Regional Strain of Right Ventricle From Computed Tomography Improves Risk Stratification of Right Ventricle Failure.

Patients who undergo implantation of a left ventricular assist device (LVAD) are at a high risk for right ventricular failure (RVF), presumably due to poor right ventricular (RV) function before surgery. Cine computerized tomography (cineCT) can be used to evaluate RV size, function, and endocardial strain. However, CT-based strain measures in patients undergoing workup for LVAD implantation have not been evaluated. We quantified RV strain in the free wall (FW) and septal wall (SW) in patients with end-stage heart failure using cineCT. Compared to controls, both FW and SW strains were significantly impaired in heart failure patients. The difference between FW and SW strains predicted RV failure after LVAD implantation (area-under-the curve [AUC] = 0.82). Cine CT strain can be combined with RV volumetry to risk-stratify patients. In our study, patients with preserved RV volumes and poor strain had a higher rate of RV failure (57%), than those with preserved volume and preserved strain (0%). This suggests that CT could improve risk stratification of patients receiving LVADs and that strain metrics were particularly useful in risk-stratifying patients with preserved RV volumes.

The future of CMR: All-in-one vs. real-time CMR (Part 2).

Cardiac Magnetic Resonance (CMR) protocols can be lengthy and complex, which has driven the research community to develop new technologies to make these protocols more efficient and patient-friendly. Two different approaches to improving CMR have been proposed, specifically "all-in-one" CMR, where several contrasts and/or motion states are acquired simultaneously, and "real-time" CMR, in which the examination is accelerated to avoid the need for breathholding and/or cardiac gating. The goal of this two-part manuscript is to describe these two different types of emerging rapid CMR protocols. To this end, the vision of all-in-one and real-time imaging are described, along with techniques which have been devised and tested along the pathway of clinical implementation. The pros and cons of the different methods are presented, and the remaining open needs of each are detailed. Part 1 tackles the "All-in-One" approaches, and Part 2 focuses on the "Real-Time" approaches along with an overall summary of these emerging methods.

Mapping the Spatial Extent of Hypoperfusion in Chronic Thromboembolic Pulmonary Hypertension Using Multienergy CT.

Purpose: To assess if a novel automated method to spatially delineate and quantify the extent of hypoperfusion on multienergy CT angiograms can aid the evaluation of chronic thromboembolic pulmonary hypertension (CTEPH) disease severity. Materials and Methods: Multienergy CT angiograms obtained between January 2018 and December 2020 in 51 patients with CTEPH (mean age, 47 years ± 17 [SD]; 27 women) were retrospectively compared with those in 110 controls with no imaging findings suggestive of pulmonary vascular abnormalities (mean age, 51 years ± 16; 81 women). Parenchymal iodine values were automatically isolated using deep learning lobar lung segmentations. Low iodine concentration was used to delineate areas of hypoperfusion and calculate hypoperfused lung volume (HLV). Receiver operating characteristic curves, correlations with preoperative and postoperative changes in invasive hemodynamics, and comparison with visual assessment of lobar hypoperfusion by two expert readers were evaluated. Results: Global HLV correctly separated patients with CTEPH from controls (area under the receiver operating characteristic curve = 0.84; 10% HLV cutoff: 90% sensitivity, 72% accuracy, and 64% specificity) and correlated moderately with hemodynamic severity at time of imaging (pulmonary vascular resistance [PVR], ρ = 0.67; P < .001) and change after surgical treatment (∆PVR, ρ = -0.61; P < .001). In patients surgically classified as having segmental disease, global HLV correlated with preoperative PVR (ρ = 0.81) and postoperative ∆PVR (ρ = -0.70). Lobar HLV correlated moderately with expert reader lobar assessment (ρHLV = 0.71 for reader 1; ρHLV = 0.67 for reader 2). Conclusion: Automated quantification of hypoperfused areas in patients with CTEPH can be performed from clinical multienergy CT examinations and may aid clinical evaluation, particularly in patients with segmental-level disease.Keywords: CT-Spectral Imaging (Multienergy), Pulmonary, Pulmonary Arteries, Embolism/Thrombosis, Chronic Thromboembolic Pulmonary Hypertension, Multienergy CT, Hypoperfusion© RSNA, 2023.

