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• W4317474375 endingPage "102744" @default.
• W4317474375 startingPage "102744" @default.
• W4317474375 abstract "Diffusion MRI is a useful neuroimaging tool for non-invasive mapping of human brain microstructure and structural connections. The analysis of diffusion MRI data often requires brain segmentation, including volumetric segmentation and cerebral cortical surfaces, from additional high-resolution T1-weighted (T1w) anatomical MRI data, which may be unacquired or corrupted by subject motion or hardware failure or cannot be accurately co-registered to the diffusion data that are not corrected for susceptibility-induced geometric distortion. To address these challenges, this study proposes to synthesize high-quality T1w anatomical images directly from diffusion data using convolutional neural networks (CNNs) (entitled “DeepAnat”), including a U-Net and a hybrid generative adversarial network (GAN), and perform brain segmentation on synthesized T1w images or co-register synthesized and native T1w images. The quantitative and systematic evaluation using data of 60 young subjects provided by the Human Connectome Project (HCP) shows that the synthesized T1w images and results for brain segmentation and comprehensive diffusion analysis tasks are highly similar to those from native T1w images. The brain segmentation accuracy is slightly higher for the U-Net than the GAN. The efficacy of DeepAnat is further validated on a larger dataset of 300 more elderly subjects provided by the UK Biobank. Moreover, the U-Nets trained and validated on the HCP and UK Biobank data are shown to be highly generalizable to the diffusion data from Massachusetts General Hospital Connectome Diffusion Microstructure Dataset (MGH CDMD) acquired with different hardware systems and imaging protocols and therefore can be used directly without retraining or with fine-tuning for further improved performance. Finally, it is quantitatively demonstrated that the alignment between native T1w images and diffusion images uncorrected for geometric distortion assisted by synthesized T1w images substantially improves upon that by co-registering the diffusion and T1w images directly using the data of 20 subjects from MGH CDMD. In summary, our study demonstrates the benefits and practical feasibility of DeepAnat for assisting various diffusion MRI data analyses and supports its use in neuroscientific applications." @default.
• W4317474375 created "2023-01-20" @default.
• W4317474375 creator A5011455554 @default.
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• W4317474375 date "2023-05-01" @default.
• W4317474375 modified "2023-10-18" @default.
• W4317474375 title "Diffusion MRI data analysis assisted by deep learning synthesized anatomical images (DeepAnat)" @default.
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