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arXiv · 2304.00102

Deep Factor Model: A Novel Approach for Motion Compensated Multi-Dimensional MRI

Yan Chen·James H. Holmes·Curtis Corum·Vincent Magnotta·Mathews Jacob
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Abstract

Recent quantitative parameter mapping methods including MR fingerprinting (MRF) collect a time series of images that capture the evolution of magnetization. The focus of this work is to introduce a novel approach termed as Deep Factor Model(DFM), which offers an efficient representation of the multi-contrast image time series. The higher efficiency of the representation enables the acquisition of the images in a highly undersampled fashion, which translates to reduced scan time in 3D high-resolution multi-contrast applications. The approach integrates motion estimation and compensation, making the approach robust to subject motion during the scan.

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Yan Chen, James H. Holmes, Curtis Corum, Vincent Magnotta, Mathews Jacob. 2023-03-31. Deep Factor Model: A Novel Approach for Motion Compensated Multi-Dimensional MRI. https://arxiv.org/abs/2304.00102

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