Next-Acceleration-Scale Prediction for Autoregressive MRI Reconstruction
Summary
Discrete autoregressive MRI reconstruction using privileged information distillation achieves superior performance under extreme undersampling by leveraging visual autoregressive modeling techniques.
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Paper page - Next-Acceleration-Scale Prediction for Autoregressive MRI Reconstruction
Source: https://huggingface.co/papers/2605.19354
Abstract
Discrete autoregressive MRI reconstruction using privileged information distillation achieves superior performance under extreme undersampling by leveraging visual autoregressive modeling techniques.
MRI reconstruction is an inherently ill-posed inverse problem, since incomplete measurements admit many plausible solutions. This ambiguity becomes more severe under high acceleration, where pixel-domain continuous predictors tend to average over feasible reconstructions and suppress high-frequency anatomy. We address this limitation by moving reconstruction todiscrete multi-scale latent spaceand posing it asautoregressive next-acceleration-scale prediction. Leveraging discrete priors proven effective invisual autoregressive modeling, our method restricts the solution to compact sequences ofcodebook tokens, enabling sharp reconstructions even from extremely sparse measurements. This discrete autoregressive formulation also aligns naturally with modern large language model post-training techniques. Building on this observation, we introduceon-policyprivileged information distillationforvisual autoregressive modeling, where a teacher is provided training only privileged context that is unavailable at inference, in our case fully sampled acquisitions, and supervises a student trained on its own rollouts, leading to consistent reconstruction gains. Through extensive experiments on thefastMRI benchmark, we show that our approach delivers improved reconstruction performance across diverse sampling patterns underextreme undersampling. Project website is https://yilmazkorkmaz1.github.io/discrete-mri-reconstruction-opd/{here}.
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