CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| _version_ | 1866912238469644288 |
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| author | Xu, Xiaojian Gan, Weijie Kothapalli, Satya V. V. N. Yablonskiy, Dmitriy A. Kamilov, Ulugbek S. |
| author_facet | Xu, Xiaojian Gan, Weijie Kothapalli, Satya V. V. N. Yablonskiy, Dmitriy A. Kamilov, Ulugbek S. |
| contents | Quantitative MRI (qMRI) refers to a class of MRI methods for quantifying the spatial distribution of biological tissue parameters. Traditional qMRI methods usually deal separately with artifacts arising from accelerated data acquisition, involuntary physical motion, and magnetic-field inhomogeneities, leading to suboptimal end-to-end performance. This paper presents CoRRECT, a unified deep unfolding (DU) framework for qMRI consisting of a model-based end-to-end neural network, a method for motion-artifact reduction, and a self-supervised learning scheme. The network is trained to produce R2* maps whose k-space data matches the real data by also accounting for motion and field inhomogeneities. When deployed, CoRRECT only uses the k-space data without any pre-computed parameters for motion or inhomogeneity correction. Our results on experimentally collected multi-Gradient-Recalled Echo (mGRE) MRI data show that CoRRECT recovers motion and inhomogeneity artifact-free R2* maps in highly accelerated acquisition settings. This work opens the door to DU methods that can integrate physical measurement models, biophysical signal models, and learned prior models for high-quality qMRI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_06330 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping Xu, Xiaojian Gan, Weijie Kothapalli, Satya V. V. N. Yablonskiy, Dmitriy A. Kamilov, Ulugbek S. Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Quantitative MRI (qMRI) refers to a class of MRI methods for quantifying the spatial distribution of biological tissue parameters. Traditional qMRI methods usually deal separately with artifacts arising from accelerated data acquisition, involuntary physical motion, and magnetic-field inhomogeneities, leading to suboptimal end-to-end performance. This paper presents CoRRECT, a unified deep unfolding (DU) framework for qMRI consisting of a model-based end-to-end neural network, a method for motion-artifact reduction, and a self-supervised learning scheme. The network is trained to produce R2* maps whose k-space data matches the real data by also accounting for motion and field inhomogeneities. When deployed, CoRRECT only uses the k-space data without any pre-computed parameters for motion or inhomogeneity correction. Our results on experimentally collected multi-Gradient-Recalled Echo (mGRE) MRI data show that CoRRECT recovers motion and inhomogeneity artifact-free R2* maps in highly accelerated acquisition settings. This work opens the door to DU methods that can integrate physical measurement models, biophysical signal models, and learned prior models for high-quality qMRI. |
| title | CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2210.06330 |