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We argue that the dominant approach to explainable AI for explaining image classification, annotating images with heatmaps, provides little value for users unfamiliar with deep learning. We argue that explainable AI for images should produce output like experts produce when communicating with one another, with apprentices, and with novices. We provide an expanded set of goals of explainable AI systems and propose a Turing Test for explainable AI.

PURPOSE: To assess the feasibility of a newly developed algorithm, called deep learning synthetic strain (DLSS), to infer myocardial velocity from cine steady-state free precession (SSFP) images and detect wall motion abnormalities in patients with ischemic heart disease. MATERIALS AND METHODS: In this retrospective study, DLSS was developed by using a data set of 223 cardiac MRI examinations including cine SSFP images and four-dimensional flow velocity data (November 2017 to May 2021). To establish normal ranges, segmental strain was measured in 40 individuals (mean age, 41 years ± 17 [SD]; 30 men) without cardiac disease. Then, DLSS performance in the detection of wall motion abnormalities was assessed in a separate group of patients with coronary artery disease, and these findings were compared with consensus results of four independent cardiothoracic radiologists (ground truth). Algorithm performance was evaluated by using receiver operating characteristic curve analysis. RESULTS: Median peak segmental radial strain in individuals with normal cardiac MRI findings was 38% (IQR: 30%-48%). Among patients with ischemic heart disease (846 segments in 53 patients; mean age, 61 years ± 12; 41 men), the Cohen κ among four cardiothoracic readers for detecting wall motion abnormalities was 0.60-0.78. DLSS achieved an area under the receiver operating characteristic curve of 0.90. Using a fixed 30% threshold for abnormal peak radial strain, the algorithm achieved a sensitivity, specificity, and accuracy of 86%, 85%, and 86%, respectively. CONCLUSION: The deep learning algorithm had comparable performance with subspecialty radiologists in inferring myocardial velocity from cine SSFP images and identifying myocardial wall motion abnormalities at rest in patients with ischemic heart disease.Keywords: Neural Networks, Cardiac, MR Imaging, Ischemia/Infarction Supplemental material is available for this article. © RSNA, 2023.

Management of chronic thromboembolic pulmonary hypertension (CTEPH) should be determined by a multidisciplinary team, ideally at a specialized CTEPH referral center. Radiologists contribute to this multidisciplinary process by helping to confirm the diagnosis of CTEPH and delineating the extent of disease, both of which help determine a treatment decision. Preoperative assessment of CTEPH usually employs multiple imaging modalities, including ventilation-perfusion (V/Q) scanning, echocardiography, CT pulmonary angiography (CTPA), and right heart catheterization with pulmonary angiography. Accurate diagnosis or exclusion of CTEPH at imaging is imperative, as this remains the only form of pulmonary hypertension that is curative with surgery. Unfortunately, CTEPH is often misdiagnosed at CTPA, which can be due to technical factors, patient-related factors, radiologist-related factors, as well as a host of disease mimics including acute pulmonary embolism, in situ thrombus, vasculitis, pulmonary artery sarcoma, and fibrosing mediastinitis. Although V/Q scanning is thought to be substantially more sensitive for CTEPH compared with CTPA, this is likely due to lack of recognition of CTEPH findings rather than a modality limitation. Preoperative evaluation for pulmonary thromboendarterectomy (PTE) includes assessment of technical operability and surgical risk stratification. While the definitive therapy for CTEPH is PTE, other minimally invasive or noninvasive therapies also lead to clinical improvements including greater survival. Complications of PTE that can be identified at postoperative imaging include infection, reperfusion edema or injury, pulmonary hemorrhage, pericardial effusion or hemopericardium, and rethrombosis.

