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Abstract #4804

Accelerated MR screenings with direct k-space classification

Raghav Singhal1, Mukund Sudarshan1, Luke Ginocchio2, Angela Tong2, Hersh Chandarana2, Daniel Sodickson2, Rajesh Ranganath3,4, and Sumit Chopra1,2
1Courant Institute of Mathematical Sciences, New York University, New York, NY, United States, 2Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 3Department of Population Health, New York University, New York, NY, United States, 4Center for Data Science, New York University, New York, NY, United States


Despite its rich clinical information content, MR has seen limited adoption in population-level screenings, due to concerns about specificity combined with high scan duration and cost. In order to begin to address such issues, and to accelerate the entire pipeline from data acquisition to diagnosis, we introduce ARMS, an algorithm that learns k-space undersampling patterns to maximize the accuracy of pathology detection. ARMS detects pathologies directly from undersampled k-space data, bypassing explicit image reconstruction. We use ARMS to detect clinically significant prostate cancer and knee abnormalities in 2D MR scans, achieving an acceleration of 12.5x without compromising accuracy.

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