Recycling diagnostic MRI for empowering brain morphometric research – Critical & practical assessment on learning-based image super-resolution
Preliminary studies have shown the feasibility of deep learning (DL)-based super-resolution (SR) technique for reconstructing thick-slice/gap diagnostic MR images into high-resolution isotropic data, which would be of great significance for brain research field if the vast amount of diagnostic MRI d...
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2021
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oai:doaj.org-article:ff567b0fdbc84ce991ece2a84fce771a2021-11-04T04:26:47ZRecycling diagnostic MRI for empowering brain morphometric research – Critical & practical assessment on learning-based image super-resolution1095-957210.1016/j.neuroimage.2021.118687https://doaj.org/article/ff567b0fdbc84ce991ece2a84fce771a2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1053811921009605https://doaj.org/toc/1095-9572Preliminary studies have shown the feasibility of deep learning (DL)-based super-resolution (SR) technique for reconstructing thick-slice/gap diagnostic MR images into high-resolution isotropic data, which would be of great significance for brain research field if the vast amount of diagnostic MRI data could be successively put into brain morphometric study. However, less evidence has addressed the practicability of the strategy, because lack of a large-sample available real data for constructing DL model. In this work, we employed a large cohort (n = 2052) of peculiar data with both low through-plane resolution diagnostic and high-resolution isotropic brain MR images from identical subjects. By leveraging a series of SR approaches, including a proposed novel DL algorithm of Structure Constrained Super Resolution Network (SCSRN), the diagnostic images were transformed to high-resolution isotropic data to meet the criteria of brain research in voxel-based and surface-based morphometric analyses. We comprehensively assessed image quality and the practicability of the reconstructed data in a variety of morphometric analysis scenarios. We further compared the performance of SR approaches to the ground truth high-resolution isotropic data. The results showed (i) DL-based SR algorithms generally improve the quality of diagnostic images and render morphometric analysis more accurate, especially, with the most superior performance of the novel approach of SCSRN. (ii) Accuracies vary across brain structures and methods, and (iii) performance increases were higher for voxel than for surface based approaches. This study supports that DL-based image super-resolution potentially recycle huge amount of routine diagnostic brain MRI deposited in sleeping state, and turning them into useful data for neurometric research.Gaoping LiuZehong CaoQiang XuQirui ZhangFang YangXinyu XieJingru HaoYinghuan ShiBoris C. BernhardtYichu HeFeng ShiGuangming LuZhiqiang ZhangElsevierarticleDeep-learningImage super-resolutionBrain MRIMorphometric analysisPractical assessmentNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENNeuroImage, Vol 245, Iss , Pp 118687- (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Deep-learning Image super-resolution Brain MRI Morphometric analysis Practical assessment Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 |
| spellingShingle |
Deep-learning Image super-resolution Brain MRI Morphometric analysis Practical assessment Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Gaoping Liu Zehong Cao Qiang Xu Qirui Zhang Fang Yang Xinyu Xie Jingru Hao Yinghuan Shi Boris C. Bernhardt Yichu He Feng Shi Guangming Lu Zhiqiang Zhang Recycling diagnostic MRI for empowering brain morphometric research – Critical & practical assessment on learning-based image super-resolution |
| description |
Preliminary studies have shown the feasibility of deep learning (DL)-based super-resolution (SR) technique for reconstructing thick-slice/gap diagnostic MR images into high-resolution isotropic data, which would be of great significance for brain research field if the vast amount of diagnostic MRI data could be successively put into brain morphometric study. However, less evidence has addressed the practicability of the strategy, because lack of a large-sample available real data for constructing DL model. In this work, we employed a large cohort (n = 2052) of peculiar data with both low through-plane resolution diagnostic and high-resolution isotropic brain MR images from identical subjects. By leveraging a series of SR approaches, including a proposed novel DL algorithm of Structure Constrained Super Resolution Network (SCSRN), the diagnostic images were transformed to high-resolution isotropic data to meet the criteria of brain research in voxel-based and surface-based morphometric analyses. We comprehensively assessed image quality and the practicability of the reconstructed data in a variety of morphometric analysis scenarios. We further compared the performance of SR approaches to the ground truth high-resolution isotropic data. The results showed (i) DL-based SR algorithms generally improve the quality of diagnostic images and render morphometric analysis more accurate, especially, with the most superior performance of the novel approach of SCSRN. (ii) Accuracies vary across brain structures and methods, and (iii) performance increases were higher for voxel than for surface based approaches. This study supports that DL-based image super-resolution potentially recycle huge amount of routine diagnostic brain MRI deposited in sleeping state, and turning them into useful data for neurometric research. |
| format |
article |
| author |
Gaoping Liu Zehong Cao Qiang Xu Qirui Zhang Fang Yang Xinyu Xie Jingru Hao Yinghuan Shi Boris C. Bernhardt Yichu He Feng Shi Guangming Lu Zhiqiang Zhang |
| author_facet |
Gaoping Liu Zehong Cao Qiang Xu Qirui Zhang Fang Yang Xinyu Xie Jingru Hao Yinghuan Shi Boris C. Bernhardt Yichu He Feng Shi Guangming Lu Zhiqiang Zhang |
| author_sort |
Gaoping Liu |
| title |
Recycling diagnostic MRI for empowering brain morphometric research – Critical & practical assessment on learning-based image super-resolution |
| title_short |
Recycling diagnostic MRI for empowering brain morphometric research – Critical & practical assessment on learning-based image super-resolution |
| title_full |
Recycling diagnostic MRI for empowering brain morphometric research – Critical & practical assessment on learning-based image super-resolution |
| title_fullStr |
Recycling diagnostic MRI for empowering brain morphometric research – Critical & practical assessment on learning-based image super-resolution |
| title_full_unstemmed |
Recycling diagnostic MRI for empowering brain morphometric research – Critical & practical assessment on learning-based image super-resolution |
| title_sort |
recycling diagnostic mri for empowering brain morphometric research – critical & practical assessment on learning-based image super-resolution |
| publisher |
Elsevier |
| publishDate |
2021 |
| url |
https://doaj.org/article/ff567b0fdbc84ce991ece2a84fce771a |
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