Field-of-view extension for brain diffusion MRI via deep generative models.

IF 1.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Journal of Medical Imaging Pub Date : 2024-07-01 Epub Date: 2024-08-24 DOI:10.1117/1.JMI.11.4.044008
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
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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 PSNR b 0 = 22.397 , SSIM b 0 = 0.905 , PSNR b 1300 = 22.479 , and SSIM b 1300 = 0.893 ; on the National Alzheimer's Coordinating Center (NACC) dataset, it achieved PSNR b 0 = 21.304 , SSIM b 0 = 0.892 , PSNR b 1300 = 21.599 , and SSIM b 1300 = 0.877 . The proposed framework improved the tractography accuracy, as demonstrated by an increased average Dice score for 72 tracts ( p < 0.001 ) 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.

通过深度生成模型扩展脑弥散核磁共振成像的视场。
目的:在脑弥散磁共振成像(dMRI)中,不完整的视场(FOV)会严重影响对全脑组织微观结构和连接性的容积和束状分析。我们的目标是开发一种方法,直接从现有的不完整视场的 dMRI 扫描中估算缺失的切片。我们假设,具有完整视场的估算图像可以改善具有不完整视场的损坏数据的全脑束学。因此,我们的方法提供了一种可取的替代方法,而不是丢弃有价值的脑部 dMRI 数据,使后续的牵引成像分析成为可能,否则这些分析将具有挑战性或无法通过损坏的数据实现:方法:我们提出了一个基于深度生成模型的框架,该模型可估算出不完整 FOV 的 dMRI 扫描中缺失的大脑区域。该模型能够学习扩散加权图像(DWIs)中的扩散特征和相应结构图像中明显的解剖学特征,从而有效地估算FOV不完整部分DWIs中缺失的切片:在威斯康星州阿尔茨海默氏症预防注册数据集(WRAP)上评估估算切片时,所提出的框架达到了 PSNR b 0 = 22.397 , SSIM b 0 = 0.905 , PSNR b 1300 = 22.479 ,SSIM b 1300 = 0.893 ;在国家阿尔茨海默氏症协调中心(NACC)数据集上,实现了 PSNR b 0 = 21.304 ,SSIM b 0 = 0.892 ,PSNR b 1300 = 21.599 ,SSIM b 1300 = 0.877 。在 WRAP 和 NACC 数据集上,拟议框架提高了 72 个神经束的平均 Dice 分数(P 0.001),从而提高了神经束绘制的准确性:结果表明,所提出的框架在具有不完整 FOV 的脑 dMRI 数据中实现了足够的估算性能,可用于改善全脑牵引成像,从而修复损坏的数据。在分析与阿尔茨海默病相关的脑束时,我们的方法在扩展的完整 FOV 下获得了更准确的全脑束图结果,并降低了不确定性。
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来源期刊
Journal of Medical Imaging
Journal of Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.10
自引率
4.20%
发文量
0
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.
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