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Objectives: To determine the influence of iterative reconstruction (IR) on quantitative computed tomography (CT) measurements of emphysema, air trapping, and airway wall and lumen dimensions, compared to filtered back-projection (FBP).Methods: Inspiratory and expiratory chest CTs of 75 patients (37 male, 38 female; mean age 64.0 ± 5.7 years) were reconstructed using FBP and IR. CT emphysema, CT air trapping and airway dimensions of a segmental bronchus were quantified using several commonly used quantification methods. The two algorithms were compared using the concordance correlation coefficient (p (c)) and Wilcoxon signed rank test.Results: Only the E/I-ratio(MLD) as a measure of CT air trapping and airway dimensions showed no significant differences between the algorithms, whereas all CT emphysema and the other CT air trapping measures were significantly different at IR when compared to FBP (P < 0.001).Conclusion: The evaluated IR algorithm significantly influences quantitative CT measures in the assessment of emphysema and air trapping. However, the E/I-ratio(MLD) as a measure of CT air trapping, as well as the airway measurements, is unaffected by this reconstruction method. Quantitative CT of the lungs should be performed with careful attention to the CT protocol, especially when iterative reconstruction is introduced.Key Points: • New techniques in CT allow numerous quantitative measurements of lung function. • Iterative reconstruction influences quantitative CT measurements of emphysema and air trapping. • Expiratory-to-inspiratory ratio of mean lung density and airway measurements are unaffected by iterative reconstruction. • Quantitative lung-CT should be performed with careful attention to the CT protocol. [ABSTRACT FROM AUTHOR]

STUDY DESIGN.: A prospective single-arm trial. OBJECTIVE.: To investigate whether dynamic isolated resistance training of global lumbar extensor muscles leads to changes in lumbar multifidus (LM) morphology in terms of cross-sectional muscle, and, if so, whether these changes are associated with observed changes in self-experienced functional status of chronic nonspecific low back pain (CNSLBP). SUMMARY OF BACKGROUND DATA.: LM morphology is associated with the recurrence of CNSLBP. METHODS.: Sixteen male patients underwent a dynamic isolated resistance-training program for the lower back muscles of approximately 10 sessions in 12 weeks. In the next 12 weeks, frequency of training was tailored to the patients' need. Participants underwent lumbar magnetic resonance imaging at baseline (T0), after 12 weeks (T12), and after 24 weeks (T24). Functional cross-sectional area was obtained by analyzing the magnetic resonance images. Functional status was assessed using the patient-specific functional scale, Roland-Morris disability questionnaire, and global perceived effect scale. RESULTS.: Roland-Morris disability questionnaire and patient-specific functional scale scores showed significant and clinically relevant improvements between baseline and T12, with 44% and 39%, respectively. Between T12 and T24, these scores did not change significantly. Seven participants (44%) reported clinically relevant improvements in global perceived effect at T12. At T24, 1 more participant reported a relevant global perceived effect improvement, whereas 2 participants (13%) reported worsening of their condition. The magnetic resonance imaging analysis showed minor nonsignificant changes in functional cross-sectional area. CONCLUSION.: Our study shows that 10 weeks of dynamic isolated training of the lumbar extensors, once a week, leads to clinically relevant improvements in functional status of men with CNSLBP, without accompanying improvements in functional cross-sectional area of LM. These findings suggest that improvement in LM morphology is not a critical success factor in restoring functional status of patients with CNSLBP, at least in the short term (6 mo). [ABSTRACT FROM AUTHOR]

Background: The aim of our current systematic dynamic phantom study was first, to optimize reconstruction parameters of coronary CTA (CCTA) acquired on photon counting CT (PCCT) for coronary artery calcium (CAC) scoring, and second, to assess the feasibility of calculating CAC scores from CCTA, in comparison to reference calcium scoring CT (CSCT) scans., Methods: In this phantom study, an artificial coronary artery was translated at velocities corresponding to 0, < 60, and 60-75 beats per minute (bpm) within an anthropomorphic phantom. The density of calcifications was 100 (very low), 200 (low), 400 (medium), and 800 (high) mgHA/cm 3 , respectively. CCTA was reconstructed with the following parameters: virtual non-iodine (VNI), with and without iterative reconstruction (QIR level 2, QIR off, respectively); kernels Qr36 and Qr44f; slice thickness/increment 3.0/1.5 mm and 0.4/0.2 mm. The agreement in risk group classification between CAC CCTA and CAC CSCT scoring was measured using Cohen weighted linear κ with 95% CI., Results: For CCTA reconstructed with 0.4 mm slice thickness, calcium detectability was perfect (100%). At < 60 bpm, CAC CCTA of low, and medium density calcification was underestimated by 53%, and 15%, respectively. However, CAC CCTA was not significantly different from CAC CSCT of very low, and high-density calcifications. The best risk agreement was achieved when CCTA was reconstructed with QIR off, Qr44f, and 0.4 mm slice thickness (κ = 0.762, 95% CI 0.671-0.853)., Conclusion: In this dynamic phantom study, the detection of calcifications with different densities was excellent with CCTA on PCCT using thin-slice VNI reconstruction. Agatston scores were underestimated compared to CSCT but agreement in risk classification was substantial., Clinical Relevance Statement: Photon counting CT may enable the implementation of coronary artery calcium scoring from coronary CTA in daily clinical practice., Key Points: Photon-counting CTA allows for excellent detectability of low-density calcifications at all heart rates. Coronary artery calcium scoring from coronary CTA acquired on photon counting CT is feasible, although improvement is needed. Adoption of the standard acquisition and reconstruction protocol for calcium scoring is needed for improved quantification of coronary artery calcium to fully employ the potential of photon counting CT., (© 2024. The Author(s).)

Objective: To evaluate the effect of volumetric analysis on the diagnosis and management of indeterminate solid pulmonary nodules in routine clinical practice., Methods: This was a retrospective study with 107 computed tomography (CT) cases of solid pulmonary nodules (range, 6-15 mm), 57 pathology-proven malignancies (lung cancer, n = 34; metastasis, n = 23), and 50 benign nodules. Nodules were evaluated on a total of 309 CT scans (average number of CTs/nodule, 2.9 [range, 2-7]). CT scans were from multiple institutions with variable technique. Nine radiologists (attendings, n = 3; fellows, n = 3; residents, n = 3) were asked their level of suspicion for malignancy (low/moderate or high) and management recommendation (no follow-up, CT follow-up, or care escalation) for baseline and follow-up studies first without and then with volumetric analysis data. Effect of volumetry on diagnosis and management was assessed by generalized linear and logistic regression models., Results: Volumetric analysis improved sensitivity ( P = 0.009) and allowed earlier recognition ( P < 0.05) of malignant nodules. Attending radiologists showed higher sensitivity in recognition of malignant nodules ( P = 0.03) and recommendation of care escalation ( P < 0.001) compared with trainees. Volumetric analysis altered management of high suspicion nodules only in the fellow group ( P = 0.008). κ Statistics for suspicion for malignancy and recommended management were fair to substantial (0.38-0.66) and fair to moderate (0.33-0.50). Volumetric analysis improved interobserver variability for identification of nodule malignancy from 0.52 to 0.66 ( P = 0.004) only on the second follow-up study., Conclusions: Volumetric analysis of indeterminate solid pulmonary nodules in routine clinical practice can result in improved sensitivity and earlier identification of malignant nodules. The effect of volumetric analysis on management recommendations is variable and influenced by reader experience., Competing Interests: The authors declare no conflict of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Artificial intelligence (AI) models for medical imaging tasks, such as classification or segmentation, require large and diverse datasets of images. However, due to privacy and ethical issues, as well as data sharing infrastructure barriers, these datasets are scarce and difficult to assemble. Synthetic medical imaging data generated by AI from existing data could address this challenge by augmenting and anonymizing real imaging data. In addition, synthetic data enable new applications, including modality translation, contrast synthesis, and professional training for radiologists. However, the use of synthetic data also poses technical and ethical challenges. These challenges include ensuring the realism and diversity of the synthesized images while keeping data unidentifiable, evaluating the performance and generalizability of models trained on synthetic data, and high computational costs. Since existing regulations are not sufficient to guarantee the safe and ethical use of synthetic images, it becomes evident that updated laws and more rigorous oversight are needed. Regulatory bodies, physicians, and AI developers should collaborate to develop, maintain, and continually refine best practices for synthetic data. This review aims to provide an overview of the current knowledge of synthetic data in medical imaging and highlights current key challenges in the field to guide future research and development., (© RSNA, 2024.)

Coronary CT angiography is increasingly used for cardiac diagnosis. Dose modulation techniques can reduce radiation dose, but resulting functional images are noisy and challenging for functional analysis. This retrospective study describes and evaluates a deep learning method for denoising functional cardiac imaging, taking advantage of multiphase information in a three-dimensional convolutional neural network. Coronary CT angiograms ( n = 566) were used to derive synthetic data for training. Deep learning-based image denoising was compared with unprocessed images and a standard noise reduction algorithm (block-matching and three-dimensional filtering [BM3D]). Noise and signal-to-noise ratio measurements, as well as expert evaluation of image quality, were performed. To validate the use of the denoised images for cardiac quantification, threshold-based segmentation was performed, and results were compared with manual measurements on unprocessed images. Deep learning-based denoised images showed significantly improved noise compared with standard denoising-based images (SD of left ventricular blood pool, 20.3 HU ± 42.5 [SD] vs 33.4 HU ± 39.8 for deep learning-based image denoising vs BM3D; P < .0001). Expert evaluations of image quality were significantly higher in deep learning-based denoised images compared with standard denoising. Semiautomatic left ventricular size measurements on deep learning-based denoised images showed excellent correlation with expert quantification on unprocessed images (intraclass correlation coefficient, 0.97). Deep learning-based denoising using a three-dimensional approach resulted in excellent denoising performance and facilitated valid automatic processing of cardiac functional imaging. Keywords: Cardiac CT Angiography, Deep Learning, Image Denoising Supplemental material is available for this article. © RSNA, 2024.

