Meier Dominik S, Guttmann Charles R G
Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 221 Longwood Avenue, RFB 396,Boston, MA, 02115, USA.
Neuroimage. 2003 Oct;20(2):1193-209. doi: 10.1016/S1053-8119(03)00354-9.
In progressive neurological disorders, such as multiple sclerosis (MS), magnetic resonance imaging (MRI) follow-up is used to monitor disease activity and progression and to understand the underlying pathogenic mechanisms. This article presents image postprocessing methods and validation for integrating multiple serial MRI scans into a spatiotemporal volume for direct quantitative evaluation of the temporal intensity profiles. This temporal intensity signal and its dynamics have thus far not been exploited in the study of MS pathogenesis and the search for MRI surrogates of disease activity and progression. The integration into a four-dimensional data set comprises stages of tissue classification, followed by spatial and intensity normalization and partial volume filtering. Spatial normalization corrects for variations in head positioning and distortion artifacts via fully automated intensity-based registration algorithms, both rigid and nonrigid. Intensity normalization includes separate stages of correcting intra- and interscan variations based on the prior tissue class segmentation. Different approaches to image registration, partial volume correction, and intensity normalization were validated and compared. Validation included a scan-rescan experiment as well as a natural-history study on MS patients, imaged in weekly to monthly intervals over a 1-year follow-up. Significant error reduction was observed by applying tissue-specific intensity normalization and partial volume filtering. Example temporal profiles within evolving multiple sclerosis lesions are presented. An overall residual signal variance of 1.4% +/- 0.5% was observed across multiple subjects and time points, indicating an overall sensitivity of 3% (for axial dual echo images with 3-mm slice thickness) for longitudinal study of signal dynamics from serial brain MRI.
在诸如多发性硬化症（MS）等进行性神经疾病中，磁共振成像（MRI）随访用于监测疾病活动和进展，并了解潜在的致病机制。本文介绍了图像后处理方法以及将多个连续MRI扫描整合到时空体积中以直接定量评估时间强度分布的验证方法。迄今为止，这种时间强度信号及其动态变化尚未在MS发病机制研究以及疾病活动和进展的MRI替代指标的探索中得到利用。整合到四维数据集中包括组织分类阶段，随后是空间和强度归一化以及部分容积滤波。空间归一化通过基于强度的全自动配准算法（包括刚性和非刚性算法）校正头部定位和畸变伪影的变化。强度归一化包括基于先前组织类别分割校正扫描内和扫描间变化的单独阶段。对不同的图像配准、部分容积校正和强度归一化方法进行了验证和比较。验证包括重扫实验以及对MS患者的自然史研究，在1年的随访中以每周至每月的间隔进行成像。通过应用组织特异性强度归一化和部分容积滤波，观察到显著的误差降低。展示了多发性硬化症病变发展过程中的示例时间分布。在多个受试者和时间点观察到的总体残余信号方差为1.4%±0.5%，表明对于来自连续脑MRI的信号动态纵向研究，总体灵敏度为3%（对于3毫米层厚的轴向双回波图像）。
