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ARXIV:2604.12709 · MEDICAL AI · SUBMITTED 15 APR · 17:01 UTC · FRESHNESS STALE
ARXIV:2604.12709MEDICAL AISUBMITTED 15 APR · 17:01 UTCFRESHNESS STALEXinyu Peng · Ziyang Zheng · Wenrui Dai · Duoduo Xue · Shaohui Li · Chenglin Li · +2 at arXiv
An information-theoretic framework for task-adapted compressed sensing MRI that enables probabilistic inference for uncertainty prediction and adaptive sampling for clinical tasks.
Opportunity summary
Pain An information-theoretic framework for task-adapted compressed sensing MRI that enables probabilistic inference for uncertainty prediction and adaptive sampling for clinical tasks.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
An information-theoretic framework for task-adapted compressed sensing MRI that enables probabilistic inference for uncertainty prediction and adaptive sampling for clinical tasks. However, existing task-adapted CS-MRI methods suffer from the uncertainty problem for medical diagnosis…
Task-adapted compressed sensing magnetic resonance imaging (CS-MRI) is emerging to address the specific demands of downstream clinical tasks with significantly fewer k-space measurements than required by Nyquist sampling. However, existing task-adapted CS-MRI methods suffer…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. However, existing task-adapted CS-MRI methods suffer from the uncertainty problem for medical diagnosis and cannot achieve adaptive sampling in end-to-end optimization with reconstruction or…
Medical AI moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
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An information-theoretic framework for task-adapted compressed sensing MRI that enables probabilistic inference for uncertainty prediction and adaptive sampling for clinical tasks.
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10.48550/arXiv.2604.12709An information-theoretic framework for task-adapted compressed sensing MRI that enables probabilistic inference for uncertainty prediction and adaptive sampling for clinical tasks.
Abstract
Task-adapted compressed sensing magnetic resonance imaging (CS-MRI) is emerging to address the specific demands of downstream clinical tasks with significantly fewer k-space measurements than required by Nyquist sampling. However, existing task-adapted CS-MRI methods suffer from the uncertainty problem for medical diagnosis and cannot achieve adaptive sampling in end-to-end optimization with reconstruction or clinical tasks. To address these limitations, we propose the first task-adapted CS-MRI from the information-theoretic perspective to simultaneously achieve probabilistic inference for uncertainty prediction and adapt to arbitrary sampling ratios and versatile clinical applications. Specifically, we formalize the task-adapted CS-MRI optimization problem by maximizing the mutual information between undersampled k-space measurements and clinical tasks to enable probabilistic inference for addressing the uncertainty problem. We leverage amortized optimization and construct tractable variational bounds for mutual information to jointly optimize sampling, reconstruction, and task-inference models, which enables flexible sampling ratio control using a single end-to-end trained model. Furthermore, the proposed framework addresses two kinds of distinct clinical scenarios within a unified approach, i.e., i) joint task and reconstruction, where reconstruction serves as an auxiliary process to enhance task performance; and ii) task implementation with suppressed reconstruction, applicable for privacy protection. Extensive experiments on large-scale MRI datasets demonstrate that the proposed framework achieves highly competitive performance on standard metrics like Dice compared to deterministic counterpart but provides better distribution matching to the ground-truth posterior distribution as measured by the generalized energy distance (GED).
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PROBLEM
An information-theoretic framework for task-adapted compressed sensing MRI that enables probabilistic inference for uncertainty prediction and adaptive sampling for clinical tasks. However, existing task-adapted CS-MRI methods suffer from the uncertainty problem for medical diag...
METHOD
Task-adapted compressed sensing magnetic resonance imaging (CS-MRI) is emerging to address the specific demands of downstream clinical tasks with significantly fewer k-space measurements than required by Nyquist sampling. However, existing task-adapted CS-MRI methods suffer from...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. However, existing task-adapted CS-MRI methods suffer from the uncertainty problem for medical diagnosis and cannot achieve adaptive sampling in end-to-end optimization with reconstruction or clinical task...
WHY NOW
Medical AI moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
An information-theoretic framework for task-adapted compressed sensing MRI that enables probabilistic inference for uncertainty prediction and adaptive sampling for clinical tasks. However, existing task-adapted CS-MRI methods suffer from the uncertainty problem for medical diagnosis and cannot achieve adaptive sampling in end-to-end optimization with reconstruction or clinical tasks.
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partial
Task-adapted compressed sensing magnetic resonance imaging (CS-MRI) is emerging to address the specific demands of downstream clinical tasks with significantly fewer k-space measurements than required by Nyquist sampling. However, existing task-adapted CS-MRI methods suffer from the uncertainty problem for medical diagnosis and cannot achieve adaptive sampling in end-to-end optimization with reconstruction or clinical tasks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. However, existing task-adapted CS-MRI methods suffer from the uncertainty problem for medical diagnosis and cannot achieve adaptive sampling in end-to-end optimization with reconstruction or clinical tasks. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Medical AI moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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An information-theoretic framework for task-adapted compressed sensing MRI that enables probabilistic inference for uncertainty prediction and adaptive sampling for clinical tasks.
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