Patient-specific requirements and clinical validation of MRI-based pressure mapping: A two-center study in patients with aortic coarctation.

BACKGROUND: Invasive peak-to-peak pressure gradients are the current clinical reference standard for assessing aortic coarctation. To obtain them, patients need to undergo arterial heart catheterization. Unless an intervention is performed, the procedure remains purely diagnostic, while the concomitant risks remain. PURPOSE: To validate MRI-based pressure mapping against pressure drop derived from heart catheterization and to define minimal clinical requirements. STUDY TYPE: Prospective clinical validation study. POPULATION: Twenty-seven coarctation patients with an indicated heart catheterization were enrolled at two clinical centers. MRI SEQUENCES: 1.5T including 4D velocity-encoded MRI and 3D anatomical imaging of the aorta. ASSESSMENT: Pressure drop across the stenosis was calculated by pressure mapping based on the pressure Poisson equation. Calculated pressure drops were compared with catheter measured data. Spatial and temporal resolution were analyzed using in silico phantom-based data as well as in vivo measurements. STATISTICS: Pressure drop was compared to peak-to-peak measurements. A two-sample paired mean equivalence test was used. RESULTS: In patients without imaging artifacts and a required spatial resolution ≥5 voxel/diameter, significant equivalence of pressure mapping compared to heart catheterization was found (17.5 ± 6.49 vs. 16.6 ± 6.53 mmHg, P < 0.001). DATA CONCLUSION: Pressure mapping provides equivalent accuracy to pressure drop obtained from heart catheterization in patients 1) without previous stenting and 2) with sufficient spatial image resolution (at least 5 voxels/diameter). In these patients the method can reliably be performed prior to the actual procedure, and thus allows safe noninvasive treatment planning based on MRI. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;49:81-89.

Deep Learning-Based Automated Labeling of Coronary Segments for Structured Reporting of Coronary Computed Tomography Angiography in Accordance With Society of Cardiovascular Computed Tomography Guidelines.

PURPOSE: To evaluate a novel deep learning (DL)-based automated coronary labeling approach for structured reporting of coronary artery disease according to the guidelines of the Society of Cardiovascular Computed Tomography (CT) on coronary CT angiography (CCTA). PATIENTS AND METHODS: A retrospective cohort of 104 patients (60.3 ± 10.7 y, 61% males) who had undergone prospectively electrocardiogram-synchronized CCTA were included. Coronary centerlines were automatically extracted, labeled, and validated by 2 expert readers according to Society of Cardiovascular CT guidelines. The DL algorithm was trained on 706 radiologist-annotated cases for the task of automatically labeling coronary artery centerlines. The architecture leverages tree-structured long short-term memory recurrent neural networks to capture the full topological information of the coronary trees by using a two-step approach: a bottom-up encoding step, followed by a top-down decoding step. The first module encodes each sub-tree into fixed-sized vector representations. The decoding module then selectively attends to the aggregated global context to perform the local assignation of labels. To assess the performance of the software, percentage overlap was calculated between the labels of the algorithm and the expert readers. RESULTS: A total number of 1491 segments were identified. The artificial intelligence-based software approach yielded an average overlap of 94.4% compared with the expert readers' labels ranging from 87.1% for the posterior descending artery of the right coronary artery to 100% for the proximal segment of the right coronary artery. The average computational time was 0.5 seconds per case. The interreader overlap was 96.6%. CONCLUSIONS: The presented fully automated DL-based coronary artery labeling algorithm provides fast and precise labeling of the coronary artery segments bearing the potential to improve automated structured reporting for CCTA.

How scan parameter choice affects deep learning-based coronary artery disease assessment from computed tomography.

