用於自回歸MRI重建的下一加速比例預測

摘要

MRI重建本質上是一個病態逆問題，因為不完整的量測會產生多種可能的解。在高加速倍數下，這種模糊性變得更加嚴重，此時像素域的連續預測器往往會對可行的重建結果進行平均化，並抑制高頻解剖結構。為了解決此限制，我們將重建移至離散多尺度潛在空間，並將其視為自回歸式的下一個加速尺度預測。利用在視覺自回歸建模中已被證實有效的離散先驗知識，我們的方法將解限制為碼本標記的緊湊序列，即使在極稀疏的量測下也能實現清晰的重建。這種離散自回歸的表述方式也自然地與現代大型語言模型的後訓練技術相吻合。基於此觀察，我們引入了針對視覺自回歸建模的同策略特權信息蒸餾，其中教師模型僅使用在推理時無法獲得的特權上下文（在本例中為全採樣採集）進行訓練，並監督學生模型根據其自身的生成序列進行訓練，從而實現一致的重建增益。透過在fastMRI基準測試上的廣泛實驗，我們證明，在極度欠採樣的情況下，我們的方法能在多種採樣模式下提供改進的重建表現。專案網站為：https://yilmazkorkmaz1.github.io/discrete-mri-reconstruction-opd/{here}。

English

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 to discrete multi-scale latent space and posing it as autoregressive next-acceleration-scale prediction. Leveraging discrete priors proven effective in visual autoregressive modeling, our method restricts the solution to compact sequences of codebook 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 introduce on-policy privileged information distillation for visual 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 the fastMRI benchmark, we show that our approach delivers improved reconstruction performance across diverse sampling patterns under extreme undersampling. Project website is https://yilmazkorkmaz1.github.io/discrete-mri-reconstruction-opd/{here}.