Active Sampling for MRI-based Sequential Decision Making

• URL: http://arxiv.org/abs/2505.04586v1
• Date: Wed, 07 May 2025 17:27:51 GMT
• Title: Active Sampling for MRI-based Sequential Decision Making
• Authors: Yuning Du, Jingshuai Liu, Rohan Dharmakumar, Sotirios A. Tsaftaris,
• Abstract summary: We present a novel reinforcement learning framework enabling comprehensive, sequential, diagnostic evaluation from undersampled k-space data.<n>Our framework achieves diagnostic performance competitive with various policy-based benchmarks on disease detection, severity, and overall sequential diagnosis, while substantially saving k-space samples.
• Score: 10.32110889047933
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the superior diagnostic capability of Magnetic Resonance Imaging (MRI), its use as a Point-of-Care (PoC) device remains limited by high cost and complexity. To enable such a future by reducing the magnetic field strength, one key approach will be to improve sampling strategies. Previous work has shown that it is possible to make diagnostic decisions directly from k-space with fewer samples. Such work shows that single diagnostic decisions can be made, but if we aspire to see MRI as a true PoC, multiple and sequential decisions are necessary while minimizing the number of samples acquired. We present a novel multi-objective reinforcement learning framework enabling comprehensive, sequential, diagnostic evaluation from undersampled k-space data. Our approach during inference actively adapts to sequential decisions to optimally sample. To achieve this, we introduce a training methodology that identifies the samples that contribute the best to each diagnostic objective using a step-wise weighting reward function. We evaluate our approach in two sequential knee pathology assessment tasks: ACL sprain detection and cartilage thickness loss assessment. Our framework achieves diagnostic performance competitive with various policy-based benchmarks on disease detection, severity quantification, and overall sequential diagnosis, while substantially saving k-space samples. Our approach paves the way for the future of MRI as a comprehensive and affordable PoC device. Our code is publicly available at https://github.com/vios-s/MRI_Sequential_Active_Sampling

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