Document Type

Conference Paper

Publication Date

2011

DOI

10.1117/12.878208

Publication Title

Medical Imaging 2011: Computer-Aided Diagnosis, Proceedings of SPIE Vol. 7963

Volume

7963

Pages

79632U (1-7)

Conference Name

Medical Imaging 2011: Computer-Aided Diagnosis, February 12-17, 2011, Lake Buena Vista, Florida

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.

Rights

Copyright 2011 Society of Photo‑Optical Instrumentation Engineers (SPIE).

One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.

Original Publication Citation

Banerjee, D., Tran, L., Li, J., Shen, Y., McKenzie, F., & Wang, J. (2011).Prediction of brain tumor progression using multiple histogram matched MRI scans. In R.M. Summers & B.V. Ginneken (Eds.), Medical Imaging 2011: Computer-Aided Diagnosis, Proceedings of SPIE Vol. 7963 (79632U). SPIE of Bellingham, WA. https://doi.org/10.1117/12.878208

ORCID

0000-0003-0091-6986 (Li)

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