Conference proceeding
Dynamic MRI using deep manifold self-learning
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Vol.2020, pp.1052-1055
04/2020
DOI: 10.1109/ISBI45749.2020.9098382
PMID: 33603956
Abstract
We propose a deep self-learning algorithm to learn the manifold structure of free-breathing and ungated cardiac data and to recover the cardiac CINE MRI from highly under-sampled measurements. Our method learns the manifold structure in the dynamic data from navigators using autoencoder network. The trained autoencoder is then used as a prior in the image reconstruction framework. We have tested the proposed method on free-breathing and ungated cardiac CINE data, which is acquired using a navigated golden-angle gradient-echo radial sequence. Results show the ability of our method to better capture the manifold structure, thus providing us reduced spatial and temporal blurring as compared to the SToRM reconstruction.
Details
- Title: Subtitle
- Dynamic MRI using deep manifold self-learning
- Creators
- Abdul Haseeb Ahmed - University of IOWA,Department of Electrical and Computer EngineeringHemant Aggarwal - University of IOWA,Department of Electrical and Computer EngineeringPrashant Nagpal - University of IOWA,Department of Electrical and Computer EngineeringMathews Jacob - University of IOWA,Department of Electrical and Computer Engineering
- Resource Type
- Conference proceeding
- Publication Details
- 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Vol.2020, pp.1052-1055
- DOI
- 10.1109/ISBI45749.2020.9098382
- PMID
- 33603956
- NLM abbreviation
- Proc IEEE Int Symp Biomed Imaging
- ISSN
- 1945-7928
- eISSN
- 1945-8452
- Publisher
- IEEE
- Language
- English
- Date published
- 04/2020
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Iowa Neuroscience Institute; Radiation Oncology
- Record Identifier
- 9984070286702771
Metrics
17 Record Views