TY - GEN
T1 - Prediction of brain tumor progression using multiple histogram matched MRI scans
AU - Banerjee, Debrup
AU - Tran, Loc
AU - Li, Jiang
AU - Shen, Yuzhong
AU - McKenzie, Frederic
AU - Wang, Jihong
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
KW - DTI
KW - Histogram Matching
KW - Predicting Brain Tumor Progression
UR - http://www.scopus.com/inward/record.url?scp=79955778601&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79955778601&partnerID=8YFLogxK
U2 - 10.1117/12.878208
DO - 10.1117/12.878208
M3 - Conference contribution
AN - SCOPUS:79955778601
SN - 9780819485052
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2011
T2 - Medical Imaging 2011: Computer-Aided Diagnosis
Y2 - 15 February 2011 through 17 February 2011
ER -