Wang Bo, Prastawa Marcel, Irimia Andrei, Chambers Micah C, Sadeghi Neda, Vespa Paul M, van Horn John D, Gerig Guido
Scientific Computing and Imaging Institute, University of Utah ; School of Computing, University of Utah.
Laboratory of Neuro Imaging, University of California at Los Angeles.
Proc IEEE Int Symp Biomed Imaging. 2013 Dec 31;2013:1392-1395. doi: 10.1109/ISBI.2013.6556793.
Quantitative imaging biomarkers are important for assessment of impact, recovery and treatment efficacy in patients with traumatic brain injury (TBI). To our knowledge, the identification of such biomarkers characterizing disease progress and recovery has been insufficiently explored in TBI due to difficulties in registration of baseline and follow-up data and automatic segmentation of tissue and lesions from multimodal, longitudinal MR image data. We propose a new methodology for computing imaging biomarkers in TBI by extending a recently proposed spatiotemporal 4D modeling approach in order to compute quantitative features of tissue change. The proposed method computes surface-based and voxel-based measurements such as cortical thickness, volume changes, and geometric deformation. We analyze the potential for clinical use of these biomarkers by correlating them with TBI-specific patient scores at the level of the whole brain and of individual regions. Our preliminary results indicate that the proposed voxel-based biomarkers are correlated with clinical outcomes.
定量成像生物标志物对于评估创伤性脑损伤（TBI）患者的损伤程度、恢复情况及治疗效果至关重要。据我们所知，由于在多模态纵向磁共振图像数据中进行基线和随访数据配准以及组织和病变自动分割存在困难，在TBI中表征疾病进展和恢复的此类生物标志物的识别尚未得到充分探索。我们通过扩展最近提出的时空4D建模方法，提出了一种计算TBI成像生物标志物的新方法，以计算组织变化的定量特征。所提出的方法计算基于表面和基于体素的测量值，如皮质厚度、体积变化和几何变形。我们通过将这些生物标志物与全脑和各个区域水平上的TBI特异性患者评分相关联，分析了这些生物标志物临床应用的潜力。我们的初步结果表明，所提出的基于体素的生物标志物与临床结果相关。