Rationale and Objectives: Post-TAVR persistent pulmonary hypertension (PH) is a better predictor of poor outcome than pre-TAVR PH. In this longitudinal study we sought to evaluate whether pulmonary artery (distensibility (D PA ) measured on preprocedural ECG-gated CTA is associated with persistent-PH and 2-year mortality after TAVR., Materials and Methods: Three hundred and thirty-six patients undergoing TAVR between July 2012 and March 2016 were retrospectively included and followed for all-cause mortality until November 2017. All patients underwent retrospectively ECG-gated CTA prior to TAVR. Main pulmonary artery (MPA) area was measured in systole and in diastole. D PA was calculated as: [(area-MPA max -area-MPA min )/area-MPA max ]%. ROC analysis was performed to assess the AUC for persistent-PH. Youden Index was used to determine the optimal threshold of D PA for persistent-PH. Two groups were compared based on a D PA threshold of 8% (specificity of 70% for persistent-PH). Kaplan-Meier, Cox proportional-hazard, and logistic regression analyses were performed. The primary clinical endpoint was defined as persistent-PH post-TAVR. The secondary endpoint was defined as all-cause mortality 2 years after TAVR., Results: Median follow-up time was 413 (interquartiles 339-757) days. A total of 183 (54%) had persistent-PH and 68 (20%) patients died within 2-years after TAVR. Patients with D PA <8% had significantly more persistent-PH (67% vs 47%, p<0.001) and 2-year deaths (28% vs 15%, p=0.006), compared to patients with D PA >8%. Adjusted multivariable regression analyses showed that D PA <8% was independently associated with persistent-PH (OR 2.10 [95%-CI 1.3-4.5], p=0.007) and 2-year mortality (HR 2.91 [95%-CI 1.5-5.8], p=0.002). Kaplan-Meier analysis showed that 2-year mortality of patients with D PA <8% was significantly higher compared to patients with D PA ≥8% (mortality 28% vs 15%; log-rank p=0.003)., Conclusion: D PA on preprocedural CTA is independently associated with persistent-PH and two-year mortality in patients who undergo TAVR., Competing Interests: Declaration of Competing Interest None., (Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Background: Substantial variation in Agatston scores (AS) acquired with different computed tomography (CT) scanners may influence patient risk classification., Objectives: This study sought to develop a calibration tool for state-of-the-art CT systems resulting in vendor-neutral AS (vnAS), and to assess the impact of vnAS on coronary heart disease (CHD) event prediction., Methods: The vnAS calibration tool was derived by imaging 2 anthropomorphic calcium containing phantoms on 7 different CT and 1 electron beam tomography system, which was used as the reference system. The effect of vnAS on CHD event prediction was analyzed with data from 3,181 participants from MESA (Multi-Ethnic Study on Atherosclerosis). Chi-square analysis was used to compare CHD event rates between low (vnAS <100) and high calcium groups (vnAS ≥100). Multivariable Cox proportional hazard regression models were used to assess the incremental value of vnAS., Results: For all CT systems, a strong correlation with electron beam tomography-AS was found (R 2 >0.932). Of the MESA participants originally in the low calcium group (n = 781), 85 (11%) participants were reclassified to a higher risk category based on the recalculated vnAS. For reclassified participants, the CHD event rate of 15% was significantly higher compared with participants in the low calcium group (7%; P = 0.008) with a CHD HR of 3.39 (95% CI: 1.82-6.35; P = 0.001)., Conclusions: The authors developed a calibration tool that enables calculation of a vnAS. MESA participants who were reclassified to a higher calcium category by means of the vnAS experienced more CHD events, indicating improved risk categorization., Competing Interests: Funding Support and Author Disclosures MESA was supported by R01 HL146666 and contracts 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, and N01-HC-95169 from the National Heart, Lung, and Blood Institute, and by grants UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420 from the National Center for Advancing Translational Sciences (NCATS). Dr van der Werf is currently employed by Philips; the work as reported in this study was performed as employee of Utrecht University Medical Center and of Erasmus Medical Center. Dr Budoff has received financial support from General Electric and from the National Institutes of Health. Dr Willemink has received a research grant from the American Heart Association and is employed by Segmed, Inc. Dr Leiner has received a research grant from the Netherlands Heart Foundation and the Dutch Research Council. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2023 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Surveillance imaging of patients with chronic aortic diseases, such as aneurysms and dissections, relies on obtaining and comparing cross-sectional diameter measurements along the aorta at predefined aortic landmarks, over time. The orientation of the cross-sectional measuring planes at each landmark is currently defined manually by highly trained operators. Centerline-based approaches are unreliable in patients with chronic aortic dissection, because of the asymmetric flow channels, differences in contrast opacification, and presence of mural thrombus, making centerline computations or measurements difficult to generate and reproduce. In this work, we present three alternative approaches - INS, MCDS, MCDbS - based on convolutional neural networks and uncertainty quantification methods to predict the orientation (ϕ,θ) of such cross-sectional planes. For the monitoring of chronic aortic dissections, we show how a dataset of 162 CTA volumes with overall 3273 imperfect manual annotations routinely collected in a clinic can be efficiently used to accomplish this task, despite the presence of non-negligible interoperator variabilities in terms of mean absolute error (MAE) and 95% limits of agreement (LOA). We show how, despite the large limits of agreement in the training data, the trained model provides faster and more reproducible results than either an expert user or a centerline method. The remaining disagreement lies within the variability produced by three independent expert annotators and matches the current state of the art, providing a similar error, but in a fraction of the time., Competing Interests: Declaration of competing interest The authors have no conflicts of interest to declare., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Objective: Analysis of textural features of pulmonary nodules in chest CT, also known as radiomics, has several potential clinical applications, such as diagnosis, prognostication, and treatment response monitoring. For clinical use, it is essential that these features provide robust measurements. Studies with phantoms and simulated lower dose levels have demonstrated that radiomic features can vary with different radiation dose levels. This study presents an in vivo stability analysis of radiomic features for pulmonary nodules against varying radiation dose levels., Methods: Nineteen patients with a total of thirty-five pulmonary nodules underwent four chest CT scans at different radiation dose levels (60, 33, 24, and 15 mAs) in a single session. The nodules were manually delineated. To assess the robustness of features, we calculated the intra-class correlation coefficient (ICC). To visualize the effect of milliampere-second variation on groups of features, a linear model was fitted to each feature. We calculated bias and calculated the R 2 value as a measure of goodness of fit., Results: A small minority of 15/100 (15%) radiomic features were considered stable (ICC > 0.9). Bias increased and R 2  decreased at lower dose, but shape features seemed to be more robust to milliampere-second variations than other feature classes., Conclusion: A large majority of pulmonary nodule radiomic features were not inherently robust to radiation dose level variations. For a subset of features, it was possible to correct this variability by a simple linear model. However, the correction became increasingly less accurate at lower radiation dose levels., Clinical Relevance Statement: Radiomic features provide a quantitative description of a tumor based on medical imaging such as computed tomography (CT). These features are potentially useful in several clinical tasks such as diagnosis, prognosis prediction, treatment effect monitoring, and treatment effect estimation., Key Points: • The vast majority of commonly used radiomic features are strongly influenced by variations in radiation dose level. • A small minority of radiomic features, notably the shape feature class, are robust against dose-level variations according to ICC calculations. • A large subset of radiomic features can be corrected by a linear model taking into account only the radiation dose level., (© 2023. The Author(s).)

Abstract: Noninvasive cardiac imaging has rapidly evolved during the last decade owing to improvements in computed tomography (CT)-based technologies, among which we highlight the recent introduction of the first clinical photon-counting detector CT (PCD-CT) system. Multiple advantages of PCD-CT have been demonstrated, including increased spatial resolution, decreased electronic noise, and reduced radiation exposure, which may further improve diagnostics and may potentially impact existing management pathways. The benefits that can be obtained from the initial experiences with PCD-CT are promising. The implementation of this technology in cardiovascular imaging allows for the quantification of coronary calcium, myocardial extracellular volume, myocardial radiomics features, epicardial and pericoronary adipose tissue, and the qualitative assessment of coronary plaques and stents. This review aims to discuss these major applications of PCD-CT with a focus on cardiac and myocardial characterization., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Background: On the basis of the hypothesis that virtual noniodine (VNI)-based coronary artery calcium scoring (CACS) is feasible at reduced radiation doses, this study assesses the impact of radiation dose reduction on the accuracy of this VNI algorithm on a photon-counting detector (PCD)-CT. Methods: In a systematic in vitro setting, a phantom for CACS simulating three chest sizes was scanned on a clinical PCD-CT. The standard radiation dose was chosen at volumetric CT dose indices (CTDI Vol ) of 1.5, 3.3, 7.0 mGy for small, medium-sized, and large phantoms, and was gradually reduced by adjusting the tube current resulting in 100, 75, 50, and 25%, respectively. VNI images were reconstructed at 55 keV, quantum iterative reconstruction (QIR)1, and at 60 keV/QIR4, and evaluated regarding image quality (image noise (IN), contrast-to-noise ratio (CNR)), and CACS. All VNI results were compared to true noncontrast (TNC)-based CACS at 70 keV and standard radiation dose (reference). Results: IN TNC was significantly higher than IN VNI , and IN VNI at 55 keV/QIR1 higher than at 60 keV/QIR4 (100% dose: 16.7 ± 1.9 vs. 12.8 ± 1.7 vs. 7.7 ± 0.9; p < 0.001 for every radiation dose). CNR TNC was higher than CNR VNI , but it was better to use 60 keV/QIR4 ( p < 0.001). CACS VNI showed strong correlation and agreement at every radiation dose ( p < 0.001, r > 0.9, intraclass correlation coefficient > 0.9). The coefficients of the variation in root-mean squared error were less than 10% and thus clinically nonrelevant for the CACS VNI of every radiation dose. Conclusion: This phantom study suggests that CACS VNI is feasible on PCD-CT, even at reduced radiation dose while maintaining image quality and CACS accuracy.

