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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).)

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.)

Objective: Late adverse events (LAEs) are common among initially uncomplicated type B aortic dissection (uTBAD); however, identifying those patients at highest risk of LAEs remains a significant challenge. Early false lumen (FL) growth has been suggested to increase risk, but confident determination of growth is often hampered by error in two-dimensional clinical measurements. Semi-automated three-dimensional (3D) mapping of aortic growth, such as by vascular deformation mapping (VDM), can potentially overcome this limitation using computed tomography angiograms (CTA). We hypothesized that FL growth in the early pre-dissection phase by VDM can accurately predict LAEs., Methods: We performed a two-center retrospective study of patients with uTBAD, with paired CTAs in the acute (1-14 days) and subacute/early chronic (1-6 months) periods. VDM analysis was used to map 3D growth. Standard clinical CT measures (ie, aortic diameters, tear characteristics) were also collected. Multivariate analysis was conducted using a decision tree and Cox proportional hazards model. LAEs were defined as aneurysmal FL (>55 mm); rapid growth (>5 mm within 6 months); aorta-specific mortality, rupture, or re-dissection., Results: A total of 107 (69% male) patients with uTBAD initially met inclusion criteria with a median follow-up of 7.3 years (interquartile range [IQR], 4.7-9.9 years). LAEs occurred in 72 patients (67%) at 2.5 years (IQR, 0.7-4.8 years) after the initial event. A multivariate decision tree model identified VDM growth (>2.1 mm) and baseline diameter (>42.7 mm) as optimal predictors of LAEs (area under the receiver operating characteristic curve = 0.94), achieving an 87% accuracy (sensitivity of 93%, specificity of 76%) after leave-one-out validation. Guideline reported high-risk features were not significantly different between groups., Conclusions: Early growth of the FL in uTBAD was the best tested indicator for LAEs and improves upon the current gold-standard of baseline diameter in selecting patients for early prophylactic thoracic endovascular aortic repair., Competing Interests: Disclosures N.S.B. is entitled to royalties related to licensure of intellectual property from vascular deformation mapping (VDM) to Imbio Inc., (Copyright © 2024 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.)

Purpose: To evaluate the diagnostic accuracy of a structured reporting score (SRS) in treatment response assessment for acute pyelonephritis (APN) using a diffusion-weighted imaging (DWI) -based MRI approach. Additionally, we explored the influence of reader experience on the interpretation of SRS and DWI, including lesion conspicuity and measurements of Apparent Diffusion Coefficient (ADC) maps., Methods: Follow-up DWI-based MRIs of 36 patients treated for APN between September 2021 and June 2023 were retrospectively reviewed by three readers. Follow-up blood inflammatory markers were used as reference standard. Treatment response was assessed using a structured reporting score (SRS). Each reader assigned a score from 1 to 3 to the "conspicuity" of the residual disease on DWI. Quantitative ADC measurements were compared with the Mann-Whitney U test. Descriptive statistics and Intraclass Correlation Coefficient (ICC) were calculated., Results: The diagnostic accuracy of SRS was 80.6 %, 76.9 %, and 72.2 % for the Reader 1, 2, and 3 respectively. ICC decreased from 0.82 (Reader 1 and 2), to 0.68 when considering all readers. The average conspicuity varied between 2.3 and 2.7. ADC values were significantly higher in complete responders for Reader 1 and 2 (153.5-154.5 vs 107.7-116.2, p < 0.001). The ICC was good (0.89) for Reader 1 and 2 and moderate (0.60) when considering all readers., Conclusions: Treatment response of pyelonephritis can be accurately assessed by a DWI-based MRI, potentially avoiding unnecessary contrast agent administration and radiation exposure. SRS and DWI analysis showed a good inter-observer agreement but a certain learning curve may be necessary for less expert readers., Competing Interests: Declaration of competing interest Andrea Delli Pizzi is member of the Scientific Editorial Board of Insight Into Imaging. All the other authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Purpose: To determine the optimal window setting for virtual monoenergetic images (VMI) reconstructed from dual-layer spectral coronary computed tomography angiography (DE-CCTA) datasets., Material and Methods: 50 patients (30 males; mean age 61.1 ± 12.4 years who underwent DE-CCTA from May 2021 to June 2022 for suspected coronary artery disease, were retrospectively included. Image quality assessment was performed on conventional images and VMI reconstructions at 70 and 40 keV. Objective image quality was assessed using contrast-to-noise ratio (CNR). Two independent observers manually identified the best window settings (B-W/L) for VMI 70 and VMI 40 visualization. B-W/L were then normalized with aortic attenuation using linear regression analysis to obtain the optimized W/L (O-W/L) settings. Additionally, subjective image quality was evaluated using a 5-point Likert scale, and vessel diameters were measured to examine any potential impact of different W/L settings., Results: VMI 40 demonstrated higher CNR values compared to conventional and VMI 70. B-W/L settings identified were 1180/280 HU for VMI 70 and 3290/900 HU for VMI 40. Subsequent linear regression analysis yielded O-W/L settings of 1155/270 HU for VMI 70 and 3230/880 HU for VMI 40. VMI 40 O-W/L received the highest scores for each parameter compared to conventional (all p < 0.0027). Using O-W/L settings for VMI 70 and VMI 40 did not result in significant differences in vessel measurements compared to conventional images., Conclusion: Optimization of VMI requires adjustments in W/L settings. Our results recommend W/L settings of 1155/270 HU for VMI 70 and 3230/880 HU for VMI 40., (© 2024. The Author(s).)

