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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

German

Iterative MRI-Rekonstruktoren produzieren Zwischenabschätzungen, bevor sie ein endgültiges Bild zurückgeben. Wir untersuchen diese Schlußbahnen als modellinterne Darstellung des realisierten Rekonstruktionsrisikos, gemessen durch log-NMSE. Modell-native Zustände werden auf Größenabschätzungen projiziert, durch einen festen 16-dimensionalen Descriptor zusammengefasst und mappiert zu Risiken mithilfe eines Quellen-trained Ridge-Readout. Über drei Rekonstruktions-Rackbänke, Trajectory-Features sind stark vorausschauend von Rekonstruktionsrisiken auf gehaltenen-out-Quelldaten. Unter Null-Shot-Zielwechsel, ihr Verhalten ist Rückgrat-abhängig, zeigt sowohl

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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