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

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

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

Computer Vision and Pattern Recognition 2023-04-04 v1 Signal Processing

Authors: Yan Chen , James H. Holmes , Curtis Corum , Vincent Magnotta , Mathews Jacob

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.

Keywords

nonnegative matrix factorization fingerprint recognition image reconstruction

Cite

@article{arxiv.2304.00102,
  title  = {Deep Factor Model: A Novel Approach for Motion Compensated Multi-Dimensional MRI},
  author = {Yan Chen and James H. Holmes and Curtis Corum and Vincent Magnotta and Mathews Jacob},
  journal= {arXiv preprint arXiv:2304.00102},
  year   = {2023}
}

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4 pages, 4 figures

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

Abstract

Keywords

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