Treatment response assessment of rectal cancer patients is a critical component of personalized cancer care and it allows to identify suitable candidates for organ-preserving strategies. This pilot study employed a novel multi-omics approach combining MRI-based radiomic features and untargeted metabolomics to infer treatment response at staging. The metabolic signature highlighted how tumor cell viability is predictively down-regulated, while the response to oxidative stress was up-regulated in responder patients, showing significantly reduced oxoproline values at baseline compared to non-responder patients (p-value < 10 -4 ). Tumors with a high degree of texture homogeneity, as assessed by radiomics, were more likely to achieve a major pathological response (p-value < 10 -3 ). A machine learning classifier was implemented to summarize the multi-omics information and discriminate responders and non-responders. Combining all available radiomic and metabolomic features, the classifier delivered an AUC of 0.864 (± 0.083, p-value < 10 -3 ) with a best-point sensitivity of 90.9% and a specificity of 81.8%. Our results suggest that a multi-omics approach, integrating radiomics and metabolomic data, can enhance the predictive value of standard MRI and could help to avoid unnecessary surgical treatments and their associated long-term complications., (© 2024. The Author(s).)

Radiology: Cardiothoracic Imaging publishes novel research and technical developments in cardiac, thoracic, and vascular imaging. The journal published many innovative studies during 2023 and achieved an impact factor for the first time since its inaugural issue in 2019, with an impact factor of 7.0. The current review article, led by the Radiology: Cardiothoracic Imaging trainee editorial board, highlights the most impactful articles published in the journal between November 2022 and October 2023. The review encompasses various aspects of coronary CT, photon-counting detector CT, PET/MRI, cardiac MRI, congenital heart disease, vascular imaging, thoracic imaging, artificial intelligence, and health services research. Key highlights include the potential for photon-counting detector CT to reduce contrast media volumes, utility of combined PET/MRI in the evaluation of cardiac sarcoidosis, the prognostic value of left atrial late gadolinium enhancement at MRI in predicting incident atrial fibrillation, the utility of an artificial intelligence tool to optimize detection of incidental pulmonary embolism, and standardization of medical terminology for cardiac CT. Ongoing research and future directions include evaluation of novel PET tracers for assessment of myocardial fibrosis, deployment of AI tools in clinical cardiovascular imaging workflows, and growing awareness of the need to improve environmental sustainability in imaging. Keywords: Coronary CT, Photon-counting Detector CT, PET/MRI, Cardiac MRI, Congenital Heart Disease, Vascular Imaging, Thoracic Imaging, Artificial Intelligence, Health Services Research © 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.

Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved in implementing AI in cardiovascular imaging are highly diverse, varying by imaging modality, patient subtype, features to be extracted and analyzed, and clinical application. This article establishes a framework that defines value from an organizational perspective, followed by value chain analysis to identify the activities in which AI might produce the greatest incremental value creation. The various perspectives that should be considered are highlighted, including clinicians, imagers, hospitals, patients, and payers. Integrating the perspectives of all health care stakeholders is critical for creating value and ensuring the successful deployment of AI tools in a real-world setting. Different AI tools are summarized, along with the unique aspects of AI applications to various cardiac imaging modalities, including cardiac computed tomography, magnetic resonance imaging, and positron emission tomography. AI is applicable and has the potential to add value to cardiovascular imaging at every step along the patient journey, from selecting the more appropriate test to optimizing image acquisition and analysis, interpreting the results for classification and diagnosis, and predicting the risk for major adverse cardiac events.

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.)

Purpose: To develop and assess the utility of synthetic dual-energy CT (sDECT) images generated from single-energy CT (SECT) using two state-of-the-art generative adversarial network (GAN) architectures for artificial intelligence-based image translation., Methods: In this retrospective study, 734 patients (389F; 62.8 years ± 14.9) who underwent enhanced DECT of the chest, abdomen, and pelvis between January 2018 and June 2019 were included. Using 70-keV as the input images (n = 141,009) and 50-keV, iodine, and virtual unenhanced (VUE) images as outputs, separate models were trained using Pix2PixHD and CycleGAN. Model performance on the test set (n = 17,839) was evaluated using mean squared error, structural similarity index, and peak signal-to-noise ratio. To objectively test the utility of these models, synthetic iodine material density and 50-keV images were generated from SECT images of 16 patients with gastrointestinal bleeding performed at another institution. The conspicuity of gastrointestinal bleeding using sDECT was compared to portal venous phase SECT. Synthetic VUE images were generated from 37 patients who underwent a CT urogram at another institution and model performance was compared to true unenhanced images., Results: sDECT from both Pix2PixHD and CycleGAN were qualitatively indistinguishable from true DECT by a board-certified radiologist (avg accuracy 64.5%). Pix2PixHD had better quantitative performance compared to CycleGAN (e.g., structural similarity index for iodine: 87% vs. 46%, p-value < 0.001). sDECT using Pix2PixHD showed increased bleeding conspicuity for gastrointestinal bleeding and better removal of iodine on synthetic VUE compared to CycleGAN., Conclusions: sDECT from SECT using Pix2PixHD may afford some of the advantages of DECT., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The left atrial auricle (LAA) is the main source of intracardiac thrombi, which contribute significantly to the total number of stroke cases. It is also considered a major site of origin for atrial fibrillation in patients undergoing ablation procedures. The LAA is known to have a high degree of morphological variability, with shape and structure identified as important contributors to thrombus formation. A detailed understanding of LAA form, dimension, and function is crucial for radiologists, cardiologists, and cardiac surgeons. This review describes the normal anatomy of the LAA as visualized through multiple imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI), and echocardiography. Special emphasis is devoted to a discussion on how the morphological characteristics of the LAA are closely related to the likelihood of developing LAA thrombi, including insights into LAA embryology., 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., (© 2023 The Authors. Published by Elsevier Ltd.)

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.)