Recently, algorithms capable of assessing the severity of Coronary Artery Disease (CAD) in form of the Coronary Artery Disease-Reporting and Data System (CAD-RADS) grade from Coronary Computed Tomography Angiography (CCTA) scans using Deep Learning (DL) were proposed. Before considering to apply these algorithms in clinical practice, their robustness regarding different commonly used Computed Tomography (CT)-specific image formation parameters-including denoising strength, slab combination, and reconstruction kernel-needs to be evaluated. For this study, we reconstructed a data set of 500 patient CCTA scans under seven image formation parameter configurations. We select one default configuration and evaluate how varying individual parameters impacts the performance and stability of a typical algorithm for automated CAD assessment from CCTA. This algorithm consists of multiple preprocessing and a DL prediction step. We evaluate the influence of the parameter changes on the entire pipeline and additionally on only the DL step by propagating the centerline extraction results of the default configuration to all others. We consider the standard deviation of the CAD severity prediction grade difference between the default and variation configurations to assess the stability w.r.t. parameter changes. For the full pipeline we observe slight instability (± 0.226 CAD-RADS) for all variations. Predictions are more stable with centerlines propagated from the default to the variation configurations (± 0.122 CAD-RADS), especially for differing denoising strengths (± 0.046 CAD-RADS). However, stacking slabs with sharp boundaries instead of mixing slabs in overlapping regions (called true stack ± 0.313 CAD-RADS) and increasing the sharpness of the reconstruction kernel (± 0.150 CAD-RADS) leads to unstable predictions. Regarding the clinically relevant tasks of excluding CAD (called rule-out; AUC default 0.957, min 0.937) and excluding obstructive CAD (called hold-out; AUC default 0.971, min 0.964) the performance remains on a high level for all variations. Concluding, an influence of reconstruction parameters on the predictions is observed. Especially, scans reconstructed with the true stack parameter need to be treated with caution when using a DL-based method. Also, reconstruction kernels which are underrepresented in the training data increase the prediction uncertainty.

AI-based, automated chamber volumetry from gated, non-contrast CT.

BACKGROUND: Accurate chamber volumetry from gated, non-contrast cardiac CT (NCCT) scans can be useful for potential screening of heart failure. OBJECTIVES: To validate a new, fully automated, AI-based method for cardiac volume and myocardial mass quantification from NCCT scans compared to contrasted CT Angiography (CCTA). METHODS: Of a retrospectively collected cohort of 1051 consecutive patients, 420 patients had both NCCT and CCTA scans at mid-diastolic phase, excluding patients with cardiac devices. Ground truth values were obtained from the CCTA scans. RESULTS: The NCCT volume computation shows good agreement with ground truth values. Volume differences [95% CI ] and correlation coefficients were: -9.6 [-45; 26] mL, r â€‹= â€‹0.98 for LV Total, -5.4 [-24; 13] mL, r â€‹= â€‹0.95 for LA, -8.7 [-45; 28] mL, r â€‹= â€‹0.94 for RV, -5.2 [-27; 17] mL, r â€‹= â€‹0.92 for RA, -3.2 [-42; 36] mL, r â€‹= â€‹0.91 for LV blood pool, and -6.7 [-39; 26] g, r â€‹= â€‹0.94 for LV wall mass, respectively. Mean relative volume errors of less than 7% were obtained for all chambers. CONCLUSIONS: Fully automated assessment of chamber volumes from NCCT scans is feasible and correlates well with volumes obtained from contrast study.

Accuracy of an Artificial Intelligence Deep Learning Algorithm Implementing a Recurrent Neural Network With Long Short-term Memory for the Automated Detection of Calcified Plaques From Coronary Computed Tomography Angiography.

