Conference proceeding
Joint Optimization of Sampling Pattern and Priors in Model Based Deep Learning
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Vol.2020, pp.926-929
04/2020
DOI: 10.1109/ISBI45749.2020.9098639
PMID: 33584975
Abstract
Deep learning methods are emerging as powerful alternatives for compressed sensing MRI to recover images from highly undersampled data. Unlike compressed sensing, the image redundancies that are captured by these models are not well understood. The lack of theoretical understanding also makes it challenging to choose the sampling pattern that would yield the best possible recovery. To overcome these challenges, we propose to optimize the sampling patterns and the parameters of the reconstruction block in a model-based deep learning framework. We show that the joint optimization by the model-based strategy results in improved performance than direct inversion CNN schemes due to better decoupling of the effect of sampling and image properties. The quantitative and qualitative results confirm the benefits of joint optimization by the model-based scheme over the direct inversion strategy.
Details
- Title: Subtitle
- Joint Optimization of Sampling Pattern and Priors in Model Based Deep Learning
- Creators
- Hemant K Aggarwal - University of Iowa,Iowa,USAMathews Jacob - University of Iowa,Iowa,USA
- Resource Type
- Conference proceeding
- Publication Details
- 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Vol.2020, pp.926-929
- DOI
- 10.1109/ISBI45749.2020.9098639
- PMID
- 33584975
- 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
- 9984070620502771
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