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MRI Reconstruction Risk Estimation from Inference Dynamics

Tamir Shor, Alex Bronstein · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26558 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

迭代MRI重建器在返回最终图像之前生产中间估计。我们研究这些推断轨迹作为实现重建风险的模型内部表示,由log-NMSE衡量。模型原生状态被投射到大小图像,由固定16维描述器总结,并通过源训练的边缘读取来绘制风险。在三个重建背骨中,轨迹特征在持有源数据上强烈预测重建风险。在零射击目标转移中,他们的行为是背骨依赖的,显示既保留积极关联,又反转反转。 条件分析表明,轨道信息可以与仅启动预测器以外的错误相关。 Afix预测进一步分析表明,有用的轨道信息可以在完成重建之前出现。 重复案例的转换和低调

Machine translation (Meta M2M100); the English original follows.

Abstract

Iterative MRI reconstructors produce intermediate estimates before returning a final image. We study these inference trajectories as a model-internal representation of realized reconstruction risk, measured by log-NMSE. Model-native states are projected to magnitude images, summarized by a fixed 16-dimensional descriptor, and mapped to risk using a source-trained ridge readout. Across three reconstruction backbones, trajectory features are strongly predictive of reconstruction risk on held-out source data. Under zero-shot target shift, their behavior is backbone-dependent, exhibiting both preserved positive association and readout reversal. Conditional analyses show that trajectory information can remain associated with error beyond an initialization-only predictor. A prefix analysis further shows that useful trajectory information can emerge before reconstruction completes. Repeated-mask and low-shot calibration studies indicate that trajectories primarily encode persistent case difficulty, while their orientation and calibration can change under domain shift.

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Topics: Trajectory, Inference, Computer science, Calibration, Artificial intelligence, Iterative reconstruction

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