Since its inaugural issue in 2019, Radiology: Cardiothoracic Imaging has disseminated the latest scientific advances and technical developments in cardiac, vascular, and thoracic imaging. In this review, we highlight select articles published in this journal between October 2021 and October 2022. The scope of the review encompasses various aspects of coronary artery and congenital heart diseases, vascular diseases, thoracic imaging, and health services research. Key highlights include changes in the revised Coronary Artery Disease Reporting and Data System 2.0, the value of coronary CT angiography in informing prognosis and guiding treatment decisions, cardiac MRI findings after COVID-19 vaccination or infection, high-risk features at CT angiography to identify patients with aortic dissection at risk for late adverse events, and CT-guided fiducial marker placement for preoperative planning for pulmonary nodules. Ongoing research and future directions include photon-counting CT and artificial intelligence applications in cardiovascular imaging. Keywords: Pediatrics, CT Angiography, CT-Perfusion, CT-Spectral Imaging, MR Angiography, PET/CT, Transcatheter Aortic Valve Implantation/Replacement (TAVI/TAVR), Cardiac, Pulmonary, Vascular, Aorta, Coronary Arteries © RSNA, 2023., Competing Interests: Disclosures of conflicts of interest: D.M. Grants or contracts 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. G.J.A. Member of Radiology: Cardiothoracic Imaging trainee editorial board. K.G.O. Payment or honoraria from Grand Rounds for lectures, presentations, speakers bureaus, manuscript writing, or educational events; president of the Society of Cardiovascular Magnetic Resonance; associate editor for Radiology: Cardiothoracic Imaging. D.V. Treasurer of the North American Society for Cardiovascular Imaging; member of Radiology: Cardiothoracic Imaging editorial board; mentor for Radiology: Cardiothoracic Imaging trainee editorial board. D.F. Deputy editor for Radiology: Cardiothoracic Imaging. S.A. Royalties from Elsevier for textbook authorship; member of the board of directors of the Society of Cardiovascular Computed Tomography; editor of Radiology: Cardiothoracic Imaging. K.H. Payment or honoraria from Sanofi Genzyme for lectures, presentations, speakers bureaus, manuscript writing, or educational events; associate editor and trainee editorial board lead for Radiology: Cardiothoracic Imaging., (© 2023 by the Radiological Society of North America, Inc.)

Oncotype Dx Recurrence Score (RS) has been validated in patients with ER + /HER2 - invasive breast carcinoma to estimate patient risk of recurrence and guide the use of adjuvant chemotherapy. We investigated the role of MRI-based radiomics features extracted from the tumor and the peritumoral tissues to predict the risk of tumor recurrence. A total of 62 patients with biopsy-proved ER + /HER2 - breast cancer who underwent pre-treatment MRI and Oncotype Dx were included. An RS > 25 was considered discriminant between low-intermediate and high risk of tumor recurrence. Two readers segmented each tumor. Radiomics features were extracted from the tumor and the peritumoral tissues. Partial least square (PLS) regression was used as the multivariate machine learning algorithm. PLS β-weights of radiomics features included the 5% features with the largest β-weights in magnitude (top 5%). Leave-one-out nested cross-validation (nCV) was used to achieve hyperparameter optimization and evaluate the generalizable performance of the procedure. The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. A null hypothesis probability threshold of 5% was chosen (p < 0.05). The exploratory analysis for the complete dataset revealed an average absolute correlation among features of 0.51. The nCV framework delivered an AUC of 0.76 (p = 1.1∙10 -3 ). When combining "early" and "peak" DCE images of only T or TST, a tendency toward statistical significance was obtained for TST with an AUC of 0.61 (p = 0.05). The 47 features included in the top 5% were balanced between T and TST (23 and 24, respectively). Moreover, 33/47 (70%) were texture-related, and 25/47 (53%) were derived from high-resolution images (1 mm). A radiomics-based machine learning approach shows the potential to accurately predict the recurrence risk in early ER + /HER2 - breast cancer patients., (© 2023. The Author(s).)

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.)