Filtered back projection (FBP) has been the standard CT image reconstruction method for 4 decades. A simple, fast, and reliable technique, FBP has delivered high-quality images in several clinical applications. However, with faster and more advanced CT scanners, FBP has become increasingly obsolete. Higher image noise and more artifacts are especially noticeable in lower-dose CT imaging using FBP. This performance gap was partly addressed by model-based iterative reconstruction (MBIR). Yet, its "plastic" image appearance and long reconstruction times have limited widespread application. Hybrid iterative reconstruction partially addressed these limitations by blending FBP with MBIR and is currently the state-of-the-art reconstruction technique. In the past 5 years, deep learning reconstruction (DLR) techniques have become increasingly popular. DLR uses artificial intelligence to reconstruct high-quality images from lower-dose CT faster than MBIR. However, the performance of DLR algorithms relies on the quality of data used for model training. Higher-quality training data will become available with photon-counting CT scanners. At the same time, spectral data would greatly benefit from the computational abilities of DLR. This review presents an overview of the principles, technical approaches, and clinical applications of DLR, including metal artifact reduction algorithms. In addition, emerging applications and prospects are discussed., (© RSNA, 2023.)

Objectives: Establishing the reproducibility of expert-derived measurements on CTA exams of aortic dissection is clinically important and paramount for ground-truth determination for machine learning., Methods: Four independent observers retrospectively evaluated CTA exams of 72 patients with uncomplicated Stanford type B aortic dissection and assessed the reproducibility of a recently proposed combination of four morphologic risk predictors (maximum aortic diameter, false lumen circumferential angle, false lumen outflow, and intercostal arteries). For the first inter-observer variability assessment, 47 CTA scans from one aortic center were evaluated by expert-observer 1 in an unconstrained clinical assessment without a standardized workflow and compared to a composite of three expert-observers (observers 2-4) using a standardized workflow. A second inter-observer variability assessment on 30 out of the 47 CTA scans compared observers 3 and 4 with a constrained, standardized workflow. A third inter-observer variability assessment was done after specialized training and tested between observers 3 and 4 in an external population of 25 CTA scans. Inter-observer agreement was assessed with intraclass correlation coefficients (ICCs) and Bland-Altman plots., Results: Pre-training ICCs of the four morphologic features ranged from 0.04 (-0.05 to 0.13) to 0.68 (0.49-0.81) between observer 1 and observers 2-4 and from 0.50 (0.32-0.69) to 0.89 (0.78-0.95) between observers 3 and 4. ICCs improved after training ranging from 0.69 (0.52-0.87) to 0.97 (0.94-0.99), and Bland-Altman analysis showed decreased bias and limits of agreement., Conclusions: Manual morphologic feature measurements on CTA images can be optimized resulting in improved inter-observer reliability. This is essential for robust ground-truth determination for machine learning models., Key Points: • Clinical fashion manual measurements of aortic CTA imaging features showed poor inter-observer reproducibility. • A standardized workflow with standardized training resulted in substantial improvements with excellent inter-observer reproducibility. • Robust ground truth labels obtained manually with excellent inter-observer reproducibility are key to develop reliable machine learning models., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

Purpose: To compare coronary artery calcium volume and score (CACS) between photon-counting detector (PCD) and conventional energy integrating detector (EID) computed tomography (CT) in a phantom and prospective patient study., Methods: A commercially available CACS phantom was scanned with a standard CACS protocol (120 kVp, slice thickness/increment 3/1.5 mm, and a quantitative Qr36 kernel), with filtered back projection on the EID-CT, and with monoenergetic reconstruction at 70 keV and quantum iterative reconstruction off on the PCD-CT. The same settings were used to prospectively acquire data in patients ( n = 23, 65 ± 12.1 years), who underwent PCD- and EID-CT scans with a median of 5.5 (3.0-12.5) days between the two scans in the period from August 2021 to March 2022. CACS was quantified using a commercially available software solution. A regression formula was obtained from the aforementioned comparison and applied to simulate risk reclassification in a pre-existing cohort of 514 patients who underwent a cardiac EID-CT between January and December 2021., Results: Based on the phantom experiment, CACS PCD-CT showed a more accurate measurement of the reference CAC volumes (overestimation of physical volumes: PCD-CT 66.1 ± 1.6% vs. EID-CT: 77.2 ± 0.5%). CACS EID-CT and CACS PCD-CT were strongly correlated, however, the latter measured significantly lower values in the phantom (CACS PCD-CT : 60.5 (30.2-170.3) vs CACS EID-CT 74.7 (34.6-180.8), p = 0.0015, r = 0.99, mean bias -9.7, Limits of Agreement (LoA) -36.6/17.3) and in patients (non-significant) (CACS PCD-CT : 174.3 (11.1-872.7) vs CACS EID-CT 218.2 (18.5-876.4), p = 0.10, r = 0.94, mean bias -41.1, LoA -315.3/232.5). The systematic lower measurements of Agatston score on PCD-CT system led to reclassification of 5.25% of our simulated patient cohort to a lower classification class., Conclusion: CACS PCD-CT is feasible and correlates strongly with CACS EID-CT , however, leads to lower CACS values. PCD-CT may provide results that are more accurate for CACS than EID-CT., Competing Interests: UJS received institutional research support and/or personal fees from Bayer, Bracco, Elucid Bioimaging, Guerbet, HeartFlow, Keya Medical, and Siemens. AV-S received institutional research support and/or personal fees from Elucid Bioimaging and Siemens. MJW was a stockholder, co-founder, and employee at Segmed and receives a personal research grant from the American Heart Association. TE received a speaker fee and travel support from Siemens Medical Solutions USA, Inc. JO’D was an employee of Siemens Medical Solutions USA, Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Wolf, Halfmann, Schoepf, Zsarnoczay, Fink, Griffith, Aquino, Willemink, O’Doherty, Hell, Suranyi, Kabakus, Baruah, Varga-Szemes and Emrich.)

In computed tomography, coronary artery calcium (CAC) scores are influenced by image reconstruction. The effect of a newly introduced deep learning-based reconstruction (DLR) on CAC scoring in relation to other algorithms is unknown. The aim of this study was to evaluate the effect of four generations of image reconstruction techniques (filtered back projection (FBP), hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), and DLR) on CAC detectability, quantification, and risk classification. First, CAC detectability was assessed with a dedicated static phantom containing 100 small calcifications varying in size and density. Second, CAC quantification was assessed with a dynamic coronary phantom with velocities equivalent to heart rates of 60-75 bpm. Both phantoms were scanned and reconstructed with four techniques. Last, scans of fifty patients were included and the Agatston calcium score was calculated for all four reconstruction techniques. FBP was used as a reference. In the phantom studies, all reconstruction techniques resulted in less detected small calcifications, up to 22%. No clinically relevant quantification changes occurred with different reconstruction techniques (less than 10%). In the patient study, the cardiovascular risk classification resulted, for all reconstruction techniques, in excellent agreement with the reference (κ = 0.96-0.97). However, MBIR resulted in significantly higher Agatston scores (61 (5.5-435.0) vs. 81.5 (9.25-435.0); p < 0.001) and 6% reclassification rate. In conclusion, HIR and DLR reconstructed scans resulted in similar Agatston scores with excellent agreement and low-risk reclassification rate compared with routine reconstructed scans (FBP). However, caution should be taken with low Agatston scores, as based on phantom study, detectability of small calcifications varies with the used reconstruction algorithm, especially with MBIR and DLR., (© 2022. The Author(s).)

