Abstract #0043

Deep Learning Reconstruction for Free-Breathing Radial Cine Imaging

Mahmut Yurt¹, Kanghyun Ryu², Zhitao Li³, Xucheng Zhu⁴, Xianglun Mao⁴, Kawin Setsompop⁵, Martin Janich⁴, John Pauly¹, Ali Syed⁵, and Shreyas Vasanawala^5,6

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

Synopsis

Keywords: Image Reconstruction, Cardiovascular

Motivation: We aim to introduce a cardiac cine imaging protocol to address the issues of motion susceptibility and robustness in the previous techniques.

Goal(s): Our objective is to demonstrate an accelerated acquisition and high-quality reconstruction framework based on free-breathing radial cardiac cine imaging that shows enhanced patient comfort and robustness against respiratory motion.

Approach: We synergistically leverage a raw k-space preprocessing module, region optimized coil compression, and deep learning reconstruction based on memory efficient unrolled neural networks.

Results: Our experiments indicate that the proposed framework achieves high reconstruction quality at large acceleration factors (e.g., 8x), in terms of spatial and temporal accuracy.

Impact: Conventional cardiac protocols use Cartesian k-space sampling and are susceptible to motion artifacts. We provide an acquisition and reconstruction framework based on a free-breathing protocol and deep learning reconstruction for enhanced patient comfort and robustness against motion artifacts.

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