Background: Coronary artery calcium (CAC) can be identified on nongated chest computed tomography (CT) scans, but this finding is not consistently incorporated into care. A deep learning algorithm enables opportunistic CAC screening of nongated chest CT scans. Our objective was to evaluate the effect of notifying clinicians and patients of incidental CAC on statin initiation., Methods: NOTIFY-1 (Incidental Coronary Calcification Quality Improvement Project) was a randomized quality improvement project in the Stanford Health Care System. Patients without known atherosclerotic cardiovascular disease or a previous statin prescription were screened for CAC on a previous nongated chest CT scan from 2014 to 2019 using a validated deep learning algorithm with radiologist confirmation. Patients with incidental CAC were randomly assigned to notification of the primary care clinician and patient versus usual care. Notification included a patient-specific image of CAC and guideline recommendations regarding statin use. The primary outcome was statin prescription within 6 months., Results: Among 2113 patients who met initial clinical inclusion criteria, CAC was identified by the algorithm in 424 patients. After chart review and additional exclusions were made, a radiologist confirmed CAC among 173 of 194 patients (89.2%) who were randomly assigned to notification or usual care. At 6 months, the statin prescription rate was 51.2% (44/86) in the notification arm versus 6.9% (6/87) with usual care ( P <0.001). There was also more coronary artery disease testing in the notification arm (15.1% [13/86] versus 2.3% [2/87]; P =0.008)., Conclusions: Opportunistic CAC screening of previous nongated chest CT scans followed by clinician and patient notification led to a significant increase in statin prescriptions. Further research is needed to determine whether this approach can reduce atherosclerotic cardiovascular disease events., Registration: URL: https://www., Clinicaltrials: gov; Unique identifier: NCT04789278.

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 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.)

Chronic aortic dissection comprises a heterogeneous group of unrepaired and repaired disease requiring lifelong clinical and imaging surveillance. CT and MRI are the main imaging modalities for longitudinal surveillance, with growing interest in emerging imaging techniques for prognostic potential. Imaging difficulties span technical and diagnostic challenges, some of which are unique to the repaired aorta, with specific complications depending on the type of repair. This review describes existing and emerging imaging techniques, outlines the technical and diagnostic challenges encountered at CT and MRI, and highlights the diagnostic pitfalls of chronic aortic dissection., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Aortic dissection is a chronic disease that requires lifelong clinical and imaging surveillance, long after the acute event. Imaging has an important role in prognosis, timing of repair, device sizing, and monitoring for complications, especially in the endovascular therapy era. Important anatomic features at preprocedural imaging include the location of the primary intimal tear and aortic zonal and branch vessel involvement, which influence the treatment strategy. Challenges of repair in the chronic phase include a small true lumen in conjunction with a stiff intimal flap, complex anatomy, and retrograde perfusion from distal reentry tears. The role of thoracic endovascular aortic repair (TEVAR) remains controversial for treatment of chronic aortic dissection. Standard TEVAR is aimed at excluding the primary intimal tear to decrease false lumen perfusion, induce false lumen thrombosis, promote aortic remodeling, and prevent aortic growth. In addition to covering the primary intimal tear with an endograft, several adjunctive techniques have been developed to mitigate retrograde false lumen perfusion. These techniques are broadly categorized into false lumen obliteration and landing zone optimization strategies, such as the provisional extension to induce complete attachment (PETTICOAT), false lumen embolization, cheese-wire fenestration, and knickerbocker techniques. Familiarity with these techniques is important to recognize expected changes and complications at postintervention imaging. The authors detail imaging options, provide examples of simple and complex endovascular repairs of aortic dissections, and highlight complications that can be associated with various techniques. Online supplemental material is available for this article. © RSNA, 2022.

Congenital heart diseases (CHD) represent a major clinical and diagnostic challenge for correct abnormality identification and subsequent successful therapy; even more challenging is following-up patient health after multiple post-interventional corrections often required in complex cardio-vascular abnormalities. We describe a multi-modality imaging evaluation of a complex congenital cardio-vascular diseases, underlining the relevance of cardiac magnetic resonance to non invasively solve some issues related to postsurgical changes.

