictor of outcome. Unlike previously reported associations in untreated asphyxiates, lactate levels had no relationship with outcome, which indicates that one of the working mechanisms of therapeutic hypothermia is reduction of the metabolic rate., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Objectives: Early detection of iron loading is affected by the reproducibility of myocardial contour assessment. A novel semi-automatic myocardial segmentation method is presented on contrast-optimized composite images and compared to the results of manual drawing., Materials and Methods: Fifty-one short-axis slices at basal, mid-ventricular and apical locations from 17 patients were acquired by bright blood multi-gradient echo MRI. Four observers produced semi-automatic and manual myocardial contours on contrast-optimized composite images. The semi-automatic segmentation method relies on vector field convolution active contours to generate the endocardial contour. After creating radial pixel clusters on the myocardial wall, a combination of pixel-wise coefficient of variance (CoV) assessment and k-means clustering establishes the epicardial contour for each segment., Results: Compared to manual drawing, semi-automatic myocardial segmentation lowers the variability of T2* quantification within and between observers (CoV of 12.05 vs. 13.86% and 14.43 vs. 16.01%) by improving contour reproducibility (P < 0.001). In the presence of iron loading, semi-automatic segmentation also lowers the T2* variability within and between observers (CoV of 13.14 vs. 15.19% and 15.91 vs. 17.28%)., Conclusion: Application of semi-automatic myocardial segmentation on contrast-optimized composite images improves the reproducibility of T2* quantification.

Purpose: To investigate if intravoxel incoherent motion (IVIM) modeled diffusion-weighted imaging (DWI) can be linked to contrast-enhanced (CE-)MRI in liver parenchyma and liver lesions., Methods: Twenty-five patients underwent IVIM-DWI followed by multiphase CE-MRI using Gd-EOB-DTPA (n=20) or Gd-DOTA (n=5) concluded with IVIM-DWI. Diffusion (D slow ), microperfusion (D fast ), its fraction (f fast ), wash-in-rate (R early ) and late-enhancement-rate (R late ) of Gd-EOB-DTPA were calculated voxel-wise for the liver. Parenchyma and lesions were segmented. Pre-contrast IVIM was compared 1) between low, medium and high R early for parenchyma 2) to post-contrast IVIM substantiated with simulations 3) between low and high R late per lesion type., Results: D fast and f fast increased (P<0.001) with 25.6% and 33.8% between low and high R early of Gd-EOB-DTPA. D slow decreased (-15.0%; P<0.001) with increasing R early . Gd-DOTA demonstrated similar observations. f fast (+10%; P<0.001) and D fast (+6.6%; P<0.001) increased after Gd-EOB-DTPA, while decreasing after Gd-DOTA (-4.2% and -5.7%, P<0.001) and were confirmed by simulations. For focal nodular hyperplasia lesions (n=5) D fast and f fast increased (P<0.001) with increasing R late , whereas for hepatocellular carcinoma (n=4) and adenoma (n=7) no differences were found., Conclusion: Microperfusion measured by IVIM reflects perfusion in a way resembling CE-MRI. Also IVIM separated intra- and extracellular MR contrast media. This underlines the potential of IVIM in quantitative liver imaging., (Copyright © 2016 Elsevier Inc. All rights reserved.)

It has been demonstrated that hepatic apparent diffusion coefficients (ADC) are decreasing in patients with a Fontan circulation. It remains however unclear whether this is a true decrease of molecular diffusion, or rather reflects decreased microperfusion due to decreased portal blood flow. The purpose of this study was therefore to differentiate diffusion and microperfusion using intravoxel incoherent motion (IVIM) modeled diffusion-weighted imaging (DWI) for different liver segments in patients with a Fontan circulation, compare to a control group, and relate with liver function, chronic hepatic congestion and hepatic disease. For that purpose, livers of 59 consecutively included patients with Fontan circulation (29 men; mean-age, 19.1 years) were examined (Oct 2012─Dec 2013) with 1.5T MRI and DWI (b = 0,50,100,250,500,750,1500,1750 s/mm2). IVIM (Dslow, Dfast, ffast) and ADC were calculated for eight liver segments, compared to a control group (19 volunteers; 10 men; mean-age, 32.9 years), and correlated to follow-up duration, clinical variables, and laboratory measurements associated with liver function. The results demonstrated that microperfusion was reduced (p<0.001) in Fontan livers compared to controls with ─38.1% for Dfast and ─32.6% for ffast. Molecular diffusion (Dslow) was similar between patients and controls, while ADC was significantly lower (─14.3%) in patients (p<0.001). ADC decreased significantly with follow-up duration after Fontan operation (r = ─0.657). Dslow showed significant inverse correlations (r = ─0.591) with follow-up duration whereas Dfast and ffast did not. From these results it was concluded that the decreasing ADC values in Fontan livers compared with controls reflect decreases in hepatic microperfusion rather than any change in molecular diffusion. However, with the time elapsed since the Fontan operation molecular diffusion and ADC decreased while microperfusion remained stable. This indicates that after Fontan operation initial blood flow effects on the liver are followed by intracellular changes preceding the formation of fibrosis and cirrhosis.

Objectives: Magnetic resonance spectroscopy (MRS) of children with or without neurometabolic disease is used for the first time for quantitative assessment of brain tissue lactate signals, to elaborate on previous suggestions of MRS-detected lactate as a marker of mitochondrial disease., Methods: Multivoxel MRS of a transverse plane of brain tissue cranial to the ventricles was performed in 88 children suspected of having neurometabolic disease, divided into 'definite' (n = 17, ≥1 major criteria), 'probable' (n = 10, ≥2 minor criteria), 'possible' (n = 17, 1 minor criterion) and 'unlikely' mitochondrial disease (n = 44, none of the criteria). Lactate levels, expressed in standardized arbitrary units or relative to creatine, were derived from summed signals from all voxels. Ten 'unlikely' children with a normal neurological exam served as the MRS reference subgroup. For 61 of 88 children, CSF lactate values were obtained., Results: MRS lactate level (>12 arbitrary units) and the lactate-to-creatine ratio (L/Cr >0.22) differed significantly between the definite and the unlikely group (p = 0.015 and p = 0.001, respectively). MRS L/Cr also differentiated between the probable and the MRS reference subgroup (p = 0.03). No significant group differences were found for CSF lactate., Conclusion: MRS-quantified brain tissue lactate levels can serve as diagnostic marker for identifying mitochondrial disease in children., Key Points: • MRS-detected brain tissue lactate levels can be quantified. • MRS lactate and lactate/Cr are increased in children with mitochondrial disease. • CSF lactate is less suitable as marker of mitochondrial disease.

Purpose: To assess if specificity can be increased when semiautomated breast lesion analysis of quantitative diffusion-weighted imaging (DWI) is implemented after dynamic contrast-enhanced (DCE-) magnetic resonance imaging (MRI) in the workup of BI-RADS 3 and 4 breast lesions larger than 1 cm., Materials and Methods: In all, 120 consecutive patients (mean-age, 48 years; age range, 23-75 years) with 139 breast lesions (≥1 cm) were examined (2010-2014) with 1.5T DCE-MRI and DWI (b = 0, 50, 200, 500, 800, 1000 s/mm 2 ) and the BI-RADS classification and histopathology were obtained. For each lesion malignancy was excluded using voxelwise semiautomated breast lesion analysis based on previously defined thresholds for the apparent diffusion coefficient (ADC) and the three intravoxel incoherent motion (IVIM) parameters: molecular diffusion (D slow ), microperfusion (D fast ), and the fraction of D fast (f fast ). The sensitivity (Se), specificity (Sp), and negative predictive value (NPV) based on only IVIM parameters combined in parallel (D slow , D fast , and f fast ), or the ADC or the BI-RADS classification by DCE-MRI were compared. Subsequently, the Se, Sp, and NPV of the combination of the BI-RADS classification by DCE-MRI followed by the IVIM parameters in parallel (or the ADC) were compared., Results: In all, 23 of 139 breast lesions were benign. Se and Sp of DCE-MRI was 100% and 30.4% (NPV = 100%). Se and Sp of IVIM parameters in parallel were 92.2% and 52.2% (NPV = 57.1%) and for the ADC 95.7% and 17.4%, respectively (NPV = 44.4%). In all, 26 of 139 lesions were classified as BI-RADS 3 (n = 7) or BI-RADS 4 (n = 19). DCE-MRI combined with ADC (Se = 99.1%, Sp = 34.8%) or IVIM (Se = 99.1%, Sp = 56.5%) did significantly improve (P = 0.016) Sp of DCE-MRI alone for workup of BI-RADS 3 and 4 lesions (NPV = 92.9%)., Conclusion: Quantitative DWI has a lower NPV compared to DCE-MRI for evaluation of breast lesions and may therefore not be able to replace DCE-MRI; when implemented after DCE-MRI as problem solver for BI-RADS 3 and 4 lesions, the combined specificity improves significantly. J. Magn. Reson. Imaging 2016;44:1642-1649., (© 2016 International Society for Magnetic Resonance in Medicine.)

