HYBRID Diffusion Imaging to Detect Acute White Matter Injury After Mild TBI

描述（由申请人提供）：本提案的总体目标是推进扩散加权成像技术（混合扩散成像或HYDI）的发展，以增强轻度创伤性脑损伤（MTBI）的诊断，并阐明白质（WM）病理在这种疾病中的作用。由于目前没有明确的生物标志物，MTBI的诊断是基于临床的。弥散张量成像（DTI）被认为是一种潜在的神经成像生物标志物，但迄今为止，MTBI的结果非常复杂。我们认为，在脑白质损伤可能相当细微的MTBI中，更普遍和完整的弥散加权成像技术可能提供更准确和一致的结果。其中一种方法是q空间扩散成像，它估计水扩散的概率密度函数（PDF），而不需要任何模型假设。然而，在对整个q空间（即扩散空间）进行采样时，由于扫描时间过长，妨碍了q空间成像的临床应用。我们开发了一种称为混合扩散成像（HYDI）的q空间编码方案，以促进q空间成像的临床应用。它可以在q空间中获得同心球壳的测量结果，并可以从单个成像序列中进行多个扩散数据分析，包括DTI（内壳）、PDF（完整数据集）和q球成像（外壳），用于WM纤维束束成像。我们建议利用HYDI技术推进PDF和正交DTI测量的发展，并确认其对MTBI一周内白质损伤的敏感性和特异性。首先，将进行蒙特卡罗模拟研究，以优化HYDI用于MTBI的MR图像参数和扩散参数。随后，在一级创伤中心观察到的40名MTBI患者和40名其他损伤控制（非tbi）患者将在受伤后一周内使用HYDI进行研究。参与者将接受认知功能和脑震荡后症状的标准化评估。协方差分析（ANCOVA）将用于比较全脑HYDI WM参数和手绘感兴趣区域（roi）。此外，我们将进行概率束造影来评估易感脑损伤束的这些参数，包括胼胝体、锥体束（皮质脊髓束）、额叶皮质下WM和上纵筋束。采用多元方差分析（MANOVA）和协方差分析（MANCOVA）的一般线性模型方法来评估观察到的白质差异与认知/神经行为功能之间的相关性，以确定观察到的差异的功能意义。该项目提出了一种新的扩散成像方法，如果成功，它将大大提高MTBI的诊断能力（即补充临床病史），监测损伤恢复情况，并作为对治疗干预反应的生物标志物。

DESCRIPTION (provided by applicant): The overarching goal of this proposal is to advance the development of a diffusion weighted imaging technique (Hybrid Diffusion Imaging or HYDI) to enhance the diagnosis of mild traumatic brain injury (MTBI) and clarify the role of white matter (WM) pathology in this disorder. The diagnosis of MTBI is made on clinical grounds as there are no definitive biomarkers currently available. Diffusion tensor imaging (DTI) has been proposed as a potential neuroimaging biomarker but to date results in MTBI have been quite mixed. We believe that a more general and complete diffusion weighted imaging technique may provide more accurate and consistent results in MTBI where white matter injury may be quite subtle. One such approach is q-space diffusion imaging which estimates the probability density function (PDF) of water diffusion without any model assumptions. However, the clinical application of q-space imaging is often impeded by long scan-time when sampling the entire q- space (i.e. diffusion space). We have developed a q-space encoding scheme known as Hybrid Diffusion Imaging (HYDI) to facilitate the clinical application of q-space imaging. It obtains measurements on concentric spherical shells in q-space and enables multiple diffusion data analyses from a single imaging sequence including DTI (inner shells), PDF (whole datasets) and q-ball imaging (outer shell) for WM fiber tractography. We propose to advance the development of PDF and orthogonal DTI measures using the HYDI technique and to confirm their sensitivity and specificity to white matter injury within one week of MTBI. Initially, a Monte Carlo simulation study will be performed to optimize the MR image parameters and diffusion parameters of HYDI for MTBI. Subsequently, 40 individuals with MTBI and 40 Other Injury Controls (non-TBI) seen in a Level 1 Trauma Center will be studied with HYDI within one week of injury. Participants will undergo standardized assessment of cognitive function and post concussive symptoms. Analysis of covariance (ANCOVA) will be used to compare whole brain HYDI WM parameters and hand drawn regions of interest (ROIs). In addition we will perform probability tractography to assess these parameters in tracts vulnerable to TBI including corpus callosum, pyramidal (corticospinal) tract, frontal subcortical WM, and the superior longitudinal fasiculus. A general linear model approach including multivariate analysis of variance (MANOVA) and covariance (MANCOVA) will be used to assess the correlation between observed white matter differences and cognitive/neurobehavioral function to determine the functional significance of observed differences. This project proposes a novel approach to diffusion imaging that if successful it would greatly enhance the capacity to diagnose MTBI (i.e. supplement clinical history), monitor recovery from the injury, and serve as a biomarker for response to treatment interventions. PUBLIC HEALTH RELEVANCE: The goal of this project is to improve our diagnosis and understanding of the underlying neural mechanisms of mild traumatic brain injury (MTBI). Specifically this project proposes to refine a new neuroimaging technique that is highly sensitive to the white matter injury thought to be responsible for the symptoms of MTBI, and to test its sensitivity in a group of individuals within one week of injury. If successful this technique would greatly enhance the capacity to diagnose MTBI, monitor recovery from the injury, and serve as imaging biomarkers for response to treatment interventions.