Automated cardiac volume assessment and cardiac long- and short-axis imaging plane prediction from electrocardiogram-gated computed tomography volumes enabled by deep learning. Issue 2 (22nd March 2021)

• Record Type: Journal Article
• Authors: Chen, Zhennong; Rigolli, Marzia; Vigneault, Davis Marc; Kligerman, Seth; Hahn, Lewis; Narezkina, Anna; Craine, Amanda; Lowe, Katherine; Contijoch, Francisco
• Is Part Of: European heart journal. Volume 2:Issue 2(2021)
• Page Start: 311
• Page End: 322
• Publication Date: 2021-03-22
• Subjects: Deep learning -- Computed tomography -- Left ventricle -- Left atrium -- Wall motion abnormality; Medical informatics -- Periodicals; Medical technology -- Periodicals; Cardiovascular system -- Periodicals; 616.100284
• DOI: 10.1093/ehjdh/ztab033
• Languages: English
• ISSNs: 2634-3916

Abstract

Abstract: 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. Graphical Abstract: