Spatial normalization of diffusion tensor MRI using multiple channels

한글 요약

확산텐서영상(DTI)을 여러 사람의 뇌에서 비교하려면 개인 간 해부학적 차이를 줄이는 공간 정규화가 필요한데, 이때 어떤 영상 정보를 정합에 사용해야 가장 정확한지를 검증한 연구입니다. 데몬(demons) 알고리즘 기반의 다채널 비선형 정합에 T2 강조 영상, 분할 비등방도, 텐서 고유값, 텐서 성분 6개 등 다양한 채널 조합을 입력해 16명의 DTI 자료를 정규화하고, 신경섬유 다발의 끝점 거리와 평균 제곱 오차, 복셀 단위 텐서 정렬 정도로 결과를 평가했습니다. 모든 평가에서 텐서의 6개 독립 성분을 함께 사용한 비선형 정합이 가장 우수한 성능을 보였습니다. 이 결과는 DTI 기반 복셀 통계 분석에서 텐서 정보를 온전히 활용한 정규화가 필요함을 보여 주며, 이후 백질 연결망 연구의 전처리 기준을 제시한 연구입니다.

Diffusion Tensor MRI (DT-MRI) can provide important in vivo information for the detection of brain abnormalities in diseases characterized by compromised neural connectivity. To quantify diffusion tensor abnormalities based on voxel-based statistical analysis, spatial normalization is required to minimize the anatomical variability between studied brain structures. In this article, we used a multiple input channel registration algorithm based on a demons algorithm and evaluated the spatial normalization of diffusion tensor image in terms of the input information used for registration. Registration was performed on 16 DT-MRI data sets using different combinations of the channels, including a channel of T2-weighted intensity, a channel of the fractional anisotropy, a channel of the difference of the first and second eigenvalues, two channels of the fractional anisotropy and the trace of tensor, three channels of the eigenvalues of the tensor, and the six channel tensor components. To evaluate the registration of tensor data, we defined two similarity measures, i.e., the endpoint divergence and the mean square error, which we applied to the fiber bundles of target images and registered images at the same seed points in white matter segmentation. We also evaluated the tensor registration by examining the voxel-by-voxel alignment of tensors in a sample of 15 normalized DT-MRIs. In all evaluations, nonlinear warping using six independent tensor components as input channels showed the best performance in effectively normalizing the tract morphology and tensor orientation. We also present a nonlinear method for creating a group diffusion tensor atlas using the average tensor field and the average deformation field, which we believe is a better approach than a strict linear one for representing both tensor distribution and morphological distribution of the population.