Quantitative analysis of small coronary artery calcium detectability with an accurate simulation and validation on a clinical CT scanner.

BACKGROUND: The absence of coronary artery calcium (CAC) measured via CT is associated with very favorable prognosis, and current guidelines recommend low-density lipoprotein cholesterol (LDL-c) lowering therapy for individuals with any CAC. This motivates early detection of small granules of CAC; however, calcium scan sensitivity for detecting very low levels of calcium has not been quantified. PURPOSE: In this work, the size limit of detectability of small calcium hydroxyapatite (CaHA) granules with clinical CAC scanning was assessed using validated simulations. METHODS: CT projections of digital 3D mathematical phantoms containing small CaHA granules were simulated analytically; images were reconstructed using a filter designed to reproduce the point spread function of a specific commercial scanner, and a relationship of HU number versus diameter was derived. These simulation results were validated with experimental measurements of HU versus diameter from phantoms containing small granules of CaHA on a GE Revolution CT scanner in the clinic; ground truth measurements of the CaHA granule diameters were obtained using a Zeiss Xradia 510 Versa high-resolution 3D micro-CT imaging system. Using experimental measurements on the clinical CT scanner, detectability was quantified with a detectability index (d') using a non-prewhitened matched filter. The effect of changes to reconstruction slice thickness and reconstruction kernel on granule detectability was evaluated. RESULTS: Under typical clinical calcium scanning and reconstruction conditions, the minimum detectable diameter of a simulated spherical calcium granule with a clinically relevant CaHA density was 0.76 mm. The minimum detectable volume was 2.4 times smaller on images reconstructed at a slice thickness of 0.625 mm compared to 2.5 mm. The detectability index d' increased by a factor of 1.7 when images were reconstructed with 0.625 mm slices compared to 2.5 mm slices. d' did not change when images were reconstructed with the high-resolution BONE filter compared to the less sharp STANDARD resolution filter on the GE Revolution CT. CONCLUSIONS: We have quantified detectability versus size of small calcium granules at the resolution limit of a widely available clinical CT scanner. Detectability increased significantly with reduced slice thickness and did not change with a sharper reconstruction kernel. The simulation can be used to calculate the trade-off between dose and CAC detectability.

Myocardial Regional Shortening from 4D Cardiac CT Angiography for the Detection of Left Ventricular Segmental Wall Motion Abnormality.

Purpose: To investigate whether endocardial regional shortening computed from four-dimensional (4D) CT angiography (RSCT) can be used as a decision classifier to detect the presence of left ventricular (LV) wall motion abnormalities (WMAs). Materials and Methods: One hundred electrocardiographically gated cardiac 4D CT studies (mean age, 59 years ± 14 [SD]; 61 male patients) conducted between April 2018 and December 2020 were retrospectively evaluated. Three experts labeled LV wall motion in each of the 16 American Heart Association (AHA) segments as normal or abnormal; they also measured peak RSCT across one heartbeat in each segment. The data set was split evenly into training and validation groups. During training, interchangeability of RSCT thresholding with experts to detect WMA was assessed using the individual equivalence index (γ), and an optimal threshold of the peak RSCT (RSCT*) that achieved maximum agreement was identified. RSCT* was then validated using the validation group, and the effect of AHA segment-specific thresholds was evaluated. Agreement was assessed using κ statistics. Results: The optimal threshold, RSCT* of -0.19, when applied to all AHA segments, led to high agreement (agreement rate = 92.17%, κ = 0.82) and interchangeability with experts (γ = -2.58%). The same RSCT* also achieved high agreement in the validation group (agreement rate = 90.29%, κ = 0.76, γ = -0.38%). The use of AHA segment-specific thresholds (range: 0.16 to -0.23 across AHA segments) slightly improved agreement (1.79% increase). Conclusion: RSCT thresholding was interchangeable with expert visual analysis in detecting segmental WMA from 4D CT and may be used as an objective decision classifier.Keywords: CT, Left Ventricle, Regional Endocardial Shortening, Wall Motion Abnormality Supplemental material is available for this article. © RSNA, 2023.

Preoperative Computed Tomography Assessment of Risk of Right Ventricle Failure After Left Ventricular Assist Device Placement.

Identification of patients who are at a high risk for right ventricular failure (RVF) after left ventricular assist device (LVAD) implantation is of critical importance. Conventional tools for predicting RVF, including two-dimensional echocardiography, right heart catheterization (RHC), and clinical parameters, generally have limited sensitivity and specificity. We retrospectively examined the ability of computed tomography (CT) ventricular volume measures to identify patients who experienced RVF after LVAD implantation. Between September 2017 and November 2021, 92 patients underwent LVAD surgery at our institution. Preoperative CT-derived ventricular volumes were obtained in 20 patients. Patients who underwent CT evaluation had a similar demographics and rate of RVF after LVAD as patients who did not undergo cardiac CT imaging. In the study cohort, seven of 20 (35%) patients experienced RVF (2 unplanned biventricular assist device, 5 prolonged inotropic support). Computed tomography-derived right ventricular end-diastolic and end-systolic volume indices were the strongest predictors of RVF compared with demographic, echocardiographic, and RHC data with areas under the receiver operating curve of 0.79 and 0.76, respectively. Computed tomography volumetric assessment of RV size can be performed in patients evaluated for LVAD treatment. RV measures of size provide a promising means of pre-LVAD assessment for postoperative RV failure.

Pressure Volume Loop Analysis of the Right Ventricle in Heart Failure With Computed Tomography.

Right ventricular (RV) function is an important marker of mortality in chronic left-sided heart failure. Right ventricular function is particularly important for patients receiving left ventricular assist devices as it is a predictor of postoperative RV failure. RV stroke work index (RVSWI), the area enclosed by a pressure-volume (PV) loop, is prognostic of RV failure. However, clinical RVSWI approximates RVSWI as the product of thermodilution-derived stroke volume and the pulmonary pressure gradient. This ignores the energetic contribution of regurgitant flow and does not allow for advanced energetic measures, such as pressure-volume area and efficiency. Estimating RVSWI from forward flow may underestimate the underlying RV function. We created single-beat PV loops by combining data from cine computed tomography (CT) and right heart catheterization in 44 heart failure patients, tested the approximations made by clinical RVSWI and found it to underestimate PV loop RVSWI, primarily due to regurgitant flow in tricuspid regurgitation. The ability of RVSWI to predict post-operative RV failure improved when the single-beat approach was used. Further, RV pressure-volume area and efficiency measures were obtained and show broad agreement with other functional measures. Future work is needed to investigate the utility of these PV metrics in a clinical setting.

DiFiR-CT: Distance field representation to resolve motion artifacts in computed tomography.

