mlsanityData to 14 Oct 2026

Active Sampling for MRI-based Sequential Decision Making

arXiv 2505.04586 ↗First posted 7 May 2025eess.IV cs.CV cs.LGcited by 1

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Abstract (arXiv)

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

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 23 of its 55 references that are in the map. First posted on arXiv 7 May 2025.

Builds on

23 of 55 references.

References · 23

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11 more
  1. 2019Multiple Landmark Detection using Multi-Agent Reinforcement Learningcited by 23
  2. 2020Extending LOUPE for K-space Under-sampling Pattern Optimization in Multi-coil MRIcited by 22
  3. 2022Lexicographic Multi-Objective Reinforcement Learningcited by 17
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  6. 2024Classification, Regression and Segmentation directly from k-Space in Cardiac MRIcited by 8
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Frequency-Hierarchical Active k-Space Sampling for Diagnostic MRIcited by 0

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