Mapping Tractography Across Subjects
This work addresses the challenge of cross-subject tractography alignment for neuroscience researchers, offering a novel approach that could improve anatomical analysis, though it appears incremental as it builds on graph matching concepts.
The authors tackled the problem of aligning tractography data across subjects by proposing a graph-based mapping method that directly matches streamlines without spatial transformation, showing preliminary promising results in segmenting the corticospinal tract compared to standard registration techniques.
Diffusion magnetic resonance imaging (dMRI) and tractography provide means to study the anatomical structures within the white matter of the brain. When studying tractography data across subjects, it is usually necessary to align, i.e. to register, tractographies together. This registration step is most often performed by applying the transformation resulting from the registration of other volumetric images (T1, FA). In contrast with registration methods that "transform" tractographies, in this work, we try to find which streamline in one tractography correspond to which streamline in the other tractography, without any transformation. In other words, we try to find a "mapping" between the tractographies. We propose a graph-based solution for the tractography mapping problem and we explain similarities and differences with the related well-known graph matching problem. Specifically, we define a loss function based on the pairwise streamline distance and reformulate the mapping problem as combinatorial optimization of that loss function. We show preliminary promising results where we compare the proposed method, implemented with simulated annealing, against a standard registration techniques in a task of segmentation of the corticospinal tract.