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 Oshinski^1,8

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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