PURPOSE: The purpose of this study was to evaluate the accuracy of a novel fully automated deep learning (DL) algorithm implementing a recurrent neural network (RNN) with long short-term memory (LSTM) for the detection of coronary artery calcium (CAC) from coronary computed tomography angiography (CCTA) data. MATERIALS AND METHODS: Under an IRB waiver and in HIPAA compliance, a total of 194 patients who had undergone CCTA were retrospectively included. Two observers independently evaluated the image quality and recorded the presence of CAC in the right (RCA), the combination of left main and left anterior descending (LM-LAD), and left circumflex (LCx) coronary arteries. Noncontrast CACS scans were allowed to be used in cases of uncertainty. Heart and coronary artery centerline detection and labeling were automatically performed. Presence of CAC was assessed by a RNN-LSTM. The algorithm's overall and per-vessel sensitivity, specificity, and diagnostic accuracy were calculated. RESULTS: CAC was absent in 84 and present in 110 patients. As regards CCTA, the median subjective image quality, signal-to-noise ratio, and contrast-to-noise ratio were 3.0, 13.0, and 11.4. A total of 565 vessels were evaluated. On a per-vessel basis, the algorithm achieved a sensitivity, specificity, and diagnostic accuracy of 93.1% (confidence interval [CI], 84.3%-96.7%), 82.76% (CI, 74.6%-89.4%), and 86.7% (CI, 76.8%-87.9%), respectively, for the RCA, 93.1% (CI, 86.4%-97.7%), 95.5% (CI, 88.77%-98.75%), and 94.2% (CI. 90.2%-94.6%), respectively, for the LM-LAD, and 89.9% (CI, 80.2%-95.8%), 90.0% (CI, 83.2%-94.7%), and 89.9% (CI, 85.0%-94.1%), respectively, for the LCx. The overall sensitivity, specificity, and diagnostic accuracy were 92.1% (CI, 92.1%-95.2%), 88.9% (CI. 84.9%-92.1%), and 90.3% (CI, 88.0%-90.0%), respectively. When accounting for image quality, the algorithm achieved a sensitivity, specificity, and diagnostic accuracy of 76.2%, 87.5%, and 82.2%, respectively, for poor-quality data sets and 93.3%, 89.2% and 90.9%, respectively, when data sets rated adequate or higher were combined. CONCLUSION: The proposed RNN-LSTM demonstrated high diagnostic accuracy for the detection of CAC from CCTA.

CTA coronary labeling through efficient geodesics between trees using anatomy priors.

We present an efficient realization of recent work on unique geodesic paths between tree shapes for the application of matching coronary arteries to a standard model of coronary anatomy in order to label the coronary arteries. Automatically labeled coronary arteries would speed reporting for physicians. The efficiency of the approach and the quality of the results are enhanced using the relative position of detected cardiac structures. We explain how to efficiently compute the geodesic paths between tree shapes using Dijkstra's algorithm and we present a methodology to account for missing side branches during matching. For nearly all labels our approach shows promise compared with recent work and we results for 8 additional labels.

Evaluation of elevated mean pulmonary arterial pressure based on magnetic resonance 4D velocity mapping: comparison of visualization techniques.

PURPOSE: Three-dimensional (3D) magnetic resonance phase contrast imaging (PC-MRI) allows non-invasive diagnosis of pulmonary hypertension (PH) and estimation of elevated mean pulmonary arterial pressure (mPAP) based on vortical motion of blood in the main pulmonary artery. The purpose of the present study was to compare the presence and duration of PH-associated vortices derived from different flow visualization techniques with special respect to their performance for non-invasive assessment of elevated mPAP and diagnosis of PH. METHODS: Fifty patients with suspected PH (23 patients with and 27 without PH) were investigated by right heart catheterization and time-resolved PC-MRI of the main pulmonary artery. PC-MRI data were visualized with dedicated prototype software, providing 3D vector, multi-planar reformatted (MPR) 2D vector, streamline, and particle trace representation of flow patterns. Persistence of PH-associated vortical blood flow (tvortex) was evaluated with all visualization techniques. Dependencies of tvortex on visualization techniques were analyzed by means of correlation and receiver operating characteristic (ROC) curve analysis. RESULTS: tvortex values from 3D vector visualization correlated strongly with those from other visualization techniques (r = 0.98, 0.98 and 0.97 for MPR, streamline and particle trace visualization, respectively). Areas under ROC curves for diagnosis of PH based on tvortex did not differ significantly and were 0.998 for 3D vector, MPR vector and particle trace visualization and 0.999 for streamline visualization. Correlations between elevated mPAP and tvortex in patients with PH were r = 0.96, 0.93, 0.95 and 0.92 for 3D vector, MPR vector, streamline and particle trace visualization, respectively. Corresponding standard deviations from the linear regression lines ranged between 3 and 4 mmHg. CONCLUSION: 3D vector, MPR vector, streamline as well as particle trace visualization of time-resolved 3D PC-MRI data of the main pulmonary artery can be employed for accurate vortex-based diagnosis of PH and estimation of elevated mPAP.