Mapping Tractography Across Subjects
- Cite this paper as:
- Nguyen T.B., Olivetti E., Avesani P. (2016) Mapping Tractography Across Subjects. In: Rish I., Langs G., Wehbe L., Cecchi G., Chang K., Murphy B. (eds) Machine Learning and Interpretation in Neuroimaging. MLINI 2014, MLINI 2013. Lecture Notes in Computer Science, vol 9444. Springer, Cham
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.