Scoliosis is a condition of abnormal lateral spinal curvature affecting an estimated 2 to 3% of the US population, or seven million people. The Cobb angle is the standard measurement of spinal curvature in scoliosis but is known to have high interobserver and intraobserver variability. Thus, the objective of this study was to build and validate a system for automatic quantitative evaluation of the Cobb angle and to compare AI generated and human reports in the clinical setting. After IRB was obtained, we retrospectively collected 2150 frontal view scoliosis radiographs at a tertiary referral center (January 1, 2019, to January 1, 2021, ≥ 16years old, no hardware). The dataset was partitioned into 1505 train (70%), 215 validation (10%), and 430 test images (20%). All thoracic and lumbar vertebral bodies were segmented with bounding boxes, generating approximately 36,550 object annotations that were used to train a Faster R-CNN Resnet-101 object detection model. A controller algorithm was written to localize vertebral centroid coordinates and derive the Cobb properties (angle and endplate) of dominant and secondary curves. AI-derived Cobb angle measurements were compared to the clinical report measurements, and the Spearman rank-order demonstrated significant correlation (0.89, p

Supplemental material is available for this article.

To investigate the utility and generalizability of deep learning subtraction angiography (DLSA) for generating synthetic digital subtraction angiograms (DSA) without misalignment artifacts.DSA images and native digital angiograms of the cerebral, hepatic, and splenic vasculature, both with and without motion artifacts, were retrospectively collected. Images were divided into a motion-free training set (n=66 patients, 9,161 images) and a motion artifact-containing test set (n=22 patients, 3,322 images). The deep neural network pix2pix was trained to produce synthetic DSA images without misalignment artifacts directly from native digital angiograms using motion-free DSA images of the cerebral and hepatic vasculature as ground truth. After training, the algorithm was tested on digital angiograms of hepatic and splenic vasculature with substantial motion. Four board-certified radiologists evaluated performance via visual assessment with a five-grade Likert scale. Subgroup analyses were performed to analyze the impact of transfer learning and the ability of this approach to generalize to novel vasculature.Compared to the traditional DSA method, the proposed approach was found to generate synthetic DSAs with significantly fewer background artifacts (mean rating of 1.9 [95% CI 1.1-2.6] vs. 3.5 [95% CI 3.5-4.4]; p=0.01), without a significant difference in foreground vascular detail (mean rating of 3.1 [95% CI 2.6-3.5] vs. 3.3 [95% CI 2.8-3.8]; p=0.19) in both the hepatic and splenic vasculature. Transfer learning significantly improved the quality of generated images (p0.001).DLSA successfully generates synthetic angiograms without misalignment artifacts, is improved through transfer learning, and generalizes reliably to novel vasculature that was not included in the training data.

Stereotactic radiosurgery (SRS) is used to treat cerebral arteriovenous malformations (AVMs). However, early evaluation of efficacy is difficult as structural magnetic resonance imaging (MRI)/magnetic resonance angiography (MRA) often does not demonstrate appreciable changes within the first 6 months. The aim of this study was to evaluate the use of four-dimensional (4D) flow MRI to quantify hemodynamic changes after SRS as early as 2 months. This was a retrospective observational study, which included 14 patients with both pre-SRS and post-SRS imaging obtained at multiple time points from 1 to 27 months after SRS. A 3T MRI Scanner was used to obtain T2 single-shot fast spin echo, time-of-flight MRA, and postcontrast 4D flow with three-dimensional velocity encoding between 150 and 200 cm/s. Post-hoc two-dimensional cross-sectional flow was measured for the dominant feeding artery, the draining vein, and the corresponding contralateral artery as a control. Measurements were performed by two independent observers, and reproducibility was assessed. Wilcoxon signed-rank tests were used to compare differences in flow, circumference, and pulsatility between the feeding artery and the contralateral artery both before and after SRS; and differences in nidus size and flow and circumference of the feeding artery and draining vein before and after SRS. Arterial flow (L/min) decreased in the primary feeding artery (mean: 0.1 ± 0.07 vs. 0.3 ± 0.2; p < 0.05) and normalized in comparison to the contralateral artery (mean: 0.1 ± 0.07 vs. 0.1 ± 0.07; p = 0.068). Flow decreased in the draining vein (mean: 0.1 ± 0.2 vs. 0.2 ± 0.2; p < 0.05), and the circumference of the draining vein also decreased (mean: 16.1 ± 8.3 vs. 15.7 ± 6.7; p < 0.05). AVM volume decreased after SRS (mean: 45.3 ± 84.8 vs. 38.1 ± 78.7; p < 0.05). However, circumference (mm) of the primary feeding artery remained similar after SRS (mean: 15.7 ± 2.7 vs. 16.1 ± 3.1; p = 0.600). 4D flow may be able to demonstrate early hemodynamic changes in AVMs treated with radiosurgery, and these changes appear to be more pronounced and occur earlier than the structural changes on standard MRI/MRA. Level of Evidence: 4 Technical Efficacy Stage: 1.

