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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