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: To (1) compare bladder cancer (BC) muscle invasiveness among three b-values using a contrast-free approach based on Vesical Imaging-Reporting and Data System (VI-RADS), to (2) determine if muscle-invasiveness assessment is affected by the reader experience, and to (3) compare BC conspicuity among three b-values, qualitatively and quantitatively., Methods: Thirty-eight patients who underwent a bladder MRI on a 3.0-T scanner were enrolled. The gold standard was histopathology report following transurethral resection of BC. Three sets of images, including T2w and different b-values for DWI, set 1 (b = 1000 s/mm 2 ), set 2 (b = 1500 s/mm 2 ), and set 3 (b = 2000 s/mm 2 ), were reviewed by three differently experienced readers. Descriptive statistics and Intraclass Correlation Coefficient (ICC) were calculated. Comparisons among readers and DWI sets were performed with the Wilcoxon test. Receiver operating characteristic (ROC) analysis was performed. Areas under the curves (AUCs) and pairwise comparison were calculated., Results: AUCs of muscle-invasiveness assessment ranged from 0.896 to 0.984 (reader 1), 0.952-0.968 (reader 2), and 0.952-0.984 (reader 3) without significant differences among different sets and readers (p > 0.05). The mean conspicuity qualitative scores were higher in Set 1 (2.21-2.33), followed by Set 2 (2-2.16) and Set 3 (1.82-2.14). The quantitative conspicuity assessment showed that mean normalized intensity of tumor was significantly higher in Set 2 (4.217-4.737) than in Set 1 (3.923-4.492) and Set 3 (3.833-3.992) (p < 0.05)., Conclusion: Muscle invasiveness can be assessed with high accuracy using a contrast-free protocol with T2W and DWI, regardless of reader's experience. b = 1500 s/mm 2 showed the best tumor delineation, while b = 1000 s/mm 2 allowed for better tumor-wall interface assessment., (© 2022. The Author(s).)

"Vieussens' ring" or "arterial circle of Vieussens" is a crucial hetero-coronaric pathway, bridging proximal right coronary artery (RCA) and left anterior descending artery (LAD) when a hemodynamically stenosis is established in the either of the vessel. In detail such coronary collateral circulation is usually supplied by branches of the conus artery. We present a case of a 62-year-old man who was admitted to our emergency department complaining of chest pain. Coronary angiography showed LAD occlusion at the mid tract with delayed and slight opacification of its distal segment sustained by Vieussens' ring. Coronary computed tomography angiography (CCTA) was subsequently performed which confirmed the presence of such natural bypass and evaluated its relationship with adjacent structures. Imaging, particularly CCTAoffers a valid tool in assessing the hetero-coronaric collateral vessel. Due to its high spatial resolution it may provide many information about the coronary anatomy by delineating their origin, course and termination.

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: 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.)

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.

The Liver Imaging Reporting and Data System (LI-RADS) is a recently developed classification aiming to improve the standardization of liver imaging assessment in patients at risk of developing hepatocellular carcinoma (HCC). The LI-RADS v2017 implemented new algorithms for ultrasound (US) screening and surveillance, contrast-enhanced US diagnosis and computed tomography/magnetic resonance imaging treatment response assessment. A minor update of LI-RADS was released in 2018 to comply with the American Association for the Study of the Liver Diseases guidance recommendations. The scope of this review is to provide a practical overview of LI-RADS v2018 focused both on the multimodality HCC diagnosis and treatment response assessment.

Objectives: Transthoracic echocardiography (TTE) is the standard technique for assessing aortic stenosis (AS), with effective orifice area (EOA) recommended for grading severity. EOA is operator-dependent, influenced by a number of pitfalls and requires multiple measurements introducing independent and random sources of error. We tested the diagnostic accuracy and precision of aliased orifice area planimetry (AOA cmr ), a new, simple, non-invasive technique for grading of AS severity by low-VENC phase-contrast cardiovascular magnetic resonance (CMR) imaging. Methods: Twenty-two consecutive patients with mild, moderate, or severe AS and six age- and sex-matched healthy controls had TTE and CMR examinations on the same day. We performed analysis of agreement and correlation among (i) AOA cmr ; (ii) geometric orifice area (GOA cmr ) by direct CMR planimetry; (iii) EOA echo by TTE-continuity equation; and (iv) the "gold standard" multimodality EOA (EOA hybrid ) obtained by substituting CMR LVOT area into Doppler continuity equation. Results: There was excellent pairwise positive linear correlation among AOA cmr , EOA hybrid , GOA cmr , and EOA echo ( p < 0.001); AOA cmr had the highest correlation with EOA hybrid ( R 2 = 0.985, p < 0.001). There was good agreement between methods, with the lowest bias (0.019) for the comparison between AOA cmr and EOA hybrid . AOA cmr yielded excellent intra- and inter-rater reliability (intraclass correlation coefficient: 0.997 and 0.998, respectively). Conclusions: Aliased orifice area planimetry by 2D phase contrast imaging is a simple, reproducible, accurate "one-stop shop" CMR method for grading AS, potentially useful when echocardiographic severity assessment is inconclusive or discordant. Larger studies are warranted to confirm and validate these promising preliminary results., Competing Interests: The 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 © 2021 Mantini, Khanji, D'Ugo, Olivieri, Caputi, Bufano, Mastrodicasa, Calvo Garcia, Rotondo, Candeloro, Tana, Cademartiri, Ionescu, Caulo, Gallina and Ricci.)

