Conference proceeding
Deep Factor Model: A Novel Approach for Motion Compensated Multi-Dimensional MRI
2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pp.1-4
04/18/2023
DOI: 10.1109/ISBI53787.2023.10230725
PMCID: PMC11087023
PMID: 38738186
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 Chen - University of IowaJames H. Holmes - University of IowaCurtis Corum - Champaign Imaging, LLCVincent Magnotta - University of IowaMathews Jacob - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pp.1-4
- DOI
- 10.1109/ISBI53787.2023.10230725
- PMID
- 38738186
- PMCID
- PMC11087023
- NLM abbreviation
- Proc IEEE Int Symp Biomed Imaging
- eISSN
- 1945-8452
- Publisher
- IEEE
- Language
- English
- Date published
- 04/18/2023
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Psychiatry; Iowa Neuroscience Institute; Radiation Oncology
- Record Identifier
- 9984459614002771
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