Unsupervised MR harmonization by learning disentangled representations using information bottleneck theory
•Unsupervised MR harmonization without traveling subjects.•Unified latent space for MR contrast synthesis.•A novel framework for disentangling contrast and anatomy in MR images.•Downstream segmentation consistency shows significant improvements after harmonization. In magnetic resonance (MR) imaging...
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| Published in | NeuroImage (Orlando, Fla.) Vol. 243; p. 118569 |
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| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
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Elsevier Inc
01.11.2021
Elsevier Limited Elsevier |
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| Abstract | •Unsupervised MR harmonization without traveling subjects.•Unified latent space for MR contrast synthesis.•A novel framework for disentangling contrast and anatomy in MR images.•Downstream segmentation consistency shows significant improvements after harmonization.
In magnetic resonance (MR) imaging, a lack of standardization in acquisition often causes pulse sequence-based contrast variations in MR images from site to site, which impedes consistent measurements in automatic analyses. In this paper, we propose an unsupervised MR image harmonization approach, CALAMITI (Contrast Anatomy Learning and Analysis for MR Intensity Translation and Integration), which aims to alleviate contrast variations in multi-site MR imaging. Designed using information bottleneck theory, CALAMITI learns a globally disentangled latent space containing both anatomical and contrast information, which permits harmonization. In contrast to supervised harmonization methods, our approach does not need a sample population to be imaged across sites. Unlike traditional unsupervised harmonization approaches which often suffer from geometry shifts, CALAMITI better preserves anatomy by design. The proposed method is also able to adapt to a new testing site with a straightforward fine-tuning process. Experiments on MR images acquired from ten sites show that CALAMITI achieves superior performance compared with other harmonization approaches. |
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| AbstractList | •Unsupervised MR harmonization without traveling subjects.•Unified latent space for MR contrast synthesis.•A novel framework for disentangling contrast and anatomy in MR images.•Downstream segmentation consistency shows significant improvements after harmonization.
In magnetic resonance (MR) imaging, a lack of standardization in acquisition often causes pulse sequence-based contrast variations in MR images from site to site, which impedes consistent measurements in automatic analyses. In this paper, we propose an unsupervised MR image harmonization approach, CALAMITI (Contrast Anatomy Learning and Analysis for MR Intensity Translation and Integration), which aims to alleviate contrast variations in multi-site MR imaging. Designed using information bottleneck theory, CALAMITI learns a globally disentangled latent space containing both anatomical and contrast information, which permits harmonization. In contrast to supervised harmonization methods, our approach does not need a sample population to be imaged across sites. Unlike traditional unsupervised harmonization approaches which often suffer from geometry shifts, CALAMITI better preserves anatomy by design. The proposed method is also able to adapt to a new testing site with a straightforward fine-tuning process. Experiments on MR images acquired from ten sites show that CALAMITI achieves superior performance compared with other harmonization approaches. In magnetic resonance (MR) imaging, a lack of standardization in acquisition often causes pulse sequence-based contrast variations in MR images from site to site, which impedes consistent measurements in automatic analyses. In this paper, we propose an unsupervised MR image harmonization approach, CALAMITI (Contrast Anatomy Learning and Analysis for MR Intensity Translation and Integration), which aims to alleviate contrast variations in multi-site MR imaging. Designed using information bottleneck theory, CALAMITI learns a globally disentangled latent space containing both anatomical and contrast information, which permits harmonization. In contrast to supervised harmonization methods, our approach does not need a sample population to be imaged across sites. Unlike traditional unsupervised harmonization approaches which often suffer from geometry shifts, CALAMITI better preserves anatomy by design. The proposed method is also able to adapt to a new testing site with a straightforward fine-tuning process. Experiments on MR images acquired from ten sites show that CALAMITI achieves superior performance compared with other harmonization approaches.In magnetic resonance (MR) imaging, a lack of standardization in acquisition often causes pulse sequence-based contrast variations in MR images from site to site, which impedes consistent measurements in automatic analyses. In this paper, we propose an unsupervised MR image harmonization approach, CALAMITI (Contrast Anatomy Learning and Analysis for MR Intensity Translation and Integration), which aims to alleviate contrast variations in multi-site MR imaging. Designed using information bottleneck theory, CALAMITI learns a globally disentangled latent space containing both anatomical and contrast information, which permits harmonization. In contrast to supervised harmonization methods, our approach does not need a sample population to be imaged across sites. Unlike traditional unsupervised harmonization approaches which often suffer from geometry shifts, CALAMITI better preserves anatomy by design. The proposed