Purpose: To assess whether short tau inversion recovery (STIR) MRI sequences can provide a tool for monitoring peripheral nerve regeneration, by comparing signal intensity changes in reinnervated muscle over time, and to determine potential clinical time points for monitoring., Materials and Methods: For this prospective study, 29 patients with complete traumatic transection of the ulnar or median nerves in the forearm were followed up to 45 months postsurgery. Standardized 1.5 Tesla STIR-MRI scans of hand muscles were obtained at fixed time intervals. Muscle signal intensities were measured semi-quantitatively and correlated to functional outcome., Results: For the patients with good function recovery, mean signal intensity ratios of 1.179 ± 0.039, 1.304 ± 0.180, 1.154 ± 0.121, 1.105 ± 0.046 and 1.038 ± 0.047 were found at 1-, 3-, 6-, 9-, and 12-month follow-up, respectively. In the group with poor function recovery, ratios of 1.240 ± 0.069, 1.374 ± 0.144, 1.407 ± 0.127, 1.386 ± 0.128 and 1.316 ± 0.116 were found. Comparing the groups showed significant differences from 6 months onward (P < 0.001), with normalizing signal intensities in the group with good function recovery and sustained elevated signal intensity in the group with poor function recovery., Conclusion: MRI of muscle can be used as a tool for monitoring motor nerve regeneration, by comparing STIR muscle signal intensities over time. A decrease in signal intensity ratio of 50% (as compared to the initial increase) seems to predict good function recovery. Long-term follow-up shows that STIR MRI can be used for at least 15 months after nerve transection to differentiate between denervated and (re)innervated muscles. J. Magn. Reson. Imaging 2016;44:401-410., (© 2016 Wiley Periodicals, Inc.)

Background: To optimize and validate intravoxel incoherent motion (IVIM) modeled diffusion-weighted imaging (DWI) compared with the apparent diffusion coefficient (ADC) for semi-automated analysis of breast lesions using a multi-reader setup., Materials and Methods: Patients (n = 176) with breast lesions (≥1 cm) and known pathology were prospectively examined (1.5 Tesla) with DWI (b = 0, 50, 200, 500, 800, 1000 s/mm(2) ) between November 2008 and July 2014 and grouped into a training and test set. Three independent readers applied a semi-automated procedure for setting regions-of-interest for each lesion and recorded ADC and IVIM parameters: molecular diffusion (Dslow ), microperfusion (Dfast ), and the fraction of Dfast (ffast ). In the training set (24 lesions, 12 benign), a semi-automated method was optimized to yield maximum true negatives (TN) with minimal false negatives (FN): only the optimal fraction (Fo) of voxels in the lesions was used and optimal thresholds were determined. The optimal Fo and thresholds were then applied to a consecutive test set (139 lesions, 23 benign) to obtain specificity and sensitivity., Results: In the training set, optimal thresholds were 1.44 × 10(-3) mm(2) /s (Dslow ), 18.55 × 10(-3) mm(2) /s (Dfast ), 0.247 (ffast ) and 2.00 × 10(-3) mm(2) /s (ADC) with Fo set to 0.61, 0.85, 1.0, and 1.0, respectively, this resulted in TN = 5 (IVIM) and TN = 1 (ADC), with FN = 0. In the test set, sensitivity and specificity among the readers were 90.5-93.1% and 43.5-52.2%, respectively, for IVIM, and 94.8-95.7% and 13.0-21.7% for ADC (P ≤ 0.0034) without inter-reader differences (P = 1.000)., Conclusion: The presented semi-automated method for breast lesion evaluation is reader independent and yields significantly higher specificity for IVIM compared with the ADC., (© 2015 Wiley Periodicals, Inc.)

