Conference proceeding
Model based image reconstruction using deep learned priors (MODL)
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Vol.2018-, pp.671-674
04/2018
DOI: 10.1109/ISBI.2018.8363663
PMID: 33584973
Abstract
We introduce a model-based image reconstruction framework, where we use a deep convolution neural network (CNN) based regularization prior. We rely on a recursive algorithm, which alternates between a CNN based denoising step and enforcement of data consistency. Unrolling the recursive algorithm yields a deep network that is trained using backpropagation. The unique aspect of this method is the use of the same CNN weights at each iteration, which makes the resulting structure consistent with the model-based formulation. Also, this approach reduces the number of trainable parameters, which hence lower the amount of training data needed. The use of a forward model also reduces the size of the network and enables the exploitation additional prior information available from calibration data. The use of the framework for multichannel MRI reconstruction provides improved reconstructions, compared to other state-of-the-art methods.
Details
- Title: Subtitle
- Model based image reconstruction using deep learned priors (MODL)
- Creators
- Hemant Kumar Aggarwal - University of Iowa, Iowa, USAMerry P Mani - University of Iowa, Iowa, USAMathews Jacob - University of Iowa, Iowa, USA
- Resource Type
- Conference proceeding
- Publication Details
- 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Vol.2018-, pp.671-674
- DOI
- 10.1109/ISBI.2018.8363663
- PMID
- 33584973
- NLM abbreviation
- Proc IEEE Int Symp Biomed Imaging
- ISSN
- 1945-7928
- eISSN
- 1945-8452
- Publisher
- IEEE
- Language
- English
- Date published
- 04/2018
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Iowa Neuroscience Institute; Radiation Oncology
- Record Identifier
- 9984070632702771
Metrics
20 Record Views