Abstract #5139

Evaluation of image quality and global cardiac function for deep learning accelerated cardiac Cine

Xucheng Zhu¹, Suryanarayanan Kaushik², Frandics Chan³, Melany Atkins⁴, Prashant Nagpal ⁵, Reed Busse², and Martin Janich⁶

¹GE Healthcare, Menlo Park, CA, United States, ²GE Healthcare, Waukesha, WI, United States, ³Radiology, Stanford University, Palo Alto, CA, United States, ⁴Radiological Consultants, Inova Fairfax Hospital, Fairfax, VA, United States, ⁵Radiology, University of Wisconsin–Madison, Madison, WI, United States, ⁶GE Healthcare, Munich, Germany

Synopsis

Keywords: Heart, Image Reconstruction, Deep learning, reconstructionCardiac bSSFP Cine is widely used clinically; however, it is time consuming and requires multiple breath-holds. Deep learning-based accelerated Cine (DLCine) is a novel technique combining accelerated variable density sampling and deep learning regularized reconstruction that allows much higher acceleration compared to conventional Cine with parallel imaging. The purpose of this work was to compare image quality and global cardiac function utilizing DLCine versus conventional Cine by three expert readers. The results demonstrate that DLCine can be used to reduce the scan time while maintaining image quality and providing accurate global cardiac function measurement.

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