Recycling diagnostic MRI for empowering brain morphometric research – Critical & practical assessment on learning-based image super-resolution
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
Affiliations
Gaoping Liu
Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, #305 East Zhongshan Rd, Nanjing, Jiangsu 210002, China
Zehong Cao
Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; School of Biomedical Engineering, Southern Medical University, Guangzhou, China
Qiang Xu
Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, #305 East Zhongshan Rd, Nanjing, Jiangsu 210002, China
Qirui Zhang
Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, #305 East Zhongshan Rd, Nanjing, Jiangsu 210002, China
Fang Yang
Department of Neurology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, China
Xinyu Xie
Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, #305 East Zhongshan Rd, Nanjing, Jiangsu 210002, China
Jingru Hao
Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, #305 East Zhongshan Rd, Nanjing, Jiangsu 210002, China
Yinghuan Shi
Department of Computer Science and Technology, Nanjing University, Nanjing 210046, China
Boris C. Bernhardt
Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
Yichu He
Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
Feng Shi
Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; Corresponding author.
Guangming Lu
Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, #305 East Zhongshan Rd, Nanjing, Jiangsu 210002, China; State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing 210093, China; Corresponding authors at: Department of Medical Imaging, Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, #305 East Zhongshan Rd, Nanjing, Jiangsu 210002, China.
Zhiqiang Zhang
Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, #305 East Zhongshan Rd, Nanjing, Jiangsu 210002, China; State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing 210093, China; Corresponding authors at: Department of Medical Imaging, Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, #305 East Zhongshan Rd, Nanjing, Jiangsu 210002, China.
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.