Purpose: To describe the design and methodological approach of a multicenter, retrospective study to externally validate a clinical and imaging-based model for predicting the risk of late adverse events in patients with initially uncomplicated type B aortic dissection (uTBAD)., Materials and Methods: The Registry of Aortic Diseases to Model Adverse Events and Progression (ROADMAP) is a collaboration between 10 academic aortic centers in North America and Europe. Two centers have previously developed and internally validated a recently developed risk prediction model. Clinical and imaging data from eight ROADMAP centers will be used for external validation. Patients with uTBAD who survived the initial hospitalization between January 1, 2001, and December 31, 2013, with follow-up until 2020, will be retrospectively identified. Clinical and imaging data from the index hospitalization and all follow-up encounters will be collected at each center and transferred to the coordinating center for analysis. Baseline and follow-up CT scans will be evaluated by cardiovascular imaging experts using a standardized technique., Results: The primary end point is the occurrence of late adverse events, defined as aneurysm formation (≥6 cm), rapid expansion of the aorta (≥1 cm/y), fatal or nonfatal aortic rupture, new refractory pain, uncontrollable hypertension, and organ or limb malperfusion. The previously derived multivariable model will be externally validated by using Cox proportional hazards regression modeling., Conclusion: This study will show whether a recent clinical and imaging-based risk prediction model for patients with uTBAD can be generalized to a larger population, which is an important step toward individualized risk stratification and therapy. Keywords: CT Angiography, Vascular, Aorta, Dissection, Outcomes Analysis, Aortic Dissection, MRI, TEVAR© RSNA, 2022See also the commentary by Rajiah in this issue., Competing Interests: Disclosures of conflicts of interest: D.M. Research grant from the National Institute of Biomedical Imaging and Bioengineering (no. 5T32EB009035); consulting fees from Segmed; stock or stock options in Segmed; member of Radiology: Cardiothoracic Imaging trainee editorial board. M.J.W. Postdoctoral Fellowship Award (no. 18POST34030192) from the American Heart Association, payments to author’s institution; consulting fees from Segmed; payment from GLG, AlphaInsight, and Guidepoint for expert testimony; leadership or fiduciary role in the Society of Cardiovascular Computed Tomography, unpaid; stock or stock options in Segmed. V.L.T. Shareholder of Segmed stock or stock options. V.H. No relevant relationships. M.C. Postdoctoral Fellowship Award (no. 826389) from the American Heart Association; payment or honoraria from FASTeR as lecturer for research methodology course; owner of stock options in Arterys; employee of Arterys. K.H. Payment or honoraria from Sanofi Genzyme and Amicus for lectures, presentations, speakers bureaus, manuscript writing, or educational events; participation on a Data Safety Monitoring Board or Advisory Board for Sanofi Genzyme; associate editor for Radiology: Cardiothoracic Imaging. M.O. No relevant relationships. D.O.T. No relevant relationships. R.O.A. Consultant for Medtronic and EndoRon; member of the Society for Vascular Surgery (SVS) Diversity Equity and Inclusion Committee and council member of the SVS Young Surgeon Section; shareholder for EndoRon and Voythus. S.H. No relevant relationships. N.S.B. Radiological Society of North America Research Scholar Grant (no. RSCH1801); entitled to royalties related to licensure of intellectual property to Imbio; patents planned, issued, or pending for U.S. patent number 10,896,507, Techniques of Deformation Analysis for Quantification of Vascular Enlargement in Aneurysmal Disease. B.Y. Honoraria from seminar hosted by Medtronic. J.M.L. Honoraria from Cardiovascular Institute of Philadelphia. T.G.G. No relevant relationships. D.P. No relevant relationships. G.F. No relevant relationships. L.L. Participation on the Medtronic Thoracic Hostile Neck Club Advisory Board, Barcelona, December 20, 2021. R.H. No relevant relationships. H.A. No relevant relationships. A.E.S. Member of Radiology: Cardiothoracic Imaging editorial board. E.C. No relevant relationships. S.M.J.v.K. No relevant relationships. G.W.H.S. No relevant relationships. A.M.S. No relevant relationships. K.B. No relevant relationships. D.C.M. No relevant relationships. M.P.F. No relevant relationships. D.F. Deputy editor for Radiology: Cardiothoracic Imaging., (© 2022 by the Radiological Society of North America, Inc.)

Limited aortic intimal tear is an uncommon lesion of the dissection spectrum. The lesion has several imaging features that are not well known, including asymmetric aortic contour abnormalities, filling defects, and various morphologic patterns, such as linear, L-shaped, T-shaped, and stellate configurations. Hemorrhage of the aortic wall may also be present in patients with this rare entity. This imaging essay reviews the CT imaging findings and clinical characteristics of patients with limited intimal tears. Keywords: Aorta, CT © RSNA, 2022., Competing Interests: Disclosures of conflicts of interest: M.H.M No relevant relationships. V.L.T. Shareholder of Segmed. R.L.H. No relevant relationships. M.J.W. Postdoctoral fellowship grant from the American Heart Association. H.M. No relevant relationships. A.S.C. No relevant relationships. G.J.B. No relevant relationships. D.F. Deputy editor for Radiology: Cardiothoracic Imaging., (© 2022 by the Radiological Society of North America, Inc.)

Deep learning models are currently the cornerstone of artificial intelligence in medical imaging. While progress is still being made, the generic technological core of convolutional neural networks (CNNs) has had only modest innovations over the last several years, if at all. There is thus a need for improvement. More recently, transformer networks have emerged that replace convolutions with a complex attention mechanism, and they have already matched or exceeded the performance of CNNs in many tasks. Transformers need very large amounts of training data, even more than CNNs, but obtaining well-curated labeled data is expensive and difficult. A possible solution to this issue would be transfer learning with pretraining on a self-supervised task using very large amounts of unlabeled medical data. This pretrained network could then be fine-tuned on specific medical imaging tasks with relatively modest data requirements. The authors believe that the availability of a large-scale, three-dimension-capable, and extensively pretrained transformer model would be highly beneficial to the medical imaging and research community. In this article, authors discuss the challenges and obstacles of training a very large medical imaging transformer, including data needs, biases, training tasks, network architecture, privacy concerns, and computational requirements. The obstacles are substantial but not insurmountable for resourceful collaborative teams that may include academia and information technology industry partners. © RSNA, 2022 Keywords: Computer-aided Diagnosis (CAD), Informatics, Transfer Learning, Convolutional Neural Network (CNN)., Competing Interests: Disclosures of conflicts of interest: M.J.W. Grant or contract from the American Heart Association (no. 18POST34030192); consulting fees from Segmed; support from Segmed for attending meetings and/or travel; stock or stock options in Segmed. H.R.R. Employed by NVIDIA. V.S. No relevant relationships., (© 2022 by the Radiological Society of North America, Inc.)

In this prospective, pilot study, we tested a kV-independent coronary artery calcium scoring CT protocol, using a novel reconstruction kernel (Sa36f). From December 2018 to November 2019, we performed an additional research scan in 61 patients undergoing clinical calcium scanning. For the standard protocol (120 kVp), images were reconstructed with a standard, medium-sharp kernel (Qr36d). For the research protocol (automated kVp selection), images were reconstructed with a novel kernel (Sa36f). Research scans were sequentially performed using a higher (cohort A, n = 31) and a lower (cohort B, n = 30) dose optimizer setting within the automatic system with customizable kV selection. Agatston scores, coronary calcium volumes, and radiation exposure of the standard and research protocol were compared. A phantom study was conducted to determine inter-scan variability. There was excellent correlation for the Agatston score between the two protocols (r = 0.99); however, the standard protocol resulted in slightly higher Agatston scores (29.4 [0-139.0] vs 17.4 [0-158.2], p = 0.028). The median calcium volumes were similar (11.5 [0-109.2] vs 11.2 [0-118.0] mm 3 ; p = 0.176), and the number of calcified lesions was not significantly different (p = 0.092). One patient was reclassified to another risk category. The research protocol could be performed at a lower kV and resulted in a substantially lower radiation exposure, with a median volumetric CT dose index of 4.1 vs 5.2 mGy, respectively (p < 0.001). Our results showed that a consistent coronary calcium scoring can be achieved using a kV-independent protocol that lowers radiation doses compared to the standard protocol. KEY POINTS: • The Sa36f kernel enables kV-independent Agatston scoring without changing the original Agatston weighting threshold. • Agatston scores and calcium volumes of the standard and research protocols showed an excellent correlation. • The research protocol resulted in a significant reduction in radiation exposure with a mean reduction of 22% in DLP and 25% in CTDI vol ., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

Objective: The aim of the current study was to systematically assess coronary artery calcium (CAC) detection and quantification for spectral photon-counting CT (SPCCT) in comparison to conventional CT and, in addition, to evaluate the possibility of radiation dose reduction., Methods: Routine clinical CAC CT protocols were used for data acquisition and reconstruction of two CAC containing cylindrical inserts which were positioned within an anthropomorphic thorax phantom. In addition, data was acquired at 50% lower radiation dose by reducing tube current, and slice thickness was decreased. Calcifications were considered detectable when three adjacent voxels exceeded the CAC scoring threshold of 130 Hounsfield units (HU). Quantification of CAC (as volume and mass score) was assessed by comparison with known physical quantities., Results: In comparison with CT, SPCCT detected 33% and 7% more calcifications for the small and large phantoms, respectively. At reduced radiation dose and reduced slice thickness, small phantom CAC detection increased by 108% and 150% for CT and SPCCT, respectively. For the large phantom size, noise levels interfered with CAC detection. Although comparable between CT and SPCCT, routine protocols CAC quantification showed large deviations (up to 134%) from physical CAC volume. At reduced radiation dose and slice thickness, physical volume overestimations decreased to 96% and 72% for CT and SPCCT, respectively. In comparison with volume scores, mass score deviations from physical quantities were smaller., Conclusion: CAC detection on SPCCT is superior to CT, and was even preserved at a reduced radiation dose. Furthermore, SPCCT allows for improved physical volume estimation., Key Points: • In comparison with conventional CT, increased coronary artery calcium detection (up to 156%) for spectral photon-counting CT was found, even at 50% radiation dose reduction. • Spectral photon-counting CT can more accurately measure physical volumes than conventional CT, especially at reduced slice thickness and for high-density coronary artery calcium. • For both conventional and spectral photon-counting CT, reduced slice thickness reconstructions result in more accurate physical mass approximation., (© 2021. The Author(s), under exclusive licence to European Society of Radiology.)

