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Region-of-Interest Adaptive Acquisition for Accelerated MRI
Zihui Wu · Tianwei Yin · Adrian Dalca · Katherine Bouman

We define and tackle region-of-interest adaptive (RoI-adaptive) acquisition for accelerated MRI. Existing methods for identifying k-space sampling patterns in accelerated MRI are optimized for the quality of the entire image or a general image-wide task. However, MRI is often acquired to image a specific RoI, such as a suspected pathology. We demonstrate that a sampling strategy that serves for a general multi-purpose task is often suboptimal for each individual objective. We propose a framework that efficiently learns MRI sampling masks specific to the RoI, leading to substantially faster acquisition that still enables accurate analysis of the RoI. We show empirically that our RoI-adaptive acquisition approach significantly outperforms general acquisition baselines in the RoI reconstruction and segmentation tasks.

Author Information

Zihui Wu (Caltech)
Tianwei Yin (UT Austin)
Adrian Dalca (MIT, HMS)
Katherine Bouman (Caltech)

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