Preprint
Deep Factor Model: A Novel Approach for Motion Compensated Multi-Dimensional MRI
ArXiv.org
03/31/2023
DOI: 10.48550/arxiv.2304.00102
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.
Details
- Title: Subtitle
- Deep Factor Model: A Novel Approach for Motion Compensated Multi-Dimensional MRI
- Creators
- Yan ChenJames H HolmesCurtis CorumVincent MagnottaMathews Jacob
- Resource Type
- Preprint
- Publication Details
- ArXiv.org
- DOI
- 10.48550/arxiv.2304.00102
- ISSN
- 2331-8422
- Language
- English
- Date posted
- 03/31/2023
- Academic Unit
- Psychiatry; Electrical and Computer Engineering; Roy J. Carver Department of Biomedical Engineering; Radiology; Iowa Neuroscience Institute; Radiation Oncology
- Record Identifier
- 9984386256802771