Objective: To determine the difference in CT values and image quality of abdominal CT images reconstructed by filtered back-projection (FBP), hybrid iterative reconstruction (IR), and deep learning reconstruction (DLR)., Methods: PubMed and Embase were systematically searched for articles regarding CT densitometry in the abdomen and the image reconstruction techniques FBP, hybrid IR, and DLR. Mean differences in CT values between reconstruction techniques were analyzed. A comparison between signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of FBP, hybrid IR, and DLR was made. A comparison of diagnostic confidence between hybrid IR and DLR was made., Results: Sixteen articles were included, six being suitable for meta-analysis. In the liver, the mean difference between hybrid IR and DLR was - 0.633 HU (p = 0.483, SD ± 0.902 HU). In the spleen, the mean difference between hybrid IR and DLR was - 0.099 HU (p = 0.925, SD ± 1.061 HU). In the pancreas, the mean difference between hybrid IR and DLR was - 1.372 HU (p = 0.353, SD ± 1.476 HU). In 14 articles, CNR was described. In all cases, DLR showed a significantly higher CNR. In 9 articles, SNR was described. In all cases but one, DLR showed a significantly higher SNR. In all cases, DLR showed a significantly higher diagnostic confidence., Conclusions: There were no significant differences in CT values reconstructed by FBP, hybrid IR, and DLR in abdominal organs. This shows that these reconstruction techniques are consistent in reconstructing CT values. DLR images showed a significantly higher SNR and CNR, compared to FBP and hybrid IR., Key Points: CT values of abdominal CT images are similar between deep learning reconstruction (DLR), filtered back-projection (FBP), and hybrid iterative reconstruction (IR). DLR results in improved image quality in terms of SNR and CNR compared to FBP and hybrid IR images. DLR can thus be safely implemented in the clinical setting resulting in improved image quality without affecting CT values., (© 2021. The Author(s).)

All patients surviving an acute aortic dissection require continued lifelong surveillance of their diseased aorta. Late complications, driven predominantly by chronic false lumen degeneration and aneurysm formation, often require surgical, endovascular, or hybrid interventions to treat or prevent aortic rupture. Imaging plays a central role in the medical decision-making of patients with chronic aortic dissection. Accurate aortic diameter measurements and rigorous, systematic documentation of diameter changes over time with different imaging equipment and modalities pose a range of practical challenges in these complex patients. Currently, no guidelines or recommendations for imaging surveillance in patients with chronic aortic dissection exist. In this document, we present state-of-the-art imaging and measurement techniques for patients with chronic aortic dissection and clarify the need for standardized measurements and reporting for lifelong surveillance. We also examine the emerging role of imaging and computer simulations to predict aortic false lumen degeneration, remodeling, and biomechanical failure from morphological and hemodynamic features. These insights may improve risk stratification, individualize contemporary treatment options, and potentially aid in the conception of novel treatment strategies in the future.

Objective: The aim of the current study was, first, to assess the coronary artery calcium (CAC) scoring potential of spectral photon-counting CT (SPCCT) in comparison with computed tomography (CT) for routine clinical protocols. Second, improved CAC detection and quantification at reduced slice thickness were assessed., Methods: Raw data was acquired and reconstructed with several combinations of reduced slice thickness and increasing strengths of iterative reconstruction (IR) for both CT systems with routine clinical CAC protocols for CT. Two CAC-containing cylindrical inserts, consisting of CAC of different densities and sizes, were placed in an anthropomorphic phantom. A specific CAC was detectable when 3 or more connected voxels exceeded the CAC scoring threshold of 130 Hounsfield units (HU). For all reconstructions, total CAC detectability was compared between both CT systems. Significant differences in CAC quantification (Agatston and volume scores) were assessed with Mann-Whitney U tests. Furthermore, volume scores were compared with the known CAC physical., Results: CAC scores for routine clinical protocols were comparable between SPCCT and CT. SPCCT showed 34% and 4% higher detectability of CAC for the small and large phantom, respectively. At reduced slice thickness, CAC detection increased by 142% and 169% for CT and SPCCT, respectively. In comparison with CT, volume scores from SPCCT were more comparable with the physical volume of the CAC., Conclusion: CAC scores using routine clinical protocols are comparable between conventional CT and SPCCT. The increased spatial resolution of SPCCT allows for increased detectability and more accurate CAC volume estimation., Key Points: • Coronary artery calcium scores using routine clinical protocols are comparable between conventional CT and spectral photon-counting CT. • In comparison with conventional CT, increased coronary artery calcium detectability was shown for spectral photon-counting CT due to increased spatial resolution. • Volumes scores were more accurately determined with spectral photon-counting CT., (© 2021. The Author(s).)

Objectives: Although the Agatston score is a commonly used quantification method, rescan reproducibility is suboptimal, and different CT scanners result in different scores. In 2007, McCollough et al (Radiology 2007;243:527-538) proposed a standard for coronary artery calcium quantification. Advancements in CT technology over the last decade, however, allow for improved acquisition and reconstruction methods. This study aims to investigate the feasibility of a reproducible reduced dose alternative of the standardized approach for coronary artery calcium quantification on state-of-the-art CT systems from 4 major vendors., Materials and Methods: An anthropomorphic phantom containing 9 calcifications and 2 extension rings were used. Images were acquired with 4 state-of-the-art CT systems using routine protocols and a variety of tube voltages (80-120 kV), tube currents (100% to 25% dose levels), slice thicknesses (3/2.5 and 1/1.25 mm), and reconstruction techniques (filtered back projection and iterative reconstruction). Every protocol was scanned 5 times after repositioning the phantom to assess reproducibility. Calcifications were quantified as Agatston scores., Results: Reducing tube voltage to 100 kV, dose to 75%, and slice thickness to 1 or 1.25 mm combined with higher iterative reconstruction levels resulted in an on average 36% lower intrascanner variability (interquartile range) compared with the standard 120 kV protocol. Interscanner variability per phantom size decreased by 34% on average. With the standard protocol, on average, 6.2 ± 0.4 calcifications were detected, whereas 7.0 ± 0.4 were detected with the proposed protocol. Pairwise comparisons of Agatston scores between scanners within the same phantom size demonstrated 3 significantly different comparisons at the standard protocol (P < 0.05), whereas no significantly different comparisons arose at the proposed protocol (P > 0.05)., Conclusions: On state-of-the-art CT systems of 4 different vendors, a 25% reduced dose, thin-slice calcium scoring protocol led to improved intrascanner and interscanner reproducibility and increased detectability of small and low-density calcifications in this phantom. The protocol should be extensively validated before clinical use, but it could potentially improve clinical interscanner/interinstitutional reproducibility and enable more consistent risk assessment and treatment strategies., Competing Interests: Conflicts of interest and sources of funding: G.D.v.P. was supported in part by an unconditional grant from PUSH: a collaboration between Siemens Healthineers and the University Medical Center Groningen; the sponsor had no role in the conceptualization, interpretation of findings, writing, or publication of the article. D.M. has no activities related to the present article; he is a shareholder of and a consultant for Segmed, Inc. K.N. received institutional research support from Siemens Healthineers, Bayer Healthcare, GE Healthcare, and HeartFlow Inc. L.J.O.'s institution received a grant from Canon Medical Systems to hire a doctoral candidate to investigate subtraction CT and disclosed no other relevant relationships. D.F. has no activities related to the present article; he received research support from Siemens Healthineers and GE Healthcare, is on the speakers' bureau at Siemens Healthineers, and has ownership interest in iSchemaView; he disclosed no other relevant relationships. M.J.W. has no activities related to the present article and received a research grant from Philips Healthcare; he is a cofounder, advisor, and stockholder of Segmed, Inc; he disclosed no other relevant relationships. J.W., N.R.v.d.W., M.J.W.G., R.W.v.H., F.d.L., R.H.J.A.S., and T.L. have nothing to disclose., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Objectives: The purpose of this study was twofold. First, the influence of a novel calcium-aware (Ca-aware) computed tomography (CT) reconstruction technique on coronary artery calcium (CAC) scores surrounded by a variety of tissues was assessed. Second, the performance of the Ca-aware reconstruction technique on moving CAC was evaluated with a dynamic phantom., Methods: An artificial coronary artery, containing two CAC of equal size and different densities (196 ± 3, 380 ± 2 mg hydroxyapatite cm -3 ), was moved in the center compartment of an anthropomorphic thorax phantom at different heart rates. The center compartment was filled with mixtures, which resembled fat, water, and soft tissue equivalent CT numbers. Raw data was acquired with a routine clinical CAC protocol, at 120 peak kilovolt (kVp). Subsequently, reduced tube voltage (100 kVp) and tin-filtration (150Sn kVp) acquisitions were performed. Raw data was reconstructed with a standard and a novel Ca-aware reconstruction technique. Agatston scores of all reconstructions were compared with the reference (120 kVp) and standard reconstruction technique, with relevant deviations defined as > 10%., Results: For all heart rates, Agatston scores for CAC submerged in fat were comparable to the reference, for the reduced-kVp acquisition with Ca-aware reconstruction kernel. For water and soft tissue, medium-density Agatston scores were again comparable to the reference for all heart rates. Low-density Agatston scores showed relevant deviations, up to 15% and 23% for water and soft tissue, respectively., Conclusion: CT CAC scoring with varying surrounding materials and heart rates is feasible at patient-specific tube voltages with the novel Ca-aware reconstruction technique., Key Points: • A dedicated calcium-aware reconstruction kernel results in similar Agatston scores for CAC surrounded by fatty materials regardless of CAC density and heart rate. • Application of a dedicated calcium-aware reconstruction kernel allows for radiation dose reduction. • Mass scores determined with CT underestimated physical mass., (© 2021. The Author(s).)

