Image coregistration: Quantitative processing framework for the assessment of brain lesions

Hannu Huhdanpaa, Darryl H. Hwang, Gregory G. Gasparian, Michael T. Booker, Yong Cen, Alexander Lerner, Orest B. Boyko, John L. Go, Paul E. Kim, Anandh Rajamohan, Meng Law, Mark S. Shiroishi

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)369-379
Number of pages11
JournalJournal of Digital Imaging
Volume27
Issue number3
DOIs
StatePublished - Jun 2014

Keywords

  • Algorithms
  • Biomedical Image Analysis
  • Brain Morphology
  • Brain imaging
  • Computer-Aided Diagnoses (CAD)
  • Digital Image Processing
  • Digital Imaging and Communications in Medicine (DICOM)
  • Image analysis
  • MR imaging
  • Segmentation
  • Software design
  • Systems integration
  • User interface

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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  • Cite this

    Huhdanpaa, H., Hwang, D. H., Gasparian, G. G., Booker, M. T., Cen, Y., Lerner, A., Boyko, O. B., Go, J. L., Kim, P. E., Rajamohan, A., Law, M., & Shiroishi, M. S. (2014). Image coregistration: Quantitative processing framework for the assessment of brain lesions. Journal of Digital Imaging, 27(3), 369-379. https://doi.org/10.1007/s10278-013-9655-y