Abstract #3496

Comprehensive Evaluation of Deep Learning Reconstruction for Free-Breathing Radial Cine Cardiac Magnetic Resonance Imaging

Mahmut Yurt^1,2, Kanghyun Ryu³, Zhitao Li⁴, Xucheng Zhu⁵, Xianglun Mao⁵, John Pauly¹, Ali Syed^2,6, and Shreyas Vasanawala^2,6

¹Department of Electrical Engineering, Stanford University, Stanford, CA, United States, ²Cardiovascular Institute, Stanford University, Stanford, CA, United States, ³Artificial Intelligence and Robotics Institute, Korea Institute of Science and Technology, Seoul, Korea, Republic of, ⁴Department of Radiology, Northwestern University, Chicago, IL, United States, ⁵GE Healthcare, Stanford, CA, United States, ⁶Department of Radiology, Stanford University, Stanford, CA, United States

Synopsis

Keywords: Image Reconstruction, AI/ML Image Reconstruction

Motivation: We aim to conduct a comprehensive evaluation of a radial cardiac cine acquisition and deep learning reconstruction protocol.

Goal(s): Our objective is to demonstrate the effectiveness and generalizability of the deep learning reconstruction for accelerated cine imaging via qualitative and quantitative assessment over a diverse cohort of volunteers and patients.

Approach: We deploy a cardiac cine sequence and collect data from a large subject cohort. Collected data are processed with raw k-space preprocessing modules, followed by a deep learning reconstruction based on unrolled neural networks. The reconstruction quality is assessed via peak signal-to-noise ratio, structural similarity index and ejection fraction ratio.

Impact: Free-breathing, radial cardiac cine acquisition and reconstruction approaches can mitigate motion artifacts and improve patient comfort and compliance. We perform a comprehensive evaluation of such a protocol to validate its effectiveness and validity on diverse populations including volunteers and patients.

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