Background Postural orthostatic tachycardia syndrome (POTS), Ehlers-Danlos syndrome (EDS), and May-Thurner syndrome (MTS) are three syndromes that are often misdiagnosed or underdiagnosed. The true prevalence of these syndromes may be higher than currently reported. The following case series is the first to report a three-way association between POTS, EDS, and MTS. Case summary We describe three patients with concomitant POTS, EDS, and MTS. Although abdominopelvic vasculature evaluation can be difficult via conventional imaging techniques, we present the use of novel dynamic contrast-enhanced magnetic resonance angiography with Differential Subsampling with Cartesian Ordering (DISCO) and four-dimensional flow magnetic resonance imaging to aid vasculature evaluation and the diagnosis of MTS. Two patients underwent left common iliac vein stenting to treat MTS, experiencing significant improvement in their POTS symptoms and quality of life. Discussion Ehlers-Danlos syndrome, POTS, and MTS may interact synergistically to exacerbate symptoms. Patients with EDS should be evaluated for possible POTS and pelvic venous complications. Left common iliac vein stenting for MTS can mitigate POTS symptoms by decreasing lower extremity venous pooling and should be considered in this patient population. Further research is needed to understand the exact mechanism and intricacies of this syndrome triad.

Quantitative imaging measurements can be facilitated by artificial intelligence (AI) algorithms, but how they might impact decision-making and be perceived by radiologists remains uncertain. After creation of a dedicated inspiratory-expiratory CT examination and concurrent deployment of a quantitative AI algorithm for assessing air trapping, five cardiothoracic radiologists retrospectively evaluated severity of air trapping on 17 examination studies. Air trapping severity of each lobe was evaluated in three stages: qualitatively (visually); semiquantitatively, allowing manual region-of-interest measurements; and quantitatively, using results from an AI algorithm. Readers were surveyed on each case for their perceptions of the AI algorithm. The algorithm improved interreader agreement (intraclass correlation coefficients: visual, 0.28; semiquantitative, 0.40; quantitative, 0.84; P < .001) and improved correlation with pulmonary function testing (forced expiratory volume in 1 second–to–forced vital capacity ratio) (visual r = −0.26, semiquantitative r = −0.32, quantitative r = −0.44). Readers perceived moderate agreement with the AI algorithm (Likert scale average, 3.7 of 5), a mild impact on their final assessment (average, 2.6), and a neutral perception of overall utility (average, 3.5). Though the AI algorithm objectively improved interreader consistency and correlation with pulmonary function testing, individual readers did not immediately perceive this benefit, revealing a potential barrier to clinical adoption. Keywords: Technology Assessment, Quantification © RSNA, 2021

Purpose To assess the potential of a transfer learning strategy leveraging radiologist supervision to enhance convolutional neural network-based (CNN) localization of pneumonia on radiographs and to further assess the prognostic value of CNN severity quantification on patients evaluated for COVID-19 pneumonia, for whom severity on the presenting radiograph is a known predictor of mortality and intubation. Materials and methods We obtained an initial CNN previously trained to localize pneumonia along with 25,684 radiographs used for its training. We additionally curated 1466 radiographs from patients who had a computed tomography (CT) performed on the same day. Regional likelihoods of pneumonia were then annotated by cardiothoracic radiologists, referencing these CTs. Combining data, a preexisting CNN was fine-tuned using transfer learning. Whole-image and regional performance of the updated CNN was assessed using receiver-operating characteristic area under the curve and Dice. Finally, the value of CNN measurements was assessed with survival analysis on 203 patients with COVID-19 and compared against modified radiographic assessment of lung edema (mRALE) score. Results Pneumonia detection area under the curve improved on both internal (0.756 to 0.841) and external (0.864 to 0.876) validation data. Dice overlap also improved, particularly in the lung bases (R: 0.121 to 0.433, L: 0.111 to 0.486). There was strong correlation between radiologist mRALE score and CNN fractional area of involvement (ρ=0.85). Survival analysis showed similar, strong prognostic ability of the CNN and mRALE for mortality, likelihood of intubation, and duration of hospitalization among patients with COVID-19. Conclusions Radiologist-supervised transfer learning can enhance the ability of CNNs to localize and quantify the severity of disease. Closed-loop systems incorporating radiologists may be beneficial for continued improvement of artificial intelligence algorithms.

