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Quantification and Analysis of Large Multimodal Clinical Image Studies: Application to Stroke

1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
2Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
3Department of Neurology, Rhode Island Hospital, Alpert Medical School of Brown University, RI, USA.
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2013
Publication Date:
Multimodal Brain Image Anal.
Volume Number:
Issue Number:
Multimodal Brain Image Anal. 2013 Sep; 8159: 18-30.
PubMed ID:
Appears in Collections:
Barbara J. Weedon Fellowship
P41 RR013218/RR/NCRR NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
K23 NS064052/NS/NINDS NIH HHS/United States
U01 NS069208/NS/NINDS NIH HHS/United States
American Stroke Association-Bugher Foundation Centers for Stroke Prevention Research
Generated Citation:
Sridharan R., Dalca A.V., Fitzpatrick K.M., Cloonan L., Kanakis A., Wu O., Furie K.L., Rosand J., Rost N.S., Golland P. Quantification and Analysis of Large Multimodal Clinical Image Studies: Application to Stroke. Multimodal Brain Image Anal. 2013 Sep; 8159: 18-30. PMID: 25632408. PMCID: PMC4306599.
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We present an analysis framework for large studies of multimodal clinical quality brain image collections. Processing and analysis of such datasets is challenging due to low resolution, poor contrast, misaligned images, and restricted field of view. We adapt existing registration and segmentation methods and build a computational pipeline for spatial normalization and feature extraction. The resulting aligned dataset enables clinically meaningful analysis of spatial distributions of relevant anatomical features and of their evolution with age and disease progression. We demonstrate the approach on a neuroimaging study of stroke with more than 800 patients. We show that by combining data from several modalities, we can automatically segment important biomarkers such as white matter hyperintensity and characterize pathology evolution in this heterogeneous cohort. Specifically, we examine two sub-populations with different dynamics of white matter hyperintensity changes as a function of patients' age. Pipeline and analysis code is available at

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