Conference proceeding
Model-Based Deep Learning for Reconstruction of Joint k-q Under-sampled High Resolution Diffusion MRI
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Vol.2020, pp.913-916
04/2020
DOI: 10.1109/ISBI45749.2020.9098593
PMID: 33574989
Abstract
We propose a model-based deep learning architecture for the reconstruction of highly accelerated diffusion magnetic resonance imaging (MRI) that enables high resolution imaging. The proposed reconstruction jointly recovers all the diffusion weighted images in a single step from a joint k-q under-sampled acquisition in a parallel MRI setting. We propose the novel use of a pre-trained denoiser as a regularizer in a model-based reconstruction for the recovery of highly under-sampled data. Specifically, we designed the denoiser based on a general diffusion MRI tissue microstructure model for multi-compartmental modeling. By using a wide range of biologically plausible parameter values for the multi-compartmental microstructure model, we simulated diffusion signal that spans the entire microstructure parameter space. A neural network was trained in an unsupervised manner using an autoencoder to learn the diffusion MRI signal subspace. We employed the autoencoder in a model-based reconstruction and show that the autoencoder provides a strong denoising prior to recover the q-space signal. We show reconstruction results on a simulated brain dataset that shows high acceleration capabilities of the proposed method.
Details
- Title: Subtitle
- Model-Based Deep Learning for Reconstruction of Joint k-q Under-sampled High Resolution Diffusion MRI
- Creators
- Merry P Mani - University of Iowa,Iowa,USAHemant K Aggarwal - University of Iowa,Iowa,USASanjay Ghosh - 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.913-916
- DOI
- 10.1109/ISBI45749.2020.9098593
- PMID
- 33574989
- 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
- 9984070995102771
Metrics
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