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Proceedings Paper

Prediction of brain tumor progression using multiple histogram matched MRI scans
Author(s): Debrup Banerjee; Loc Tran; Jiang Li; Yuzhong Shen; Frederic McKenzie; Jihong Wang
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Paper Abstract

In a recent study [1], we investigated the feasibility of predicting brain tumor progression based on multiple MRI series and we tested our methods on seven patients' MRI images scanned at three consecutive visits A, B and C. Experimental results showed that it is feasible to predict tumor progression from visit A to visit C using a model trained by the information from visit A to visit B. However, the trained model failed when we tried to predict tumor progression from visit B to visit C, though it is clinically more important. Upon a closer look at the MRI scans revealed that histograms of MRI scans such as T1, T2, FLAIR etc taken at different times have slight shifts or different shapes. This is because those MRI scans are qualitative instead of quantitative so MRI scans taken at different times or by different scanners might have slightly different scales or have different homogeneities in the scanning region. In this paper, we proposed a method to overcome this difficulty. The overall goal of this study is to assess brain tumor progression by exploring seven patients' complete MRI records scanned during their visits in the past two years. There are ten MRI series in each visit, including FLAIR, T1-weighted, post-contrast T1-weighted, T2-weighted and five DTI derived MRI volumes: ADC, FA, Max, Min and Middle Eigen Values. After registering all series to the corresponding DTI scan at the first visit, we applied a histogram matching algorithm to non-DTI MRI scans to match their histograms to those of the corresponding MRI scans at the first visit. DTI derived series are quantitative and do not require the histogram matching procedure. A machine learning algorithm was then trained using the data containing information from visit A to visit B, and the trained model was used to predict tumor progression from visit B to visit C. An average of 72% pixel-wise accuracy was achieved for tumor progression prediction from visit B to visit C.

Paper Details

Date Published: 9 March 2011
PDF: 7 pages
Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79632U (9 March 2011); doi: 10.1117/12.878208
Show Author Affiliations
Debrup Banerjee, Old Dominion Univ. (United States)
Loc Tran, Old Dominion Univ. (United States)
Jiang Li, Old Dominion Univ. (United States)
Yuzhong Shen, Old Dominion Univ. (United States)
Frederic McKenzie, Old Dominion Univ. (United States)
Jihong Wang, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)


Published in SPIE Proceedings Vol. 7963:
Medical Imaging 2011: Computer-Aided Diagnosis
Ronald M. Summers; Bram van Ginneken, Editor(s)

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