method is also able to adapt to a new testing site with a straightforward fine-tuning process. Experiments on MR images acquired from ten sites show that CALAMITI achieves superior performance compared with other harmonization approaches. In magnetic resonance (MR) imaging, a lack of standardization in acquisition often causes pulse sequence-based contrast variations in MR images from site to site, which impedes consistent measurements in automatic analyses. In this paper, we propose an unsupervised MR image harmonization approach, CALAMITI (Contrast Anatomy Learning and Analysis for MR Intensity Translation and Integration), which aims to alleviate contrast variations in multi-site MR imaging. Designed using information bottleneck theory, CALAMITI learns a globally disentangled latent space containing both anatomical and contrast information, which permits harmonization. In contrast to supervised harmonization methods, our approach does not need a sample population to be imaged across sites. Unlike traditional unsupervised harmonization approaches which often suffer from geometry shifts, CALAMITI better preserves anatomy by design. The proposed method is also able to adapt to a new testing site with a straightforward fine-tuning process. Experiments on MR images acquired from ten sites show that CALAMITI achieves superior performance compared with other harmonization approaches. In magnetic resonance (MR) imaging, a lack of standardization in acquisition often causes pulse sequence-based contrast variations in MR images from site to site, which impedes consistent measurements in automatic analyses. In this paper, we propose an unsupervised MR image harmonization approach, CALAMITI (Contrast Anatomy Learning and Analysis for MR Intensity Translation and Integration), which aims to alleviate contrast variations in multi-site MR imaging. Designed using information bottleneck theory, CALAMITI learns a globally disentangled latent space containing both anatomical and contrast information, which permits harmonization. In contrast to supervised harmonization methods, our approach does not need a sample population to be imaged across sites. Unlike traditional unsupervised harmonization approaches which often suffer from geometry shifts, CALAMITI better preserves anatomy by design. The proposed method is also able to adapt to a new testing site with a straightforward fine-tuning process. Experiments on MR images acquired from ten sites show that CALAMITI achieves superior performance compared with other harmonization approaches. |
| ArticleNumber | 118569 |
| Author | Mowry, Ellen M. Resnick, Susan M. Dewey, Blake E. He, Yufan Newsome, Scott D. Liu, Yihao Carass, Aaron Prince, Jerry L. Zuo, Lianrui |
| Author_xml | – sequence: 1 givenname: Lianrui surname: Zuo fullname: Zuo, Lianrui email: lr_zuo@jhu.edu organization: Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218 USA – sequence: 2 givenname: Blake E. surname: Dewey fullname: Dewey, Blake E. organization: Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218 USA – sequence: 3 givenname: Yihao surname: Liu fullname: Liu, Yihao organization: Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218 USA – sequence: 4 givenname: Yufan surname: He fullname: He, Yufan organization: Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218 USA – sequence: 5 givenname: Scott D. surname: Newsome fullname: Newsome, Scott D. organization: Department of Neurology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA – sequence: 6 givenname: Ellen M. surname: Mowry fullname: Mowry, Ellen M. organization: Department of Neurology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA – sequence: 7 givenname: Susan M. surname: Resnick fullname: Resnick, Susan M. organization: Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institute of Health, Baltimore, MD 20892, USA – sequence: 8 givenname: Jerry L. surname: Prince fullname: Prince, Jerry L. organization: Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218 USA – sequence: 9 givenname: Aaron surname: Carass fullname: Carass, Aaron organization: Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218 USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34506916$$D View this record in MEDLINE/PubMed |
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| Keywords | Image-to-image translation Image synthesis Disentangle Magnetic resonance imaging Harmonization |
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| Snippet | •Unsupervised MR harmonization without traveling subjects.•Unified latent space for MR contrast synthesis.•A novel framework for disentangling contrast and... In magnetic resonance (MR) imaging, a lack of standardization in acquisition often causes pulse sequence-based contrast variations in MR images from site to... In magnetic resonance (MR) imaging, a lack of standardization in acquisition often causes pulse sequence-based contrast variations in MR images from site to... |
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| SubjectTerms | Algorithms Anatomy Disentangle Harmonization Humans Image Processing, Computer-Assisted - methods Image synthesis Image-to-image translation Information Theory Learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Scanners Standardization |
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| Title | Unsupervised MR harmonization by learning disentangled representations using information bottleneck theory |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S1053811921008429 https://dx.doi.org/10.1016/j.neuroimage.2021.118569 https://www.ncbi.nlm.nih.gov/pubmed/34506916 https://www.proquest.com/docview/2578817891 https://www.proquest.com/docview/2571917097 https://doaj.org/article/c7f72d8dec0146e8ad69609eb4990e55 |
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