Beyond cost function masking: RPCA based non linear registration in the context of VLSM
Voxel based lesion symptom mapping (VLSM) allows studying the relationship between stroke location and clinical outcome. The core idea of VLSM is to map all patient cases into a common atlas space and then apply statistical tests on a voxel level comparing outcome measures of patients with a lesion in the voxel to those without lesion. A major limitation of VLSM is that it requires a previous lesion segmentation, which is mostly performed manually, for masked subject to atlas registration as well as for the VLSM analysis. The aim of this work is to evaluate the feasibility of a recently introduced robust PCA (RPCA) based iterative non linear registration framework that potentially overcomes this limitation by generating the lesion segmentation on the fly. In addition, we propose and evaluate a rapid variant of this framework (successively tightened low rank condition RPCA, stl RPCA) Based on 29 follow up FLAIR datasets of patients with ischemic stroke, the lesion segmentation capabilities and subject to atlas transformation properties of the RPCA methods are evaluated and compared to non linear registration with and without cost function masking. Results reveal that the proposed method is capable of segmenting the lesions with an average Dice coefficient of 63% Similar to nonlinear registration with cost function masking, the RPCA based registration frameworks significantly decrease confounding effects of pathologies on subject to atlas transformation properties. Overall, the RPCA frameworks lead to promising results and could considerably enhance VLSM analyses.
2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI)