Public articles linked to the same research event.
arXiv The work proposes MPFlow, a zero-shot multi-modal MRI reconstruction framework built on rectified flow that uses a self-supervised pretraining strategy, PAMRI, to learn shared cross-modal representations and jointly guides the unconditional prior with data consistency and cross-modal feature alignment at inference; on HCP T2 4x super-resolution and BraTS FLAIR 8x k-space reconstruction it matches diffusion baselines in image quality using only 20% of the sampling steps while improving tumor segmentation Dice by 15% and reducing the SHAFE hallucination score by 26%.
The work proposes MPFlow, a zero-shot multi-modal MRI reconstruction framework built on rectified flow that uses a self-supervised pretraining strategy, PAMRI, to learn shared cross-modal representations and jointly guides the unconditional prior with data consistency and cross-modal feature alignment at inference; on HCP T2 4x super-resolution and BraTS FLAIR 8x k-space reconstruction it matches diffusion baselines in image quality using only 20% of the sampling steps while improving tumor segmentation Dice by 15% and reducing the SHAFE hallucination score by 26%.
The work proposes MPFlow, a zero-shot multi-modal MRI reconstruction framework built on rectified flow that uses a self-supervised pretraining strategy, PAMRI, to learn shared cross-modal representations and jointly guides the unconditional prior with data consistency and cross-modal feature alignment at inference; on HCP T2 4x super-resolution and BraTS FLAIR 8x k-space reconstruction it matches diffusion baselines in image quality using only 20% of the sampling steps while improving tumor segmentation Dice by 15% and reducing the SHAFE hallucination score by 26%.
The work proposes MPFlow, a zero-shot multi-modal MRI reconstruction framework built on rectified flow that uses a self-supervised pretraining strategy, PAMRI, to learn shared cross-modal representations and jointly guides the unconditional prior with data consistency and cross-modal feature alignment at inference; on HCP T2 4x super-resolution and BraTS FLAIR 8x k-space reconstruction it matches diffusion baselines in image quality using only 20% of the sampling steps while improving tumor segmentation Dice by 15% and reducing the SHAFE hallucination score by 26%.