Stochastic Finite-Transition Learning and Exploration for Accelerated MRI Reconstruction
Generative MRI reconstruction maps an initial source distribution to the target image distribution along a learned recovery trajectory. Common generative methods, such as diffusion and flow, typically learn fine-grained local dynamics whose integration defines this global mapping, hence requiring many network evaluations at inference. Existing few-step sampling approaches can lower computational burden by approximating large transformations from local dynamics, albeit at the expense of reconstruction fidelity. FLEX-MRI instead partitions recovery into a small number of finite intervals between retained states defined by progressive changes in k-space support, and directly learns the recon...
| Authors | Muhammad Usama Mirza, Esin Öztürk Işık, Tolga Çukur |
|---|---|
| Primary category | eess.IV |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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