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

• Published in: Multimodal Brain Image Analysis Vol. 8159; pp. 18 - 30
• Main Authors: Sridharan, Ramesh, Dalca, Adrian V., Fitzpatrick, Kaitlin M., Cloonan, Lisa, Kanakis, Allison, Wu, Ona, Furie, Karen L., Rosand, Jonathan, Rost, Natalia S., Golland, Polina
• Format: Book Chapter Journal Article
• Language: English
• Published: Cham Springer International Publishing 2013
• Series: Lecture Notes in Computer Science
• Subjects: Manual Segmentation; Nonrigid Registration; Stroke Lesion; White Matter Hyperintensity; White Matter Hyperintensity Volume
• ISBN: 9783319021256; 3319021257
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-02126-3_3

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 http://groups.csail.mit.edu/vision/medical-vision/stroke/.