Abstract #3658

Deep-learning-enabled Approach to Automatically Eliminate Motion-induced Dark-rim Artifacts in Stress First-pass Perfusion CMRI

Hazar Benan Unal¹, Khalid Youssef², Abdul Ahmed³, Kelvin Chow⁴, Xiaoming Bi⁵, Luis Zamudio², Dilek Mirgun Yalcinkaya², Ronald Mastouri², Janet Wei⁶, C. Noel Bairey-Merz⁶, Rohan Dharmakumar⁷, and Behzad Sharif^1,7

¹Weldon School of Biomedical Engineering, Purdue University, Indianapolis, IN, United States, ²Indiana University School of Medicine, Indianapolis, IN, United States, ³Siemens Medical Solutions USA, Indianapolis, IN, United States, ⁴Siemens Medical Solutions USA, Chicago, IL, United States, ⁵Siemens Medical Solutions USA, Los Angeles, CA, United States, ⁶Cedars-Sinai Medical Center, Los Angeles, CA, United States, ⁷Department of Radiology & Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States

Synopsis

Keywords: Myocardium, Artifacts

Motivation: Dark-rim artifact (DRA) mimics perfusion defects in stress first-pass perfusion (FPP) cardiac MRI (CMRI), affecting the diagnostic accuracy in visual and quantitative methods. Goal: Eliminate motion-induced DRA in FPP CMRI.

Goal(s): Eliminate motion-induced DRA in FPP CMRI.

Approach: Apply temporal footprint (TF) reduction on scanner-reconstructed k-space to analyze effects on DRA severity in each myocardial sector (automatically segmented by a 2D+time deep neural network) and select the optimal TF reduction that minimizes DRA.

Results: Experiments on truly healthy cases showed that proposed approach significantly reduces the severity and prevalence of DRA. We also showed the feasibility of inline implementation of proposed approach.

Impact: Proposed approach offers an effective solution to eliminate DRA in first-pass perfusion CMRI. It helps improving the diagnostic accuracy of visual and quantitative methods, and the inline implementation can easily be deployed in routine clinical studies with standard CMRI protocols.

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