Chenyu Gao, Shunxing Bao, Michael E Kim, Nancy R Newlin, Praitayini Kanakaraj, Tianyuan Yao, Gaurav Rudravaram, Yuankai Huo, Daniel Moyer, Kurt Schilling, Walter A Kukull, Arthur W Toga, Derek B Archer, Timothy J Hohman, Bennett A Landman, Zhiyuan Li
{"title":"Field-of-view extension for brain diffusion MRI via deep generative models.","authors":"Chenyu Gao, Shunxing Bao, Michael E Kim, Nancy R Newlin, Praitayini Kanakaraj, Tianyuan Yao, Gaurav Rudravaram, Yuankai Huo, Daniel Moyer, Kurt Schilling, Walter A Kukull, Arthur W Toga, Derek B Archer, Timothy J Hohman, Bennett A Landman, Zhiyuan Li","doi":"10.1117/1.JMI.11.4.044008","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>In brain diffusion magnetic resonance imaging (dMRI), the volumetric and bundle analyses of whole-brain tissue microstructure and connectivity can be severely impeded by an incomplete field of view (FOV). We aim to develop a method for imputing the missing slices directly from existing dMRI scans with an incomplete FOV. We hypothesize that the imputed image with a complete FOV can improve whole-brain tractography for corrupted data with an incomplete FOV. Therefore, our approach provides a desirable alternative to discarding the valuable brain dMRI data, enabling subsequent tractography analyses that would otherwise be challenging or unattainable with corrupted data.</p><p><strong>Approach: </strong>We propose a framework based on a deep generative model that estimates the absent brain regions in dMRI scans with an incomplete FOV. The model is capable of learning both the diffusion characteristics in diffusion-weighted images (DWIs) and the anatomical features evident in the corresponding structural images for efficiently imputing missing slices of DWIs in the incomplete part of the FOV.</p><p><strong>Results: </strong>For evaluating the imputed slices, on the Wisconsin Registry for Alzheimer's Prevention (WRAP) dataset, the proposed framework achieved <math> <mrow><msub><mi>PSNR</mi> <mrow><mi>b</mi> <mn>0</mn></mrow> </msub> <mo>=</mo> <mn>22.397</mn></mrow> </math> , <math> <mrow><msub><mi>SSIM</mi> <mrow><mi>b</mi> <mn>0</mn></mrow> </msub> <mo>=</mo> <mn>0.905</mn></mrow> </math> , <math> <mrow> <msub><mrow><mi>PSNR</mi></mrow> <mrow><mi>b</mi> <mn>1300</mn></mrow> </msub> <mo>=</mo> <mn>22.479</mn></mrow> </math> , and <math> <mrow><msub><mi>SSIM</mi> <mrow><mi>b</mi> <mn>1300</mn></mrow> </msub> <mo>=</mo> <mn>0.893</mn></mrow> </math> ; on the National Alzheimer's Coordinating Center (NACC) dataset, it achieved <math> <mrow><msub><mi>PSNR</mi> <mrow><mi>b</mi> <mn>0</mn></mrow> </msub> <mo>=</mo> <mn>21.304</mn></mrow> </math> , <math> <mrow><msub><mi>SSIM</mi> <mrow><mi>b</mi> <mn>0</mn></mrow> </msub> <mo>=</mo> <mn>0.892</mn></mrow> </math> , <math> <mrow><msub><mi>PSNR</mi> <mrow><mi>b</mi> <mn>1300</mn></mrow> </msub> <mo>=</mo> <mn>21.599</mn></mrow> </math> , and <math> <mrow><msub><mi>SSIM</mi> <mrow><mi>b</mi> <mn>1300</mn></mrow> </msub> <mo>=</mo> <mn>0.877</mn></mrow> </math> . The proposed framework improved the tractography accuracy, as demonstrated by an increased average Dice score for 72 tracts ( <math><mrow><mi>p</mi> <mo><</mo> <mn>0.001</mn></mrow> </math> ) on both the WRAP and NACC datasets.</p><p><strong>Conclusions: </strong>Results suggest that the proposed framework achieved sufficient imputation performance in brain dMRI data with an incomplete FOV for improving whole-brain tractography, thereby repairing the corrupted data. Our approach achieved more accurate whole-brain tractography results with an extended and complete FOV and reduced the uncertainty when analyzing bundles associated with Alzheimer's disease.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11344266/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Medical Imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1117/1.JMI.11.4.044008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/8/24 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose: In brain diffusion magnetic resonance imaging (dMRI), the volumetric and bundle analyses of whole-brain tissue microstructure and connectivity can be severely impeded by an incomplete field of view (FOV). We aim to develop a method for imputing the missing slices directly from existing dMRI scans with an incomplete FOV. We hypothesize that the imputed image with a complete FOV can improve whole-brain tractography for corrupted data with an incomplete FOV. Therefore, our approach provides a desirable alternative to discarding the valuable brain dMRI data, enabling subsequent tractography analyses that would otherwise be challenging or unattainable with corrupted data.
Approach: We propose a framework based on a deep generative model that estimates the absent brain regions in dMRI scans with an incomplete FOV. The model is capable of learning both the diffusion characteristics in diffusion-weighted images (DWIs) and the anatomical features evident in the corresponding structural images for efficiently imputing missing slices of DWIs in the incomplete part of the FOV.
Results: For evaluating the imputed slices, on the Wisconsin Registry for Alzheimer's Prevention (WRAP) dataset, the proposed framework achieved , , , and ; on the National Alzheimer's Coordinating Center (NACC) dataset, it achieved , , , and . The proposed framework improved the tractography accuracy, as demonstrated by an increased average Dice score for 72 tracts ( ) on both the WRAP and NACC datasets.
Conclusions: Results suggest that the proposed framework achieved sufficient imputation performance in brain dMRI data with an incomplete FOV for improving whole-brain tractography, thereby repairing the corrupted data. Our approach achieved more accurate whole-brain tractography results with an extended and complete FOV and reduced the uncertainty when analyzing bundles associated with Alzheimer's disease.
期刊介绍:
JMI covers fundamental and translational research, as well as applications, focused on medical imaging, which continue to yield physical and biomedical advancements in the early detection, diagnostics, and therapy of disease as well as in the understanding of normal. The scope of JMI includes: Imaging physics, Tomographic reconstruction algorithms (such as those in CT and MRI), Image processing and deep learning, Computer-aided diagnosis and quantitative image analysis, Visualization and modeling, Picture archiving and communications systems (PACS), Image perception and observer performance, Technology assessment, Ultrasonic imaging, Image-guided procedures, Digital pathology, Biomedical applications of biomedical imaging. JMI allows for the peer-reviewed communication and archiving of scientific developments, translational and clinical applications, reviews, and recommendations for the field.