CT pulmonary angiography (CTPA) is the first-line imaging test for evaluation of acute pulmonary emboli. However, diagnostic quality is heterogeneous across institutions and is frequently limited by suboptimal pulmonary artery (PA) contrast enhancement. In this retrospective study, a deep learning algorithm for measuring enhancement of the central PAs was developed and assessed for feasibility of its use in quality improvement of CTPA. In a convenience sample of 450 patients, automated measurement of CTPA enhancement showed high agreement with manual radiologist measurement (r = 0.996). Using a threshold of less than 250 HU for suboptimal enhancement, the sensitivity and specificity of the automated classification were 100% and 99.5%, respectively. The algorithm was further evaluated in a random sampling of 3195 CTPA examinations from January 2019 through May 2021. Beginning in January 2021, the scanning protocol was transitioned from bolus tracking to a timing bolus strategy. Automated analysis of these examinations showed that most suboptimal examinations following the change in protocol were performed using one scanner, highlighting the potential value of deep learning algorithms for quality improvement in the radiology department. Keywords: CT Angiography, Pulmonary Arteries © RSNA, 2022.

Study Objectives: The surge and long tail of patients in acute respiratory distress during the coronavirus-19 (CoVID19) pandemic has inspired new innovations in diagnosing, treating and dispositioning patients during high census conditions with constrained resources During the first wave of the pandemic, we deployed an artificial intelligence (AI) algorithm for assisted interpretation of chest x-ray for use by radiologists and emergency department (ED) physicians We report first experiences of physician interaction with this novel AI algorithm designed to enhance physician abilities to identify ground glass and consolidation on chest radiographs Methods: Design: We created a fully-automated pipeline into the clinical environment to provide AI augmentation of chest x-rays, utilizing a previously developed deep learning-based AI algorithm Trained with 22,000 annotations by radiologists, the algorithm overlays X-rays with color-coded maps that indicate pneumonia probability This was provided alongside standard chest x-ray images for physicians to use in real-time at the point of care with existing imaging software For this prospective observational study, we developed a 3-point survey to characterize experiences with the tool regarding ease of use and impact on clinical decision-making Setting: Surveys were conducted during a one-month period surrounding the projected CoVID-19 surge locally (April 8-May 9) at two academic hospitals in Southern California A federal declaration of emergency occurred March 13, 2020 and the tool was urgently deployed on March 25 Types of Participants: Emergency medicine resident and attending physicians surveyed in real time by telephone Results: Of the 5,125 total visits and 1,960 chest radiographs obtained in the ED during the study period, 1,855 were analyzed by the algorithm Among these, emergency physicians were surveyed for their experiences on 202 Real-time computation and delivery of the tool took four minutes on average Overall, 86% either strongly agreed or somewhat agreed that the intervention was easy to use in the existing workflow 20% of all respondents reported that the algorithm impacted their clinical decision making In general, resident physicians found the AI implementation easier to use than attendings (Mann Whitney U, p=0 005) Descriptive statistics regarding further impact are summarized below (table 1) Conclusion: This AI technology was rapidly deployed in a large academic health system in the first wave of a global pandemic Surveyed ED physicians found this implementation easy to use within existing workflows Twenty percent of physicians reported that the tool changed clinical decision making, and approximately one third of those found that it impacted diagnostic testing decisions and treatment plans Several physicians reported ordering COVID-19 PCR testing as a direct result of the AI, resulting in positive tests and subsequent quarantining of patients who otherwise might not have been appropriately diagnosed To our knowledge, this is the first published study evaluating the impact of medical imaging AI on clinical decision making in the ED setting and may prove to be a powerful tool during the pandemic response [Formula presented]