Objectives: To determine whether single-phase dual-energy CT (DECT) differentiates vascular and nonvascular renal lesions in the portal venous phase (PVP). Optimal iodine threshold was determined and compared to Hounsfield unit (HU) measurements., Methods: We retrospectively included 250 patients (266 renal lesions) who underwent a clinically indicated PVP abdominopelvic CT on a rapid-kilovoltage-switching single-source DECT (rsDECT) or a dual-source DECT (dsDECT) scanner. Iodine concentration and HU measurements were calculated by four experienced readers. Diagnostic accuracy was determined using biopsy results and follow-up imaging as reference standard. Area under the curve (AUC) was calculated for each DECT scanner to differentiate vascular from nonvascular lesions and vascular lesions from hemorrhagic/proteinaceous cysts. Univariable and multivariable logistic regression analyses evaluated the association between variables and the presence of vascular lesions., Results: A normalized iodine concentration threshold of 0.25 mg/mL yielded high accuracy in differentiating vascular and nonvascular lesions (AUC 0.93, p < 0.001), with comparable performance to HU measurements (AUC 0.93). Both iodine concentration and HU measurements were independently associated with vascular lesions when adjusted for age, gender, body mass index, and lesion size (AUC 0.95 and 0.95, respectively). When combined, diagnostic performance was higher (AUC 0.96). Both absolute and normalized iodine concentrations performed better than HU measurements (AUC 0.92 vs. AUC 0.87) in differentiating vascular lesions from hemorrhagic/proteinaceous cysts., Conclusion: A single-phase (PVP) DECT scan yields high accuracy to differentiate vascular from nonvascular renal lesions. Iodine concentration showed a slightly higher performance than HU measurements in differentiating vascular lesions from hemorrhagic/proteinaceous cysts., Key Points: • A single-phase dual-energy CT scan in the portal venous phase differentiates vascular from nonvascular renal lesions with high accuracy (AUC 0.93). • When combined, iodine concentration and HU measurements showed the highest diagnostic performance (AUC 0.96) to differentiate vascular from nonvascular renal lesions. • Compared to HU measurements, iodine concentration showed a slightly higher performance in differentiating vascular lesions from hemorrhagic/proteinaceous cysts., (© 2021. European Society of Radiology.)

Background: The aim of this study was to evaluate the role of the distance between the aortic valve in projected position to the coronary ostium to determine risk of coronary artery obstruction after transcatheter aortic valve replacement (TAVR)., Methods: An Expected Leaflet-to-ostium Distance (ELOD) was obtained on pre-TAVR planning computed tomography by subtracting leaflet thickness and the distances from the center to the annular rim at annulus level and from the center to the coronary ostium at mid-ostial level. Variables were compared between patients with and without coronary obstruction and the level of association between variables was assessed using log odds ratio (OR)., Results: A total of 177 patients with 353 coronary arteries was analyzed. Mean annulus diameters (22.8 ± 2.8 mm and 23.4 ± 1.0 mm, p > 0.05) and mean sinus of Valsalva (SOV) diameters (31.2 ± 3.6 mm and 31.9 ± 3.6 mm, p > 0.05) were similar between patients with lower and higher coronary heights, respectively. There were three coronary obstruction cases. ELOD ≤ 2 mm in combination with leaflet length longer than mid-ostial height allowed for discrimination of cases with and without coronary obstruction. There was a significant association between coronary obstruction event and ELOD ≤ 2 mm (log OR = 6.180, p < 0.001)., Conclusions: Our study showed that a combination of ELOD < 2 mm and a longer leaflet length than mid-ostial height may be associated with increased risk for coronary obstruction during TAVR., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Author(s).)

Objectives: Currently, hurdles to implementation of artificial intelligence (AI) in radiology are a much-debated topic but have not been investigated in the community at large. Also, controversy exists if and to what extent AI should be incorporated into radiology residency programs., Methods: Between April and July 2019, an international survey took place on AI regarding its impact on the profession and training. The survey was accessible for radiologists and residents and distributed through several radiological societies. Relationships of independent variables with opinions, hurdles, and education were assessed using multivariable logistic regression., Results: The survey was completed by 1041 respondents from 54 countries. A majority (n = 855, 82%) expects that AI will cause a change to the radiology field within 10 years. Most frequently, expected roles of AI in clinical practice were second reader (n = 829, 78%) and work-flow optimization (n = 802, 77%). Ethical and legal issues (n = 630, 62%) and lack of knowledge (n = 584, 57%) were mentioned most often as hurdles to implementation. Expert respondents added lack of labelled images and generalizability issues. A majority (n = 819, 79%) indicated that AI should be incorporated in residency programs, while less support for imaging informatics and AI as a subspecialty was found (n = 241, 23%)., Conclusions: Broad community demand exists for incorporation of AI into residency programs. Based on the results of the current study, integration of AI education seems advisable for radiology residents, including issues related to data management, ethics, and legislation., Key Points: • There is broad demand from the radiological community to incorporate AI into residency programs, but there is less support to recognize imaging informatics as a radiological subspecialty. • Ethical and legal issues and lack of knowledge are recognized as major bottlenecks for AI implementation by the radiological community, while the shortage in labeled data and IT-infrastructure issues are less often recognized as hurdles. • Integrating AI education in radiology curricula including technical aspects of data management, risk of bias, and ethical and legal issues may aid successful integration of AI into diagnostic radiology., (© 2021. The Author(s).)

Patients with initially uncomplicated typeB aortic dissection (uTBAD) remain at high risk for developing late complications. Identification of morphologic features for improving risk stratification of these patients requires automated segmentation of computed tomography angiography (CTA) images. We developed three segmentation models utilizing a 3D residual U-Net for segmentation of the true lumen (TL), false lumen (FL), and false lumen thrombosis (FLT). Model 1 segments all labels at once, whereas model 2 segments them sequentially. Best results for TL and FL segmentation were achieved by model 2, with median (interquartiles) Dice similarity coefficients (DSC) of 0.85 (0.77-0.88) and 0.84 (0.82-0.87), respectively. For FLT segmentation, model 1 was superior to model 2, with median (interquartiles) DSCs of 0.63 (0.40-0.78). To purely test the performance of the network to segment FLT, a third model segmented FLT starting from the manually segmented FL, resulting in median (interquartiles) DSCs of 0.99 (0.98-0.99) and 0.85 (0.73-0.94) for patent FL and FLT, respectively. While the ambiguous appearance of FLT on imaging remains a significant limitation for accurate segmentation, our pipeline has the potential to help in segmentation of aortic lumina and thrombosis in uTBAD patients.Clinical relevance- Most predictors of aortic dissection (AD) degeneration are identified through anatomical modeling, which is currently prohibitive in clinical settings due to the timeintense human interaction. False lumen thrombosis, which often develops in patients with type B AD, has proven to show significant prognostic value for predicting late adverse events. Our automated segmentation algorithm offers the potential of personalized treatment for AD patients, leading to an increase in long-term survival.

Objectives: Radiologists' perception is likely to influence the adoption of artificial intelligence (AI) into clinical practice. We investigated knowledge and attitude towards AI by radiologists and residents in Europe and beyond., Methods: Between April and July 2019, a survey on fear of replacement, knowledge, and attitude towards AI was accessible to radiologists and residents. The survey was distributed through several radiological societies, author networks, and social media. Independent predictors of fear of replacement and a positive attitude towards AI were assessed using multivariable logistic regression., Results: The survey was completed by 1,041 respondents from 54 mostly European countries. Most respondents were male (n = 670, 65%), median age was 38 (24-74) years, n = 142 (35%) residents, and n = 471 (45%) worked in an academic center. Basic AI-specific knowledge was associated with fear (adjusted OR 1.56, 95% CI 1.10-2.21, p = 0.01), while intermediate AI-specific knowledge (adjusted OR 0.40, 95% CI 0.20-0.80, p = 0.01) or advanced AI-specific knowledge (adjusted OR 0.43, 95% CI 0.21-0.90, p = 0.03) was inversely associated with fear. A positive attitude towards AI was observed in 48% (n = 501) and was associated with only having heard of AI, intermediate (adjusted OR 11.65, 95% CI 4.25-31.92, p < 0.001), or advanced AI-specific knowledge (adjusted OR 17.65, 95% CI 6.16-50.54, p < 0.001)., Conclusions: Limited AI-specific knowledge levels among radiology residents and radiologists are associated with fear, while intermediate to advanced AI-specific knowledge levels are associated with a positive attitude towards AI. Additional training may therefore improve clinical adoption., Key Points: • Forty-eight percent of radiologists and residents have an open and proactive attitude towards artificial intelligence (AI), while 38% fear of replacement by AI. • Intermediate and advanced AI-specific knowledge levels may enhance adoption of AI in clinical practice, while rudimentary knowledge levels appear to be inhibitive. • AI should be incorporated in radiology training curricula to help facilitate its clinical adoption., (© 2021. The Author(s).)