Objectives: Reproducibility of myocardial contour determination in cardiac magnetic resonance imaging is important, especially when determining T2* values per myocardial segment as a prognostic factor of heart failure or thalassemia. A method creating a composite image with contrasts optimized for drawing myocardial contours is introduced and compared with the standard method on a single image., Materials and Methods: A total of 36 short-axis slices from bright-blood multigradient echo (MGE) T2* scans of 21 patients were acquired at eight echo times. Four observers drew free-hand myocardial contours on one manually selected T2* image (method 1) and on one image composed by blending three images acquired at TEs providing optimum contrast-to-noise ratio between the myocardium and its surrounding regions (method 2)., Results: Myocardial contouring by method 2 met higher interobserver reproducibility than method 1 (P < 0.001) with smaller Coefficient of variance (CoV) of T2* values in the presence of myocardial iron accumulation (9.79 vs. 15.91%) and in both global myocardial and mid-ventricular septum regions (12.29 vs. 16.88 and 5.76 vs. 8.16%, respectively)., Conclusion: The use of contrast-optimized composite images in MGE data analysis improves reproducibility of myocardial contour determination, leading to increased consistency in the calculated T2* values enhancing the diagnostic impact of this measure of iron overload.

Background: Patients with a Fontan circulation tend to develop liver fibrosis, liver cirrhosis and even hepatocellular carcinoma. The aim of this study is to use the magnetic resonance technique diffusing-weighted imaging (DWI) for detecting liver fibrosis/cirrhosis in Fontan patients and to establish whether DWI results are associated with functional aspects of the Fontan circulation., Methods: In a cross-sectional study, 59 Fontan patients were evaluated by liver DWI. The association between apparent diffusion coefficients (ADC) and patient characteristics, laboratory measurements and functional aspects of the Fontan circulation (NYHA class, maximum oxygen uptake during exercise and cardiac index) was assessed., Results: Liver ADC values were low (0.82×10(-3)±0.11×10(-3) mm2/s) compared with literature values for healthy volunteers and correlated negatively with calculated liver fibrosis/cirrhosis scores (Fib-4 score, p=0.019; AST/ALT ratio, p=0.009) and gamma-glutamyl transferase (p=0.001). Furthermore, ADC values correlated negatively with follow-up duration (p<0.001) and positively with cardiac index (p=0.019). No correlation between ADC values and exercise tests was found. In multivariable analysis, the ADC values were independently correlated with follow-up duration after Fontan completion., Conclusions: The results of the current study suggest that progressive liver damage due to chronic congestion and potential hypoperfusion is reflected in the liver ADC values in Fontan patients. This study highlights that liver damage in the context of the Fontan circulation might be far more common than previously thought, and that the implementation of liver assessment in the routine follow-up of Fontan patients is recommendable., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

Technological advances in magnetic resonance imaging (MRI) and computed tomography (CT), including higher spatial and temporal resolution, have made the prospect of performing absolute myocardial perfusion quantification possible, previously only achievable with positron emission tomography (PET). This could facilitate integration of myocardial perfusion biomarkers into the current workup for coronary artery disease (CAD), as MRI and CT systems are more widely available than PET scanners. Cardiac PET scanning remains expensive and is restricted by the requirement of a nearby cyclotron. Clinical evidence is needed to demonstrate that MRI and CT have similar accuracy for myocardial perfusion quantification as PET. However, lack of standardization of acquisition protocols and tracer kinetic model selection complicates comparison between different studies and modalities. The aim of this overview is to provide insight into the different tracer kinetic models for quantitative myocardial perfusion analysis and to address typical implementation issues in MRI and CT. We compare different models based on their theoretical derivations and present the respective consequences for MRI and CT acquisition parameters, highlighting the interplay between tracer kinetic modeling and acquisition settings.

Objectives: The aim of this study was to assess the intermodel agreement of different magnetic resonance myocardial perfusion models and evaluate their correspondence to stenosis diameter., Materials and Methods: In total, 260 myocardial segments were analyzed from rest and adenosine stress first-pass myocardial perfusion magnetic resonance images (1.5 T, 0.050 ± 0.005 mmol/kg body weight gadolinium; 122 segments in rest, 138 in stress) in 10 patients with suspected or known coronary artery disease. Signal intensity curves were calculated per myocardial segment, of which the contours were traced with QMASS MR V.7.6 (Medis, Leiden, the Netherlands), and exported to Matlab. Myocardial blood flow quantification was performed with distributed parameter, extended Toft, Patlak, and Fermi parametric models (in-house programs; Matlab R2013a; Mathworks Inc, Natick, MA). Modeling was applied after the signal intensity curves were corrected for spatial magnetic field inhomogeneity and contrast saturation. Overall and grouped perfusion values based on presence of coronary stenosis (>50% diameter reduction) at coronary computed tomography angiography at second generation dual-source computed tomography were compared between the perfusion models., Results: Rest and stress myocardial perfusion estimates for all models were significantly related to each other (P < 0.001). The highest correlation coefficients were found between the extended Toft and Fermi models (R = 0.89-0.91) and low correlation coefficients between the distributed parameter and Patlak models (R = 0.66-0.68). The models resulted in significantly different perfusion estimates in stress (P = 0.03), but not in rest (P = 0.74). The differences in perfusion estimates in stress were caused by differences between the distributed parameter and Patlak models and between the Patlak and Fermi models (both P < 0.001). Significantly lower perfusion estimates were found for myocardial segments subtended by coronary arteries with versus without significant stenosis, but only for estimations produced by the extended Toft model (P = 0.04) and Fermi model (P = 0.01). There were no significant differences in rest perfusion values between models., Conclusions: Quantitative myocardial perfusion values in stress depend on the modeling method used to calculate the perfusion estimate. The difference in myocardial perfusion estimate with or without stenosis in the subtending coronary artery is most pronounced when the extended Toft or Fermi model is used.