Understanding of coronary sinus (CS) anatomy and abnormalities is of critical importance due to their use in interventional procedures. Herein, the authors report a rare case of an asymptomatic 72-year-old man with a left circumflex coronary artery–to-CS fistula, together with CS ostial atresia and persistent left superior vena cava. These findings are described using both cardiac CT angiography and MRI with four-dimensional flow for anatomic and functional assessment. Keywords: Cardiac, Coronary Sinus, Aneurysms, Fistula, CT Angiography, MR Imaging Supplemental material is available for this article. © RSNA, 2022

PURPOSE: To assess the ability of four-dimensional (4D) flow MRI to quantify flow volume of the Fontan circuit, including the frequency and hemodynamic contribution of systemic-to-pulmonary venovenous collateral vessels. MATERIALS AND METHODS: In this retrospective study, patients with Fontan circulation were included from three institutions (2017–2021). Flow measurements were performed at several locations along the circuit by two readers, and collateral shunt volumes were quantified. The frequency of venovenous collaterals and structural defects were tabulated from concurrent MR angiography, contemporaneous CT, or catheter angiography and related to Fontan clinical status. Statistical analysis included Pearson and Spearman correlation and Bland-Altman analysis. RESULTS: Seventy-five patients (mean age, 20 years; range, 5–58 years; 46 female and 29 male patients) were included. Interobserver agreement was high for aortic output, pulmonary arteries, pulmonary veins, superior vena cava (Glenn shunt), and inferior vena cava (Fontan conduit) (range, ρ = 0.913–0.975). Calculated shunt volume also showed strong agreement, on the basis of the difference between aortic and pulmonary flow (ρ = 0.935). A total of 37 of 75 (49%) of the patients exhibited shunts exceeding 1.00 L/min, 81% (30 of 37) of whom had pulmonary venous or atrial flow volume step-ups and corresponding venovenous collaterals. A total of 12% of patients (nine of 75) exhibited a high-output state (>4 L/min/m(2)), most of whom had venovenous shunts exceeding 30% of cardiac output. CONCLUSION: Fontan flow and venovenous shunting can be reliably quantified at 4D flow MRI; high-output states were found in a higher proportion of patients than expected, among whom venovenous collaterals were common and constituted a substantial proportion of cardiac output. Keywords: Pediatrics, MR Angiography, Cardiac, Technology Assessment, Hemodynamics/Flow Dynamics, Congenital Supplemental material is available for this article. © RSNA, 2021

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

PURPOSE OF REVIEW Discuss foundational concepts for artificial intelligence (AI) and review recent literature on its application to aortic disease. RECENT FINDINGS Machine learning (ML) techniques are rapidly evolving for the evaluation of aortic disease - broadly categorized as algorithms for aortic segmentation, detection of pathology, and risk stratification. Advances in deep learning, particularly U-Net architectures, have revolutionized segmentation of the aorta and show potential for monitoring the size of aortic aneurysm and characterizing aortic dissection. These algorithms also facilitate application of more complex technologies including analysis of flow dynamics with 4D Flow magnetic resonance imaging (MRI) and computational simulation of fluid dynamics for aortic coarctation. In addition, AI algorithms have been proposed to assist in 'opportunistic' screening from routine imaging exams, including automated aortic calcification score, which has emerged as a strong predictor of cardiovascular risk. Finally, several ML algorithms are being explored for risk stratification of patients with aortic aneurysm and dissection, in addition to prediction of postprocedural complications. SUMMARY Multiple ML techniques have potential for characterization and risk prediction of aortic aneurysm, dissection, coarctation, and atherosclerotic disease on computed tomography and MRI. This nascent field shows considerable promise with many applications in development and in early preclinical evaluation.

