Abstract #4927
DL-Based LV Volume Segmentation to Compare Respiratory-Corrected and 4D XD-GRASP Respiratory Motion-Resolved Whole-Heart Reconstructions
Yitong Yang1, Zahraw Shah2, Athira Jacob3, Jackson Hair1, Teodora Chitiboi3, Tiziano Passerini3, Jerome Yerly4, Lorenzo Di Sopra4, Davide Piccini5, Zahra Hosseini6, Puneet Sharma3, Anurag Sahu7, Matthias Stuber4, and John Oshinski1,8
1Biomedical Engineering, Emory University/Georgia Institute of Technology, Atlanta, GA, United States, 2Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States, 3Siemens Medical Solutions USA, Princeton, NJ, United States, 4Diagnostic and Interventional Radiology, Lausanne University Hospital, Lausanne, Switzerland, 5Siemens Healthcare, Lausanne, Switzerland, 6Siemens Medical Solutions USA, Atlanta, GA, United States, 7Cardiology, Emory University School of Medicine, Atlanta, GA, United States, 8Radiology, Emory University School of Medicine, Atlanta, GA, United States
Synopsis
Rapid and accurate quantification of left ventricular volume is essential for studying cardiac function. In this work, two reconstruction techniques for free-breathing, ECG-gated, radial, golden-angle phyllotaxis acquisition of whole-heart CMR are assessed for their accuracy in quantifying left ventricular volume using deep learning (DL)-based automatic cardiac chamber segmentation. The off-line reconstruction that resolves 4D (3D+respiratory) images had better agreement between the DL-based segmentation and an expert’s manual segmentation than the in-line reconstruction that corrects for respiratory motion, as assessed by the average volume difference (p=0.026) and 3D Dice coefficients (p=0.032).
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