CAST: Conditional Acquisition-Structured Transport for Accelerated MRI

Generative models are increasingly popular in accelerated MRI reconstruction. However, generative methods typically introduce acquisition operators and data consistency only at inference. We investigate how acquisition information can improve flow matching-based generative MRI reconstruction when incorporated during training. The result is Conditional Acquisition-Structured Transport (CAST), which uses acquisition operators to structure both the source distribution and the velocity field of the flow matching model. Sampling via CAST admits a corresponding k-space trajectory that preserves acquired measurements. We evaluate CAST on 2D, 3D, and cardiac cine MRI. CAST significantly improves reconstruction over network-input conditioning alone and enables the perception–distortion trade-off to be adjusted at inference. Averaging multiple samples matches or exceeds state-of-the-art unrolled networks in PSNR, closing the distortion gap that generative methods typically exhibit, while single-sample reconstructions achieve greater distributional similarity to reference images than generative baselines. On cardiac cine MRI, CAST can reconstruct fine details and reduce artifacts.