Image Coregistration: Quantitative Processing Framework for the Assessment of Brain Lesions

• Published in: Journal of digital imaging 2014-06, Vol.27 (3), p.369-379
• Main Authors: Huhdanpaa, Hannu, Hwang, Darryl H., Gasparian, Gregory G., Booker, Michael T., Cen, Yong, Lerner, Alexander, Boyko, Orest B., Go, John L., Kim, Paul E., Rajamohan, Anandh, Law, Meng, Shiroishi, Mark S.
• Format: Article
• Language: English
• Subjects: Brain Diseases - diagnosis; Brain Mapping - methods; Databases, Factual; Humans; Image Interpretation, Computer-Assisted - methods; Imaging; Magnetic Resonance Imaging - methods; Medicine; Medicine & Public Health; Radiology; Sensitivity and Specificity; Software
• ISSN: 0897-1889; 1618-727X
• DOI: 10.1007/s10278-013-9655-y

The quantitative, multiparametric assessment of brain lesions requires coregistering different parameters derived from MRI sequences. This will be followed by analysis of the voxel values of the ROI within the sequences and calculated parametric maps, and deriving multiparametric models to classify imaging data. There is a need for an intuitive, automated quantitative processing framework that is generalized and adaptable to different clinical and research questions. As such flexible frameworks have not been previously described, we proceeded to construct a quantitative post-processing framework with commonly available software components. Matlab was chosen as the programming/integration environment, and SPM was chosen as the coregistration component. Matlab routines were created to extract and concatenate the coregistration transforms, take the coregistered MRI sequences as inputs to the process, allow specification of the ROI, and store the voxel values to the database for statistical analysis. The functionality of the framework was validated using brain tumor MRI cases. The implementation of this quantitative post-processing framework enables intuitive creation of multiple parameters for each voxel, facilitating near real-time in-depth voxel-wise analysis. Our initial empirical evaluation of the framework is an increased usage of analysis requiring post-processing and increased number of simultaneous research activities by clinicians and researchers with non-technical backgrounds. We show that common software components can be utilized to implement an intuitive real-time quantitative post-processing framework, resulting in improved scalability and increased adoption of post-processing needed to answer important diagnostic questions.