Information criteria for dynamic contrast-enhanced magnetic resonance imaging

Russell T. Shinohara, Ciprian M Crainiceanu, Brian S Caffo, Daniel S. Reich

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Inflammatory lesions form in the brain and spinal cord of patients with multiple sclerosis (MS). In many active MS lesions, blood flows abnormally into the white matter of the brain due to breakdown of the blood-brain barrier (BBB), which is know to be associated with morbidity and disability. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows quantitative study of blood flow and permeability dynamics throughout the brain. In our study, we observe 15 patients who undergo DCE-MRI periodically throughout a year. In this paper, we design and study spatiotemporal parameters of interest that cannot be obtained by visual inspection. Examples of such parameters are the rate and maximum intensity observed in regions of interest. We develop semi parametric methods for this quantification of BBB disruption at each visit.

Original languageEnglish (US)
Title of host publicationProceedings - 2013 3rd International Workshop on Pattern Recognition in Neuroimaging, PRNI 2013
Pages37-41
Number of pages5
DOIs
StatePublished - 2013
Event2013 3rd International Workshop on Pattern Recognition in Neuroimaging, PRNI 2013 - Philadelphia, PA, United States
Duration: Jun 22 2013Jun 24 2013

Other

Other2013 3rd International Workshop on Pattern Recognition in Neuroimaging, PRNI 2013
CountryUnited States
CityPhiladelphia, PA
Period6/22/136/24/13

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Keywords

  • dynamic contrast-enhanced MRI
  • functional principal components analysis
  • information criteria

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Biomedical Engineering

Cite this

Shinohara, R. T., Crainiceanu, C. M., Caffo, B. S., & Reich, D. S. (2013). Information criteria for dynamic contrast-enhanced magnetic resonance imaging. In Proceedings - 2013 3rd International Workshop on Pattern Recognition in Neuroimaging, PRNI 2013 (pp. 37-41). [6603551] https://doi.org/10.1109/PRNI.2013.19