BACKGROUND: Motion during data acquisition leads to artifacts in computed tomography (CT) reconstructions. In cases such as cardiac imaging, not only is motion unavoidable, but evaluating the motion of the object is of clinical interest. Reducing motion artifacts has typically been achieved by developing systems with faster gantry rotation or via algorithms which measure and/or estimate the displacement. However, these approaches have had limited success due to both physical constraints as well as the challenge of estimating non-rigid, temporally varying, and patient-specific motion fields. PURPOSE: To develop a novel reconstruction method which generates time-resolved, artifact-free images without estimation or explicit modeling of the motion. METHODS: We describe an analysis-by-synthesis approach which progressively regresses a solution consistent with the acquired sinogram. In our method, we focus on the movement of object boundaries. Not only are the boundaries the source of image artifacts, but object boundaries can simultaneously be used to represent both the object as well as its motion over time without the need for an explicit motion model. We represent the object boundaries via a signed distance function (SDF) which can be efficiently modeled using neural networks. As a result, optimization can be performed under spatial and temporal smoothness constraints without the need for explicit motion estimation. RESULTS: We illustrate the utility of DiFiR-CT in three imaging scenarios with increasing motion complexity: translation of a small circle, heart-like change in an ellipse's diameter, and a complex topological deformation. Compared to filtered backprojection, DiFiR-CT provides high quality image reconstruction for all three motions without hyperparameter tuning or change to the architecture. We also evaluate DiFiR-CT's robustness to noise in the acquired sinogram and found its reconstruction to be accurate across a wide range of noise levels. Lastly, we demonstrate how the approach could be used for multi-intensity scenes and illustrate the importance of the initial segmentation providing a realistic initialization. Code and supplemental movies are available at https://kunalmgupta.github.io/projects/DiFiR-CT.html. CONCLUSIONS: Projection data can be used to accurately estimate a temporally-evolving scene without the need for explicit motion estimation using a neural implicit representation and analysis-by-synthesis approach.

Intravascularly infused extracellular matrix as a biomaterial for targeting and treating inflamed tissues.

Decellularized extracellular matrix in the form of patches and locally injected hydrogels has long been used as therapies in animal models of disease. Here we report the safety and feasibility of an intravascularly infused extracellular matrix as a biomaterial for the repair of tissue in animal models of acute myocardial infarction, traumatic brain injury and pulmonary arterial hypertension. The biomaterial consists of decellularized, enzymatically digested and fractionated ventricular myocardium, localizes to injured tissues by binding to leaky microvasculature, and is largely degraded in about 3 d. In rats and pigs with induced acute myocardial infarction followed by intracoronary infusion of the biomaterial, we observed substantially reduced left ventricular volumes and improved wall-motion scores, as well as differential expression of genes associated with tissue repair and inflammation. Delivering pro-healing extracellular matrix by intravascular infusion post injury may provide translational advantages for the healing of inflamed tissues 'from the inside out'.

Right Ventricular and Pulmonary Computed Tomography Assessments in Paradoxical Low-Flow Low-Gradient Aortic Stenosis Undergoing Transcatheter Aortic Valve Replacement.

Background: Patients with paradoxical low-flow low-gradient aortic stenosis (pLFLG-AS) have high mortality and high degree of TAVR futility. Computed tomography (CT) enables accurate simultaneous right ventricular (RV) and parenchymal lung disease evaluation which may provide useful objective markers of AS severity, concomitant pulmonary comorbidities, and transcatheter aortic valve replacement (TAVR) improvement. However, the prevalence of RV dysfunction and its association with pulmonary disease in pLFLG-AS is unknown. The study objective was to test the hypothesis that pLFLG-AS patients undergoing TAVR have decreased RV function without significant parenchymal lung disease. Methods: Between August 2016 and March 2020, 194 consecutive AS patients completed high-resolution computed tomography (CT) imaging for TAVR evaluation. Subjects were stratified based on echocardiographic criteria as the study group, pLFLG (n=27), and two consecutive control groups: classic severe, normal-flow, high-gradient (n=27) and normal-flow, low-gradient (NFLG) (n=27) AS. Blinded biventricular function and lung parenchymal disease assessments were obtained by high-resolution CT imaging. Results: Patient demographics were similar between groups. pLFLG-AS had lower RV ejection fraction (49±10%) compared to both classic severe (58±7%, p<0.001) and NFLG AS (55±65%, p=0.02). There were no significant differences on lung emphysema (p=0.19), air fraction (p=0.58), and pulmonary disease presence (p=0.94) and severity (p=0.67) between groups. Conclusion: pLFLG-AS patients have lower RV ejection fraction, than classic severe and normal-flow low-gradient AS patients in the absence of significant parenchymal lung disease on CT imaging. These findings support the direct importance of RV function in the pathophysiology of aortic valve disease.

Detection of left ventricular wall motion abnormalities from volume rendering of 4DCT cardiac angiograms using deep learning.

Background: The presence of left ventricular (LV) wall motion abnormalities (WMA) is an independent indicator of adverse cardiovascular events in patients with cardiovascular diseases. We develop and evaluate the ability to detect cardiac wall motion abnormalities (WMA) from dynamic volume renderings (VR) of clinical 4D computed tomography (CT) angiograms using a deep learning (DL) framework. Methods: Three hundred forty-three ECG-gated cardiac 4DCT studies (age: 61 ± 15, 60.1% male) were retrospectively evaluated. Volume-rendering videos of the LV blood pool were generated from 6 different perspectives (i.e., six views corresponding to every 60-degree rotation around the LV long axis); resulting in 2058 unique videos. Ground-truth WMA classification for each video was performed by evaluating the extent of impaired regional shortening visible (measured in the original 4DCT data). DL classification of each video for the presence of WMA was performed by first extracting image features frame-by-frame using a pre-trained Inception network and then evaluating the set of features using a long short-term memory network. Data were split into 60% for 5-fold cross-validation and 40% for testing. Results: Volume rendering videos represent ~800-fold data compression of the 4DCT volumes. Per-video DL classification performance was high for both cross-validation (accuracy = 93.1%, sensitivity = 90.0% and specificity = 95.1%, κ: 0.86) and testing (90.9, 90.2, and 91.4% respectively, κ: 0.81). Per-study performance was also high (cross-validation: 93.7, 93.5, 93.8%, κ: 0.87; testing: 93.5, 91.9, 94.7%, κ: 0.87). By re-binning per-video results into the 6 regional views of the LV we showed DL was accurate (mean accuracy = 93.1 and 90.9% for cross-validation and testing cohort, respectively) for every region. DL classification strongly agreed (accuracy = 91.0%, κ: 0.81) with expert visual assessment. Conclusions: Dynamic volume rendering of the LV blood pool combined with DL classification can accurately detect regional WMA from cardiac CT.

Four-dimensional computed tomography of the left ventricle, Part I: Motion artifact reduction.

PURPOSE: Standard four-dimensional computed tomography (4DCT) cardiac reconstructions typically include spiraling artifacts that depend not only on the motion of the heart but also on the gantry angle range over which the data was acquired. We seek to reduce these motion artifacts and, thereby, improve the accuracy of left ventricular wall positions in 4DCT image series. METHODS: We use a motion artifact reduction approach (ResyncCT) that is based largely on conjugate pairs of partial angle reconstruction (PAR) images. After identifying the key locations where motion artifacts exist in the uncorrected images, paired subvolumes within the PAR images are analyzed with a modified cross-correlation function in order to estimate 3D velocity and acceleration vectors at these locations. A subsequent motion compensation process (also based on PAR images) includes the creation of a dense motion field, followed by a backproject-and-warp style compensation. The algorithm was tested on a 3D printed phantom, which represents the left ventricle (LV) and on challenging clinical cases corrupted by severe artifacts. RESULTS: The results from our preliminary phantom test as well as from clinical cardiac scans show crisp endocardial edges and resolved double-wall artifacts. When viewed as a temporal series, the corrected images exhibit a much smoother motion of the LV endocardial boundary as compared to the uncorrected images. In addition, quantitative results from our phantom studies show that ResyncCT processing reduces endocardial surface distance errors from 0.9 ± 0.8 to 0.2 ± 0.1 mm. CONCLUSIONS: The ResyncCT algorithm was shown to be effective in reducing motion artifacts and restoring accurate wall positions. Some perspectives on the use of conjugate-PAR images and on techniques for CT motion artifact reduction more generally are also given.

Deep learning automates detection of wall motion abnormalities via measurement of longitudinal strain from ECG-gated CT images.

Introduction: 4D cardiac CT (cineCT) is increasingly used to evaluate cardiac dynamics. While echocardiography and CMR have demonstrated the utility of longitudinal strain (LS) measures, measuring LS from cineCT currently requires reformatting the 4D dataset into long-axis imaging planes and delineating the endocardial boundary across time. In this work, we demonstrate the ability of a recently published deep learning framework to automatically and accurately measure LS for detection of wall motion abnormalities (WMA). Methods: One hundred clinical cineCT studies were evaluated by three experienced cardiac CT readers to identify whether each AHA segment had a WMA. Fifty cases were used for method development and an independent group of 50 were used for testing. A previously developed convolutional neural network was used to automatically segment the LV bloodpool and to define the 2, 3, and 4 CH long-axis imaging planes. LS was measured as the perimeter of the bloodpool for each long-axis plane. Two smoothing approaches were developed to avoid artifacts due to papillary muscle insertion and texture of the endocardial surface. The impact of the smoothing was evaluated by comparison of LS estimates to LV ejection fraction and the fractional area change of the corresponding view. Results: The automated, DL approach successfully analyzed 48/50 patients in the training cohort and 47/50 in the testing cohort. The optimal LS cutoff for identification of WMA was -21.8, -15.4, and -16.6% for the 2-, 3-, and 4-CH views in the training cohort. This led to correct labeling of 85, 85, and 83% of 2-, 3-, and 4-CH views, respectively, in the testing cohort. Per-study accuracy was 83% (84% sensitivity and 82% specificity). Smoothing significantly improved agreement between LS and fractional area change (R 2: 2 CH = 0.38 vs. 0.89 vs. 0.92). Conclusion: Automated LV blood pool segmentation and long-axis plane delineation via deep learning enables automatic LS assessment. LS values accurately identify regional wall motion abnormalities and may be used to complement standard visual assessments.

Octree Representation Improves Data Fidelity of Cardiac CT Images and Convolutional Neural Network Semantic Segmentation of Left Atrial and Ventricular Chambers.

PURPOSE: To assess whether octree representation and octree-based convolutional neural networks (CNNs) improve segmentation accuracy of three-dimensional images. MATERIALS AND METHODS: Cardiac CT angiographic examinations from 100 patients (mean age, 67 years ± 17 [standard deviation]; 60 men) performed between June 2012 and June 2018 with semantic segmentations of the left ventricular (LV) and left atrial (LA) blood pools at the end-diastolic and end-systolic cardiac phases were retrospectively evaluated. Image quality (root mean square error [RMSE]) and segmentation fidelity (global Dice and border Dice coefficients) metrics of the octree representation were compared with spatial downsampling for a range of memory footprints. Fivefold cross-validation was used to train an octree-based CNN and CNNs with spatial downsampling at four levels of image compression or spatial downsampling. The semantic segmentation performance of octree-based CNN (OctNet) was compared with the performance of U-Nets with spatial downsampling. RESULTS: Octrees provided high image and segmentation fidelity (median RMSE, 1.34 HU; LV Dice coefficient, 0.970; LV border Dice coefficient, 0.843) with a reduced memory footprint (87.5% reduction). Spatial downsampling to the same memory footprint had lower data fidelity (median RMSE, 12.96 HU; LV Dice coefficient, 0.852; LV border Dice coefficient, 0.310). OctNet segmentation improved the border segmentation Dice coefficient (LV, 0.612; LA, 0.636) compared with the highest performance among U-Nets with spatial downsampling (Dice coefficients: LV, 0.579; LA, 0.592). CONCLUSION: Octree-based representations can reduce the memory footprint and improve segmentation border accuracy.Keywords CT, Cardiac, Segmentation, Supervised Learning, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms© RSNA, 2021.

