Random forest flair reconstruction from T1, T2, and PD-weighted MRI

Amod Jog, Aaron Carass, Dzung L. Pham, Jerry Ladd Prince

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

Abstract

Fluid Attenuated Inversion Recovery (FLAIR) is a commonly acquired pulse sequence for multiple sclerosis (MS) patients. MS white matter lesions appear hyperintense in FLAIR images and have excellent contrast with the surrounding tissue. Hence, FLAIR images are commonly used in automated lesion segmentation algorithms to easily and quickly delineate the lesions. This expedites the lesion load computation and correlation with disease progression. Unfortunately for numerous reasons the acquired FLAIR images can be of a poor quality and suffer from various artifacts. In the most extreme cases the data is absent, which poses a problem when consistently processing a large data set. We propose to fill in this gap by reconstructing a FLAIR image given the corresponding T1-weighted, T2-weighted, and PD-weighted images of the same subject using random forest regression. We show that the images we produce are similar to true high quality FLAIR images and also provide a good surrogate for tissue segmentation.

Original languageEnglish (US)
Title of host publication2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1079-1082
Number of pages4
ISBN (Print)9781467319591
Publication statusPublished - Jul 29 2014
Event2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 - Beijing, China
Duration: Apr 29 2014May 2 2014

Other

Other2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
CountryChina
CityBeijing
Period4/29/145/2/14

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Keywords

  • Brain
  • Image reconstruction
  • Regression

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Cite this

Jog, A., Carass, A., Pham, D. L., & Prince, J. L. (2014). Random forest flair reconstruction from T1, T2, and PD-weighted MRI. In 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 (pp. 1079-1082). [6868061] Institute of Electrical and Electronics Engineers Inc..