PurposeTo develop a convolutional neural network (CNN)-based deformable lung registration algorithm to reduce computation time and assess its potential for lobar air trapping quantification.Materials and methodsIn this retrospective study, a CNN algorithm was developed to perform deformable registration of lung CT (LungReg) using data on 9118 patients from the COPDGene Study (data collected between 2007 and 2012). Loss function constraints included cross-correlation, displacement field regularization, lobar segmentation overlap, and the Jacobian determinant. LungReg was compared with a standard diffeomorphic registration (SyN) for lobar Dice overlap, percentage voxels with nonpositive Jacobian determinants, and inference runtime using paired t tests. Landmark colocalization error (LCE) across 10 patients was compared using a random effects model. Agreement between LungReg and SyN air trapping measurements was assessed using intraclass correlation coefficient. The ability of LungReg versus SyN emphysema and air trapping measurements to predict Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages was compared using area under the receiver operating characteristic curves.ResultsAverage performance of LungReg versus SyN showed lobar Dice overlap score of 0.91-0.97 versus 0.89-0.95, respectively (P < .001); percentage voxels with nonpositive Jacobian determinant of 0.04 versus 0.10, respectively (P < .001); inference run time of 0.99 second (graphics processing unit) and 2.27 seconds (central processing unit) versus 418.46 seconds (central processing unit) (P < .001); and LCE of 7.21 mm versus 6.93 mm (P < .001). LungReg and SyN whole-lung and lobar air trapping measurements achieved excellent agreement (intraclass correlation coefficients > 0.98). LungReg versus SyN area under the receiver operating characteristic curves for predicting GOLD stage were not statistically different (range, 0.88-0.95 vs 0.88-0.95, respectively; P = .31-.95).ConclusionCNN-based deformable lung registration is accurate and fully automated, with runtime feasible for clinical lobar air trapping quantification, and has potential to improve diagnosis of small airway diseases.Keywords: Air Trapping, Convolutional Neural Network, Deformable Registration, Small Airway Disease, CT, Lung, Semisupervised Learning, Unsupervised Learning Supplemental material is available for this article. © RSNA, 2021 An earlier incorrect version of this article appeared online. This article was corrected on December 22, 2021.

BackgroundBody composition measures, specifically low weight or reduced muscle mass, are associated with mortality in COPD, but the effect of longitudinal body composition changes is undefined.Research questionIs the longitudinal loss of fat-free mass (FFM) associated with increased mortality, including in those with initially normal or elevated body composition metrics?Study design and methodsParticipants with complete data for at least one visit in the COPDGene study (n= 9,268) and the ECLIPSE study (n= 1,760) were included and monitored for 12 and 8 years, respectively. Pectoralis muscle area (PMA) was derived from thoracic CT scans and used as a proxy for FFM. A longitudinal mixed submodel for PMA and a Cox proportional hazards submodel for survival were fitted on a joint distribution, using a shared random intercept parameter and Markov chain Monte Carlo parameter estimation.ResultsBoth cohorts demonstrated a left-shifted distribution of baseline FFM, not reflected in BMI, and an increase in all-cause mortality risk associated with longitudinal loss of PMA. For each 1-cm2 PMA loss, mortality increased 3.1%(95%CI, 2.4%-3.7%; P< .001) in COPDGene, and 2.4%(95%CI, 0.9%-4.0%; P< .001) in ECLIPSE. Increased mortality risk was independent of enrollment values for BMI and disease severity [BODE (body mass, airflow obstruction, dyspnea, and exercise capacity) index quartiles] and was significant even in participants with initially greater than average PMA.InterpretationLongitudinal loss of PMA is associated with increased all-cause mortality, regardless of BMI or initial muscle mass. Consideration of novel screening tests and further research into mechanisms contributing to muscle decline may improve risk stratification and identify novel therapeutic targets in ever smokers.

MRI is an essential diagnostic tool in the anatomic and functional evaluation of cardiovascular disease. In many practices, 2D phase-contrast (2D-PC) MRI has been used for blood flow quantification. Four-dimensional flow MRI is a time-resolved volumetric acquisition that captures the vector field of blood flow along with anatomic images. It also provides a simpler acquisition compared with 2D-PC and facilitates a more accurate and comprehensive hemodynamic assessment. Advancements in accelerated imaging have significantly shortened scanning times for 4D flow MRI while preserving image quality, enabling this technology to transition from the research arena to routine clinical practice. In this article, we review technical optimization based on our more than 10 years of clinical experience with 4D flow MRI. We also present pearls and pitfalls in the practical application of 4D flow MRI, including how to quantify cardiovascular shunts, valvular or vascular stenosis, and valvular regurgitation. As experience increases, and as 4D flow sequences and postprocessing software become more broadly available, 4D flow MRI will likely become an essential component of cardiac imaging in practices involved in the management of congenital and acquired structural heart disease.