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.)

Purpose : To differentiate oncocytoma and chromophobe renal cell carcinoma (RCC) using radiomics features computed from spherical samples of image regions of interest, "radiomic biopsies" (RBs). Approach : In a retrospective cohort study of 102 CT cases [68 males (67%), 34 females (33%); mean age ± SD, 63 ± 12    years ], we pathology-confirmed 42 oncocytomas (41%) and 60 chromophobes (59%). A board-certified radiologist performed two RB rounds. From each RB round, we computed radiomics features and compared the performance of a random forest and AdaBoost binary classifier trained from the features. To control for overfitting, we performed 10 rounds of 70% to 30% train-test splits with feature-selection, cross-validation, and hyperparameter-optimization on each split. We evaluated the performance with test ROC AUC. We tested models on data from the other RB round and compared with the same round testing with the DeLong test. We clustered important features for each round and measured a bootstrapped adjusted Rand index agreement. Results : Our best classifiers achieved an average AUC of 0.71 ± 0.024 . We found no evidence of an effect for RB round ( p = 1 ). We also found no evidence for a decrease in model performance when tested on the other RB round ( p = 0.85 ). Feature clustering produced seven clusters in each RB round with high agreement ( Rand index = 0.981 ± 0.002 , p < 0.00001 ). Conclusions : A consistent radiomic signature can be derived from RBs and could help distinguish oncocytoma and chromophobe RCC., (© 2021 Society of Photo-Optical Instrumentation Engineers (SPIE).)

Ground-glass opacities (GGOs) are a non-specific high-resolution computed tomography (HRCT) finding tipically observed in early Coronavirus disesase 19 (COVID-19) pneumonia. However, GGOs are also seen in other acute lung diseases, thus making challenging the differential diagnosis. To this aim, we investigated the performance of a radiomics-based machine learning method to discriminate GGOs due to COVID-19 from those due to other acute lung diseases. Two sets of patients were included: a first set of 28 patients (COVID) diagnosed with COVID-19 infection confirmed by real-time polymerase chain reaction (RT-PCR) between March and April 2020 having (a) baseline HRCT at hospital admission and (b) predominant GGOs pattern on HRCT; a second set of 30 patients (nCOVID) showing (a) predominant GGOs pattern on HRCT performed between August 2019 and April 2020 and (b) availability of final diagnosis. Two readers independently segmented GGOs on HRCTs using a semi-automated approach, and radiomics features were extracted using a standard open source software (PyRadiomics). Partial least square (PLS) regression was used as the multivariate machine-learning algorithm. A leave-one-out nested cross-validation was implemented. PLS β-weights of radiomics features, including the 5% features with the largest β-weights in magnitude (top 5%), were obtained. The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. The Youden's test assessed sensitivity and specificity of the classification. A null hypothesis probability threshold of 5% was chosen (p < 0.05). The predictive model delivered an AUC of 0.868 (Youden's index = 0.68, sensitivity = 93%, specificity 75%, p = 4.2 × 10 -7 ). Of the seven features included in the top 5% features, five were texture-related. A radiomics-based machine learning signature showed the potential to accurately differentiate GGOs due to COVID-19 pneumonia from those due to other acute lung diseases. Most of the discriminant radiomics features were texture-related. This approach may assist clinician to adopt the appropriate management early, while improving the triage of patients., (© 2021. The Author(s).)