In phenylketonuria, elevated plasma phenylalanine concentrations may disturb blood-to-brain large neutral amino acid (LNAA) transport and cerebral protein synthesis (CPS). We investigated the associations between these processes, using data obtained by positron emission tomography with l-[1-(11)C]-tyrosine ((11)C-Tyr) as a tracer. Blood-to-brain transport of non-Phe LNAAs was modeled by the rate constant for (11)C-Tyr transport from arterial plasma to brain tissue (K1), while CPS was modeled by the rate constant for (11)C-Tyr incorporation into cerebral protein (k3). Brain phenylalanine concentrations were measured by magnetic resonance spectroscopy in three volumes of interest (VOIs): supraventricular brain tissue (VOI 1), ventricular brain tissue (VOI 2), and fluid-containing ventricular voxels (VOI 3). The associations between k3 and each predictor variable were analyzed by multiple linear regression. The rate constant k3 was inversely associated with brain phenylalanine concentrations in VOIs 2 and 3 (adjusted R(2)=0.826, F=19.936, P=0.021). Since brain phenylalanine concentrations in these VOIs highly correlated with each other, the specific associations of each predictor with k3 could not be determined. The associations between k3 and plasma phenylalanine concentration, K1, and brain phenylalanine concentrations in VOI 1 were nonsignificant. In conclusion, our study shows an inverse association between k3 and increased brain phenylalanine concentrations.

Objectives: To evaluate the effect of the choice of b values and prior use of contrast medium on apparent diffusion coefficients (ADCs) of breast lesions derived from diffusion-weighted imaging (DWI), and on the discrimination between benign and malignant lesions., Methods: A literature search of relevant DWI studies was performed. The accuracy of DWI to characterize lesions by using b value ≤600 s/mm(2) and b value >600 s/mm(2) was presented as pooled sensitivity and specificity, and the ADC was calculated for both groups. Lesions were pooled as pre- or post-contrast DWI., Results: Of 198 articles, 26 met the inclusion criteria. Median ADCs were significantly higher (13.2-35.1 %, p < 0.001) for the group of b values ≤600 s/mm(2) compared to >600 s/mm(2). The sensitivity in both groups was similar (91 % and 89 %, p = 0.495) as well as the specificity (75 % and 84 %, p = 0.237). Contrast medium had no significant effects on the ADCs (p ≥ 0.08). The differentiation between benign and malignant lesions was optimal (58.4 %) for the combination of b = 0 and 1,000 s/mm(2)., Conclusions: The wide variety of b value combinations applied in different studies significantly affects the ADC of breast lesions and confounds quantitative DWI. If only a couple of b values are used, those of b = 0 and 1,000 s/mm(2) are recommended for the best improvement of differentiating between benign and malignant lesions., Key Points: • The choice of b values significantly affects the ADC of breast lesions. • Sensitivity and specificity are not affected by the choice of b values. • b values 0 and 1,000 s/mm (2) are recommended for optimal differentiation between benign and malignant lesions. • Contrast medium prior to DWI does not significantly affect the ADC.