Background: Identifying high-risk patients who will not derive substantial survival benefit from TAVR remains challenging. Pulmonary hypertension is a known predictor of poor outcome in patients undergoing TAVR and correlates strongly with pulmonary artery (PA) enlargement on CTA. We sought to evaluate whether PA enlargement, measured on pre-procedural computed tomography angiography (CTA), is associated with 1-year mortality in patients undergoing TAVR., Methods: We retrospectively included 402 patients undergoing TAVR between July 2012 and March 2016. Clinical parameters, including Society of Thoracic Surgeons (STS) score and right ventricular systolic pressure (RVSP) estimated by transthoracic echocardiography were reviewed. PA dimensions were measured on pre-procedural CTAs. Association between PA enlargement and 1-year mortality was analyzed. Kaplan-Meier and Cox proportional hazards regression analyses were performed., Results: The median follow-up time was 433 (interquartiles 339-797) days. A total of 56/402 (14%) patients died within 1 year after TAVR. Main PA area (area-MPA) was independently associated with 1-year mortality (hazard ratio per standard deviation equal to 2.04 [95%-confidence interval (CI) 1.48-2.76], p ​< ​0.001). Area under the curve (95%-CI) of the clinical multivariable model including STS-score and RVSP increased slightly from 0.67 (0.59-0.75) to 0.72 (0.72-0.89), p ​= ​0.346 by adding area-MPA. Although the AUC increased, differences were not significant (p ​= ​0.346). Kaplan-Meier analysis showed that mortality was significantly higher in patients with a pre-procedural non-indexed area-MPA of ≥7.40 ​cm 2 compared to patients with a smaller area-MPA (mortality 23% vs. 9%; p ​< ​0.001)., Conclusions: Enlargement of MPA on pre-procedural CTA is independently associated with 1-year mortality after TAVR., Competing Interests: Declaration of competing interest This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors., (Copyright © 2021 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.)

Objective: Coronary artery calcium (CAC) score is a strong predictor for future adverse cardiovascular events. Anthropomorphic phantoms are often used for CAC studies on computed tomography (CT) to allow for evaluation or variation of scanning or reconstruction parameters within or across scanners against a reference standard. This often results in large number of datasets. Manual assessment of these large datasets is time consuming and cumbersome. Therefore, this study aimed to develop and validate a fully automated, open-source quantification method (FQM) for coronary calcium in a standardized phantom., Materials and Methods: A standard, commercially available anthropomorphic thorax phantom was used with an insert containing nine calcifications with different sizes and densities. To simulate two different patient sizes, an extension ring was used. Image data were acquired with four state-of-the-art CT systems using routine CAC scoring acquisition protocols. For interscan variability, each acquisition was repeated five times with small translations and/or rotations. Vendor-specific CAC scores (Agatston, volume, and mass) were calculated as reference scores using vendor-specific software. Both the international standard CAC quantification methods as well as vendor-specific adjustments were implemented in FQM. Reference and FQM scores were compared using Bland-Altman analysis, intraclass correlation coefficients, risk reclassifications, and Cohen's kappa. Also, robustness of FQM was assessed using varied acquisitions and reconstruction settings and validation on a dynamic phantom. Further, image quality metrics were implemented: noise power spectrum, task transfer function, and contrast- and signal-to-noise ratio among others. Results were validated using imQuest software., Results: Three parameters in CAC scoring methods varied among the different vendor-specific software packages: the Hounsfield unit (HU) threshold, the minimum area used to designate a group of voxels as calcium, and the usage of isotropic voxels for the volume score. The FQM was in high agreement with vendor-specific scores and ICC's (median [95% CI]) were excellent (1.000 [0.999-1.000] to 1.000 [1.000-1.000]). An excellent interplatform reliability of κ = 0.969 and κ = 0.973 was found. TTF results gave a maximum deviation of 3.8% and NPS results were comparable to imQuest., Conclusions: We developed a fully automated, open-source, robust method to quantify CAC on CT scans in a commercially available phantom. Also, the automated algorithm contains image quality assessment for fast comparison of differences in acquisition and reconstruction parameters., (© 2021 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Photon-counting computed tomography (PCCT) is an emerging technology promising to substantially improve cardiovascular imaging. Recent engineering and manufacturing advances by several vendors are expected to imminently launch this new technology into clinical reality. Photon-counting detectors (PCDs) have multiple potential advantages over conventional energy integrating detectors (EIDs) such as the absence of electronic noise, multi-energy capability, and increased spatial resolution. These developments will have different timescales for implementation and will affect different clinical scopes. We describe the technical aspects of PCCT, explain the current developments, and finally discuss potential advantages of PCCT in cardiovascular imaging., Competing Interests: Declaration of competing interest None of the authors are industry employees. Veit Sandfort: Activities related to the present article: None. Activities not related to the present article: None. Other relationships: Disclosed no relevant relationships. Mats Persson: Activities related to the present article: Stockholder and past paid consultancy, Prismatic Sensors AB. Research collaboration, Prismatic Sensors AB. Past visiting researcher at General Electric Company, with funding from the EU Research Executive Agency. Activities not related to the present article: disclosed no relevant relationships. Other relationships: Disclosed no relevant relationships. Amir Pourmorteza: Activities related to the present article: None. Activities not related to the present article: past research funds from Syntermed Inc. Other relationships: Stockholder and paid consultant of Cardiowise Inc. Peter B. Noël: Activities related to the present article: receives research grant funding from Philips Healthcare. Activities not related to the present article: member of the advisory board at Philips Healthcare. receives research grant funding from the University of Pennsylvania Research Foundation (URF), and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project 432290010. Other relationships: Shareholder of MITOS GmbH and past paid consultant for Stryker Corporation. Dominik Fleischmann: Activities related to the present article: None. Activities not related to the present article: received research grants from Siemens Healthineers. Other relationships: Disclosed no relevant relationships. Martin J. Willemink: Activities related to the present article: None. Activities not related to the present article: received research grants from the American Heart Association (18POST34030192) and Philips Healthcare. He is co-founder, advisor and stockholder of Segmed, Inc. Other relationships: disclosed no relevant relationships., (Copyright © 2020 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.)

Purpose: To determine whether multiphasic dual-energy (DE) CT iodine quantitation correlates with the severity of chronic liver disease., Methods: We retrospectively included 40 cirrhotic and 28 non-cirrhotic patients who underwent a multiphasic liver protocol DECT. All three phases (arterial, portal venous (PVP), and equilibrium) were performed in DE mode. Iodine (I) values (mg I/ml) were obtained by placing regions of interest in the liver, aorta, common hepatic artery, and portal vein (PV). Iodine slopes (λ) were calculated as follows: (I equilibrium -I arterial )/time and (I equilibrium -I PVP )/time. Spearman correlations between λ and MELD scores were evaluated, and the area under the curve of the receiver operating characteristic (AUROC) was calculated to distinguish cirrhotic and non-cirrhotic patients., Results: Cirrhotic and non-cirrhotic patients had significantly different λ equilibrium-arterial [IQR] for the caudate (λ = 2.08 [1.39-2.98] vs 1.46 [0.76-1.93], P = 0.007), left (λ = 2.05 [1.50-2.76] vs 1.51 [0.59-1.90], P = 0.002) and right lobes (λ = 1.72 [1.12-2.50] vs 1.13 [0.41-0.43], P = 0.003) and for the PV (λ = 3.15 [2.20-5.00] vs 2.29 [0.85-2.71], P = 0.001). λ equilibrium-PVP were significantly different for the right (λ = 0.11 [- 0.45-1.03] vs - 0.44 [- 0.83-0.12], P = 0.045) and left lobe (λ = 0.30 [- 0.25-0.98] vs - 0.10 [- 0.35-0.24], P = 0.001). Significant positive correlations were found between MELD scores and λ equilibrium-arterial for the caudate lobe (ρ = 0.34, P = 0.004) and λ equilibrium-PVP for the caudate (ρ = 0.26, P = 0.028) and right lobe (ρ = 0.33, P = 0.007). AUROC in distinguishing cirrhotic and non-cirrhotic patients were 0.72 (P = 0.002), 0.71 (P = 0.003), and 0.75 (P = 0.001) using λ equilibrium-arterial for the left lobe, right lobe, and PV, respectively. The λ equilibrium-PVP AUROC of the right lobe was 0.73 (P = 0.001)., Conclusion: Multiphasic DECT iodine quantitation over time is significantly different between cirrhotic and non-cirrhotic patients, correlates with the MELD score, and it could potentially serve as a non-invasive measure of cirrhosis and disease severity with acceptable diagnostic accuracy.

