Abstract #3631

Accelerating 2D Kidney Magnetic Resonance Fingerprinting using Deep Learning–Based Tissue Quantification

Huay Din¹, Christina J. MacAskill², Sree Harsha Tirumani^2,3, Pew-Thian Yap⁴, Mark Griswold^2,3, Chris Flask^2,3, and Yong Chen^1,3

¹Case Western Reserve University, Cleveland, OH, United States, ²Department of Radiology, Case Western Reserve University, Cleveland, OH, United States, ³Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, United States, ⁴Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States

Synopsis

Keywords: Kidney, Quantitative Imaging, CancerIn this pilot study, we developed and optimized a spatially-constrained convolutional neural network to accelerate the scan time for 2D kidney Magnetic Resonance Fingerprinting (MRF). Our results suggest that an acceleration factor of 3 can be achieved with the proposed method, which shortens the 2D breath-hold MRF scan from 15 sec to 5 sec. In addition, the deep learning based approach can be applied for T₁ and T₂ quantification of both normal renal tissues and pathologies including renal cell carcinoma.

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