Diffusion-weighted imaging (DWI) is an important diagnostic tool in the assessment of focal liver lesions and diffuse liver diseases such as cirrhosis and fibrosis. Quantitative DWI parameters such as molecular diffusion, microperfusion and their fractions, are known to be affected when hepatic fat fractions (HFF) are higher than 5.5% (steatosis). However, less is known about the effect on DWI for HFF in the normal non-steatotic range below 5.5%, which can be found in a large part of the population. The aim of this study was therefore to evaluate the diagnostic implications of non-steatotic HFF on quantitative DWI parameters in eight liver segments. For this purpose, eleven healthy volunteers (2 men, mean-age 31.0) were prospectively examined with DWI and three series of in-/out-of-phase dual-echo spoiled gradient-recalled MRI sequences to obtain the HFF and T2*. DWI data were analyzed using the intravoxel incoherent motion (IVIM) model. Four circular regions (ø22.3 mm) were drawn in each of eight liver segments and averaged. Measurements were divided in group 1 (HFF ≤ 2.75%), group 2 (2.75< HFF ≤ 5.5%) and group 3 (HFF>5.5%). DWI parameters and T2* were compared between the three groups and between the segments. It was observed that the molecular diffusion (0.85, 0.72 and 0.49 × 10(-3) mm(2)/s) and T2* (32.2, 27.2 and 21.0 ms) differed significantly between the three groups of increasing HFF (2.18, 3.50 and 19.91%). Microperfusion and its fraction remained similar for different HFF. Correlations with HFF were observed for the molecular diffusion (r = -0.514, p<0.001) and T2* (-0.714, p<0.001). Similar results were obtained for the majority of individual liver segments. It was concluded that fat significantly decreases molecular diffusion in the liver, also in absence of steatosis (HFF ≤ 5.5%). Also, it was confirmed that fat influences T2*. Determination of HFF prior to quantitative DWI is therefore crucial.

Purpose: To assess the repeatability and reproducibility of semiquantitative magnetic resonance (MR) perfusion analysis performed by using different software packages., Materials and Methods: The study protocol was approved by the institutional ethics committee. Informed consent was obtained from each patient. Semiquantitative perfusion analysis was performed twice by two independent observers using four dedicated software packages. MR perfusion datasets originated from eight patients with known single-vessel disease who were scheduled for percutaneous coronary intervention (PCI) on the basis of coronary angiography findings. Each patient underwent two examinations: 1 day before and 1 day after PCI. Repeatability (intra- and interobserver agreements) and reproducibility (intersoftware agreement) were evaluated for perfusion upslope and myocardial perfusion reserve index with Student t test and Bland-Altman analyses., Results: Intra- and interobserver agreements were good and comparable for repeated measurements within each individual software platform (mean differences < 6%, intraclass correlation coefficient [ICC] ≥ 0.68). However, the intersoftware variability was significant (limits of agreement ≥ 65%, ICC ≤ 0.67) such that the values produced with the different software packages are not interchangeable., Conclusion: The results indicate high repeatability within individual software but low reproducibility between different software packages, suggesting that within-group and/or sequential observation of semiquantitative perfusion parameters must be performed with the same software platform. Before semiquantitative perfusion analysis can be incorporated reliably into clinical studies, it is important to resolve the differences between the software packages.

Objectives: To investigate to what extent cardiac MRI derived measurements of right ventricular (RV) volumes using the left ventricular (LV) end-systolic and end-diastolic frame misrepresent RV end-systolic and end-diastolic volumes in patients with tetralogy of Fallot (ToF) and a right bundle branch block., Methods: Sixty-five cardiac MRI scans of patients with ToF and a right bundle branch block, and 50 cardiac MRI scans of control subjects were analyzed. RV volumes and function using the end-systolic and end-diastolic frame of the RV were compared to using the end-systolic and end-diastolic frame of the LV., Results: Timing of the RV end-systolic frame was delayed compared to the LV end-systolic frame in 94% of patients with ToF and in 50% of control subjects. RV end-systolic volume using the RV end-systolic instead of LV end-systolic frame was smaller in ToF (median -3.3 ml/m(2), interquartile range -1.9 to -5.6 ml/m(2); p<0.001) and close to unchanged in control subjects. Using the RV instead of LV end-systolic and end-diastolic frame hardly affected RV end-diastolic volumes in both groups and ejection fraction in control subjects (54±4%, both methods), while increasing ejection fraction from 45±7% to 48±7% for patients with ToF (p<0.001). QRS duration correlated positively with the changes in the RV end-systolic volume (p<0.001) and RV ejection fraction obtained in ToF patients when using the RV instead of the LV end-systolic and end-diastolic frame (p = 0.004)., Conclusion: For clinical decision making in ToF patients RV volumes derived from cardiac MRI should be measured in the end-systolic frame of the RV instead of the LV.