The detection of brain alterations is crucial for understanding pathophysiological processes. The Voxel-Based Morphometry (VBM) is one of the most popular methods to achieve this task. Despite its numerous advantages, VBM is based on a highly reduced representation of the local brain anatomy since complex anatomical patterns are reduced to local averages of tissue probabilities. In this paper, we propose a new framework called Sparse-Based Morphometry (SBM) to better represent local brain anatomies. The presented patch-based approach uses dictionary learning to detect anatomical pattern modifications based on their shape and geometry. In our experiences, we compare SBM and VBM along Alzheimer’s Disease (AD) progression.
This is a preview of subscription content, log in to check access.
This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Program IdEx Bordeaux (HL-MRI ANR-10-IDEX-03-02), Clusters of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57) and the CNRS multidisciplinary project “Défi imag’In”.
Shen, S., et al.: VBM lesion detection depends on the normalization template: a study using simulated atrophy. Magn. Resonan. Imaging 25(10), 1385–1396 (2007)CrossRefGoogle Scholar
Coupé, P., et al.: Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. NeuroImage 54(2), 940–954 (2011)CrossRefGoogle Scholar
Tong, T., et al.: Segmentation of MR images via discriminative dictionary learning and sparse coding: application to hippocampus labeling. NeuroImage 76, 11–23 (2013)CrossRefGoogle Scholar
Coupé, P., et al.: Simultaneous segmentation and grading of anatomical structures for patient’s classification: application to Alzheimer’s disease. NeuroImage 59(4), 3736–3747 (2012)CrossRefGoogle Scholar
Liu, M., et al.: Ensemble sparse classification of Alzheimer’s disease. NeuroImage 60(2), 1106–1116 (2012)CrossRefGoogle Scholar
Wyman, B., et al.: Standardization of analysis sets for reporting results from ADNI MRI data. Alzheimer’s Dement. 9(3), 332–337 (2013)CrossRefGoogle Scholar
Vovk, V.: Combining p-values via averaging (2012). arXiv preprint arXiv:1212.4966
Mairal, J., et al.: Online dictionary learning for sparse coding. In: 26th AICML, pp. 689–696. ACM (2009)Google Scholar
Karas, G., et al.: Precuneus atrophy in early-onset Alzheimer’s disease: a morphometric structural MRI study. Neuroradiology 49(12), 967–976 (2007)CrossRefGoogle Scholar
Ikonomovic, M., et al.: Precuneus amyloid burden is associated with reduced cholinergic activity in Alzheimer disease. Neurology 77(1), 39–47 (2011)CrossRefGoogle Scholar
He, Y., et al.: Regional coherence changes in the early stages of Alzheimer’s disease: a combined structural and resting-state functional MRI study. Neuroimage 35(2), 488–500 (2007)CrossRefGoogle Scholar
Devanand, D., et al.: Hippocampal and entorhinal atrophy in mild cognitive impairment prediction of Alzheimer disease. Neurology 68(11), 828–836 (2007)CrossRefGoogle Scholar