Novel 4DCT Method to Measure Regional Left Ventricular Endocardial Shortening Before and After Transcatheter Mitral Valve Implantation.

BACKGROUND: Regional left ventricular (LV) mechanics in mitral regurgitation (MR) patients, and local changes in function after transcatheter mitral valve implantation (TMVI) have yet to be evaluated. Herein, we introduce a method for creating high resolution maps of endocardial function from 4DCT images, leading to detailed characterization of changes in local LV function. These changes are particularly interesting when evaluating the effect of the Tendyne™ TMVI device in the region of the epicardial pad. METHODS: Regional endocardial shortening from CT (RSCT) was evaluated in Tendyne (Abbott Medical) TMVI patients with 4DCT exams pre- and post-implantation. Regional function was evaluated in 90 LV segments (5 longitudinal × 18 circumferential). LV volumes and ejection fraction (EF) were also computed. A reproducibility study was performed in a subset of patients to determine the precision of RSCT measurements in this population. RESULTS: Baseline and local changes in RSCT post TMVI were highly variable and extremely spatially heterogeneous. Both inter- and intra-observer variability were low and demonstrated the high precision of RSCT for evaluating regional LV function. CONCLUSION: RSCT is a reproducible metric which can be evaluated in patients with highly abnormal regional LV function and geometry. After TMVI, significant spatially heterogeneous changes in RSCT were observed in all subjects; therefore, it is unlikely that the functional state of TMVI patients can be fully described by changes in LV volume or EF. Measurement of RSCT provides precise characterization of the spatially heterogeneous effects of MR and TMVI on LV function and remodeling.

Automated cardiac volume assessment and cardiac long- and short-axis imaging plane prediction from electrocardiogram-gated computed tomography volumes enabled by deep learning.

AIMS: To develop an automated method for bloodpool segmentation and imaging plane re-slicing of cardiac computed tomography (CT) via deep learning (DL) for clinical use in coronary artery disease (CAD) wall motion assessment and reproducible longitudinal imaging. METHODS AND RESULTS: One hundred patients who underwent clinically indicated cardiac CT scans with manually segmented left ventricle (LV) and left atrial (LA) chambers were used for training. For each patient, long-axis (LAX) and short-axis planes were manually defined by an imaging expert. A DL model was trained to predict bloodpool segmentations and imaging planes. Deep learning bloodpool segmentations showed close agreement with manual LV [median Dice: 0.91, Hausdorff distance (HD): 6.18 mm] and LA (Dice: 0.93, HD: 7.35 mm) segmentations and a strong correlation with manual ejection fraction (Pearson r: 0.95 LV, 0.92 LA). Predicted planes had low median location (6.96 mm) and angular orientation (7.96 ° ) errors which were comparable to inter-reader differences (P > 0.71). 84-97% of DL-prescribed LAX planes correctly intersected American Heart Association segments, which was comparable (P > 0.05) to manual slicing. In a test cohort of 144 patients, we evaluated the ability of the DL approach to provide diagnostic imaging planes. Visual scoring by two blinded experts determined ≥94% of DL-predicted planes to be diagnostically adequate. Further, DL-enabled visualization of LV wall motion abnormalities due to CAD and provided reproducible planes upon repeat imaging. CONCLUSION: A volumetric, DL approach provides multiple chamber segmentations and can re-slice the imaging volume along standardized cardiac imaging planes for reproducible wall motion abnormality and functional assessment.

Heart-centered positioning and tailored beam-shaping filtration for reduced radiation dose in coronary artery calcium imaging: A Multi-Ethnic Study of Atherosclerosis (MESA) Study.

PURPOSE: Cardiac computed tomography has a clear clinical role in the evaluation of coronary artery disease and assessment of coronary artery calcium (CAC) but the use of ionizing radiation limits the clinical use. Beam-shaping "bow-tie" filters determine the radiation dose and the effective scan field-of-view diameter (SFOV) by delivering higher X-ray fluence to a region centered at the isocenter. A method for positioning the heart near the isocenter could enable reduced SFOV imaging and reduce dose in cardiac scans. However, a predictive approach to center the heart, the extent to which heart centering can reduce the SFOV, and the associated dose reductions have not been assessed. The purpose of this study is to build a heart-centered patient positioning model, to test whether it reduces the SFOV required for accurate CAC scoring, and to quantify the associated reduction in radiation dose. METHODS: The location of 38,184 calcium lesions (3151 studies) in the Multi-Ethnic Study of Atherosclerosis was utilized to build a predictive heart-centered positioning model and compare the impact of SFOV on CAC scoring accuracy in heart-centered and conventional body-centered scanning. Then, the positioning model was applied retrospectively to an independent, contemporary cohort of 118 individuals (81 with CAC > 0) at our institution to validate the model's ability to maintain CAC accuracy while reducing the SFOV. In these patients, the reduction in dose associated with a reduced SFOV beam-shaping filter was quantified. RESULTS: Heart centering reduced the SFOV diameter 25.7% relative to body centering while maintaining high CAC scoring accuracy (0.82% risk reclassification rate). In our validation cohort, imaging at this reduced SFOV with heart-centered positioning and tailored beam-shaping filtration led to a 26.9% median dose reduction (25-75th percentile: 21.6%-29.8%) without any calcium risk reclassification. CONCLUSIONS: Heart-centered patient positioning enables a significant radiation dose reduction while maintaining CAC accuracy.

Targeted Coronary Artery Calcium Screening in High-Risk Younger Individuals Using Consumer Genetic Screening Results.

OBJECTIVES: The goal of this study was to assess the utility of a genetic risk score (GRS) in targeted coronary artery calcium (CAC) screening among young individuals. BACKGROUND: Early CAC screening and preventive therapy may reduce long-term risk of a coronary heart disease (CHD) event. However, identifying younger individuals at increased risk remains a challenge. GRS for CHD are age independent and can stratify individuals on various risk trajectories. METHODS: Using 142 variants associated with CHD events, we calculated a GRS in 1,927 individuals in the CARDIA (Coronary Artery Risk Development in Young Adults) cohort (aged 32 to 47 years) and 6,600 individuals in the MESA (Multi-Ethnic Study of Atherosclerosis) cohort (aged 44 to 87 years). We assessed GRS utility to predict CAC presence in the CARDIA cohort and stratify individuals of varying risk for CAC presence over the lifetime in both cohorts. RESULTS: The GRS predicted CAC presence in CARDIA males. It was not predictive in CARDIA females, which had a CAC prevalence of 6.4%. In combined analysis of the CARDIA and MESA cohorts, the GRS was predictive of CAC in both males and females and was used to derive an equation for the age at which CAC probability crossed a predetermined threshold. When assessed in combination with traditional risk factors, the GRS further stratified individuals. For individuals with an equal number of traditional risk factors, probability of CAC reached 25% approximately 10 years earlier for those in the highest GRS quintile compared to the lowest. CONCLUSIONS: The GRS may be used to target high-risk younger individuals for early CAC screening.

M-SiSSR: Regional Endocardial Function Using Multilabel Simultaneous Subdivision Surface Registration.

Quantification of regional cardiac function is a central goal of cardiology. Multiple methods, such as Coherent Point Drift (CPD) and Simultaneous Subdivision Surface Registration (SiSSR), have been used to register meshes to the endocardial surface. However, these methods do not distinguish between cardiac chambers during registration, and consequently the mesh may "slip" across the interface between two structures during contraction, resulting in inaccurate regional functional measurements. Here, we present Multilabel-SiSSR (M-SiSSR), a novel method for registering a "labeled" cardiac mesh (with each triangle assigned to a cardiac structure). We compare our results to the original, label-agnostic version of SiSSR and find both a visual and quantitative improvement in tracking of the mitral valve plane.