Author(s): Bahrami, Naeim; Retson, Tara; Blansit, Kevin; Wang, Kang; Hsiao, Albert | Abstract: PURPOSE:Delayed enhancement imaging is an essential component of cardiac MRI, which is used widely for the evaluation of myocardial scar and viability. The selection of an optimal inversion time (TI) or null point (TINP ) to suppress the background myocardial signal is required. The purpose of this study was to assess the feasibility of automated selection of TINP using a convolutional neural network (CNN). We hypothesized that a CNN may use spatial and temporal imaging characteristics from an inversion-recovery scout to select TINP , without the aid of a human observer. METHODS:We retrospectively collected 425 clinically acquired cardiac MRI exams performed at 1.5 T that included inversion-recovery scout acquisitions. We developed a VGG19 classifier ensembled with long short-term memory to identify the TINP . We compared the performance of the ensemble CNN in predicting TINP against ground truth, using linear regression analysis. Ground truth was defined as the expert physician annotation of the optimal TI. In a backtrack approach, saliency maps were generated to interpret the classification outcome and to increase the model's transparency. RESULTS:Prediction of TINP from our ensemble VGG19 long short-term memory closely matched with expert annotation (ρ = 0.88). Ninety-four percent of the predicted TINP were within ±36 ms, and 83% were at or after expert TI selection. CONCLUSION:In this study, we show that a CNN is capable of automated prediction of myocardial TI from an inversion-recovery experiment. Merging the spatial and temporal characteristics of the VGG-19 and long short-term-memory CNN structures appears to be sufficient to predict myocardial TI from TI scout.

Pulmonary arteriovenous malformations (AVMs) can be a complication of certain postoperative Fontan patients whose hepatic venous blood return is not distributed evenly to both lungs. A ten-year-old female, who had previously undergone staged single ventricle palliation for complex congenital heart disease, underwent a Fontan revision due to significant left-sided pulmonary AVMs and increasing arterial oxygen desaturation. The combination of four-dimensional flow cardiac magnetic resonance imaging and three-dimensional printing enabled presurgical planning for a Fontan takedown and diversion of hepatic venous flow to the azygous vein that resulted in significant clinical improvement.

PURPOSE: To compare invasive right heart catheterization with four-dimensional (4D) flow MRI for estimating shunt fraction in patients with intracardiac and extracardiac shunts. MATERIALS AND METHODS: In this retrospective study, patients who underwent 4D flow MRI and invasive right heart catheterization with a shunt run between August 2015 and November 2018 were included. The primary objective was comparison of estimated shunt fraction (ratio of pulmonary-to-systemic flow, Q(p)/Q(s)) at 4D flow and catheterization. Secondary objectives included comparison of the right ventricular–to–left ventricular stroke volume ratio (RVSV/LVSV) to shunt fraction (for those with applicable shunts) and comparison of cardiac output between 4D flow and catheterization. Statistical analysis included Pearson correlation and Bland-Altman plots. RESULTS: A total of 33 patients met inclusion criteria (mean age, 49 years ± 16 [standard deviation]; 24 women). 4D flow measurements of Q(p)/Q(s) strongly correlated with those at catheterization (r = 0.938), and there was no bias. RVSV/LVSV correlated strongly with Q(p)/Q(s) from 4D flow (r = 0.852) and catheterization (r = 0.842). Measurements of left ventricle (Q(s)) and right ventricle (Q(P)) cardiac output from 4D flow and catheterization (Fick) correlated moderately overall (r = 0.673 [Q(p)] and r = 0.750 [Q(s)]). CONCLUSION: Shunt fraction measurement using 4D flow MRI compares well with that using invasive cardiac catheterization. Supplemental material is available for this article. © RSNA, 2021