Background: Patients with medically treated type B aortic dissection (TBAD) remain at significant risk for late adverse events (LAEs). We hypothesize that not only initial morphological features, but also their change over time at follow-up are associated with LAEs., Materials and Methods: Baseline and 188 follow-up computed tomography (CT) scans with a median follow-up time of 4 years (range, 10 days to 12.7 years) of 47 patients with acute uncomplicated TBAD were retrospectively reviewed. Morphological features (n = 8) were quantified at baseline and each follow-up. Medical records were reviewed for LAEs, which were defined according to current guidelines. To assess the effects of changes of morphological features over time, the linear mixed effects models were combined with Cox proportional hazards regression for the time-to-event outcome using a joint modeling approach., Results: LAEs occurred in 21 of 47 patients at a median of 6.6 years (95% confidence interval [CI], 5.1-11.2 years). Among the 8 investigated morphological features, the following 3 features showed strong association with LAEs: increase in partial false lumen thrombosis area (hazard ratio [HR], 1.39; 95% CI, 1.18-1.66 per cm 2 increase; P < .001), increase of major aortic diameter (HR, 1.24; 95% CI, 1.13-1.37 per mm increase; P < .001), and increase in the circumferential extent of false lumen (HR, 1.05; 95% CI, 1.01-1.10 per degree increase; P < .001)., Conclusions: In medically treated TBAD, increases in aortic diameter, new or increased partial false lumen thrombosis area, and increases of circumferential extent of the false lumen are strongly associated with LAEs., (Copyright © 2019 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.)

After an ischemic event, disruptive changes in the healthy myocardium may gradually develop and may ultimately turn into fibrotic scar. While these structural changes have been described by conventional imaging modalities mostly on a macroscopic scale-i.e., late gadolinium enhancement at magnetic resonance imaging (MRI)-in recent years, novel imaging methods have shown the potential to unveil an even more detailed picture of the postischemic myocardial phenomena. These new methods may bring advances in the understanding of ischemic heart disease with potential major changes in the current clinical practice. In this review article, we provide an overview of the emerging methods for the non-invasive characterization of ischemic heart disease, including coronary ultrafast Doppler angiography, photon-counting computed tomography (CT), micro-CT (for preclinical studies), low-field and ultrahigh-field MRI, and 11 C-methionine positron emission tomography. In addition, we discuss new opportunities brought by artificial intelligence, while addressing promising future scenarios and the challenges for the application of artificial intelligence in the field of cardiac imaging.

Accurate quantification and characterization of atherosclerotic plaques with MRI requires high spatial resolution acquisitions with excellent image quality. The intrinsically better signal-to-noise ratio (SNR) at high-field clinical 7T compared to the widely employed lower field strengths of 1.5 and 3T may yield significant improvements to vascular MRI. However, 7T atherosclerosis imaging also presents specific challenges, related to local transmit coils and B1 field inhomogeneities, which may overshadow these theoretical gains. We present the development and evaluation of 3D, black-blood, ultra-high resolution vascular MRI on clinical high-field 7T in comparison lower-field 3T. These protocols were applied for in vivo imaging of atherosclerotic rabbits, which are often used for development, testing, and validation of translatable cardiovascular MR protocols. Eight atherosclerotic New Zealand White rabbits were imaged on clinical 7T and 3T MRI scanners using 3D, isotropic, high (0.63 mm3) and ultra-high (0.43 mm3) spatial resolution, black-blood MR sequences with extensive spatial coverage. Following imaging, rabbits were sacrificed for validation using fluorescence imaging and histology. Image quality parameters such as SNR and contrast-to-noise ratio (CNR), as well as morphological and functional plaque measurements (plaque area and permeability) were evaluated at both field strengths. Using the same or comparable imaging parameters, SNR and CNR were in general higher at 7T compared to 3T, with a median (interquartiles) SNR gain of +40.3 (35.3-80.1)%, and a median CNR gain of +68.1 (38.5-95.2)%. Morphological and functional parameters, such as vessel wall area and permeability, were reliably acquired at 7T and correlated significantly with corresponding, widely validated 3T vessel wall MRI measurements. In conclusion, we successfully developed 3D, black-blood, ultra-high spatial resolution vessel wall MRI protocols on a 7T clinical scanner. 7T imaging was in general superior to 3T with respect to image quality, and comparable in terms of plaque area and permeability measurements., Competing Interests: The authors have read the journal's policy and have the following competing interests: IS is a paid employee of Takeda Pharmaceutical Company Limited, and HD is a paid employee of Hyperfine Research Inc. There are no patents, products in development or marketed products associated with this research to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Although artificial intelligence (AI) has been a focus of medical research for decades, in the last decade, the field of radiology has seen tremendous innovation and also public focus due to development and application of machine-learning techniques to develop new algorithms. Interestingly, this innovation is driven simultaneously by academia, existing global medical device vendors, and-fueled by venture capital-recently founded startups. Radiologists find themselves once again in the position to lead this innovation to improve clinical workflows and ultimately patient outcome. However, although the end of today's radiologists' profession has been proclaimed multiple times, routine clinical application of such AI algorithms in 2020 remains rare. The goal of this review article is to describe in detail the relevance of appropriate imaging data as a bottleneck for innovation, provide insights into the many obstacles for technical implementation, and give additional perspectives to radiologists who often view AI solely from their clinical role. As regulatory approval processes for such medical devices are currently under public discussion and the relevance of imaging data is transforming, radiologists need to establish themselves as the leading gatekeepers for evolution of their field and be aware of the many stakeholders and sometimes conflicting interests.

Purpose: To develop a segmentation pipeline for segmentation of aortic dissection CT angiograms into true and false lumina on multiplanar reformations (MPRs) perpendicular to the aortic centerline and derive quantitative morphologic features, specifically aortic diameter and true- or false-lumen cross-sectional area., Materials and Methods: An automated segmentation pipeline including two convolutional neural network (CNN) segmentation algorithms was developed. The algorithm derives the aortic centerline, generates MPRs orthogonal to the centerline, and segments the true and false lumina. A total of 153 CT angiograms obtained from 45 retrospectively identified patients (mean age, 50 years; range, 22-79 years) were used to train ( n = 103), validate ( n = 22), and test ( n = 28) the CNN pipeline. Accuracy was evaluated by using the Dice similarity coefficient (DSC). Segmentations were then used to derive the maximal diameter of test-set patients and cross-sectional area profiles of the true and false lumina., Results: The segmentation pipeline yielded a mean DSC of 0.873 ± 0.056 for the true lumina and 0.894 ± 0.040 for the false lumina of test-set cases. Automated maximal diameter measurements correlated well with manual measurements ( R 2 = 0.95). Profiles of cross-sectional diameter, true-lumen area, and false-lumen area over several follow-up examinations were derived., Conclusion: A segmentation pipeline was used to accurately identify true and false lumina on CT angiograms of aortic dissection. These segmentations can be used to obtain diameter and other morphologic parameters for surveillance and risk stratification. Supplemental material is available for this article. © RSNA, 2020., Competing Interests: Disclosures of Conflicts of Interest: L.D.H. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: is a paid consultant for Arterys. Other relationships: disclosed no relevant relationships. G.M. disclosed no relevant relationships. K.H. disclosed no relevant relationships. M.K. disclosed no relevant relationships. M.J.W. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: is a paid consultant for Arterys; has grants/grants pending from Philips Healthcare; has stock/stock options in Segmed. Other relationships: disclosed no relevant relationships. A.M.S. disclosed no relevant relationships. M.F. disclosed no relevant relationships. D.F. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: institution has grants/grants pending from Siemens; has stock/stock options in iSchemaView and Segmed. Other relationships: disclosed no relevant relationships., (2020 by the Radiological Society of North America, Inc.)

The Project Baseline Health Study (PBHS) was launched to map human health through a comprehensive understanding of both the health of an individual and how it relates to the broader population. The study will contribute to the creation of a biomedical information system that accounts for the highly complex interplay of biological, behavioral, environmental, and social systems. The PBHS is a prospective, multicenter, longitudinal cohort study that aims to enroll thousands of participants with diverse backgrounds who are representative of the entire health spectrum. Enrolled participants will be evaluated serially using clinical, molecular, imaging, sensor, self-reported, behavioral, psychological, environmental, and other health-related measurements. An initial deeply phenotyped cohort will inform the development of a large, expanded virtual cohort. The PBHS will contribute to precision health and medicine by integrating state of the art testing, longitudinal monitoring and participant engagement, and by contributing to the development of an improved platform for data sharing and analysis., Competing Interests: Competing interestsK.A. received research funding from Verily Inc. for work at Duke Clinical and Translational Science Institute. R.C. and J.H. are employed or have a leadership role at at Google Health. R.C. is also a Board member, Cytokinetics. M.D. receives research funding from Celgene, Abbvie, United Therapeutics, Varian, Genzyme, Novartis, Verily Inc., and consults or receives honoraria from Astra Zeneca, Bristol Myer Squibb. A.H. receives research funding from American Regent, AstraZeneca, Merck, Novartis, Verily Inc., and consults for Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Boston Scientific, Merck, Novartis, and Sanofi. C.L. has the following engagements Shareholder and Advisory Board, whiterabbit.ai; Shareholder, Nines.ai; Advisory Board, Nines.ai; Shareholder, GalileoCDS, Inc.; Advisory Board, GalileoCDS, Inc; Shareholder and Board of Directors Bunker Hill, Inc.; Research Grant, GE Healthcare; Departmental Research Grant, Koninklijke Philips NV; Departmental Research Grant, Siemens AG; School of Medicine Research Grant, Google, Inc.; Travel Grant, Canon Medical Systems Corp; Travel Grant, Siemens Healthineers. S.S.G. is a consultant or receives funding from several companies that work in the healthcare space although none of these companies are directly involved in the current work. K.W.M.’s financial disclosure can be viewed at http://med.stanford.edu/profiles/kenneth-mahaffey. W.J.M., J.M., D.M., and S.S. are employed, have a leadership role, or receive equity from Verily Inc. J.M. is also a Board Member, Danaher. L.K.N. received research grant support from Verily Inc., (© The Author(s) 2020.)