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Abstract #5110

A Self Automated Normalization Algorithm of CBV Maps for Glioma Grading

Ravi Teja Seethamraju1, Hui You2,3, Jinrong Qu4,5, Eric A. Macklin6, Geoffrey S. Young

1MR R and D, Siemens Medical Solutions, USA Inc., Charlestown, MA, United States; 2Radiology, Peking Union Medical College Hospital, Beijing, China; 3Neuro Radiology, Brigham and Women's Hospital, Boston, MA, United States; 4Radiology, Tiantan Hospital, Beijing, China; 5Radiology, Henan Tumor Hospital, Zhengzhou, China; 6Biostatistics Center, Massachusetts General Hospital, Boston, MA, United States

The hot spot method is the most widely used technique for analysis of DSC PWI maps. Here, ROIs are selected on the relCBV maps in the portion of tumor that appears to have the highest relCBV. This value is divided by the relCBV of ROI selected in the contralateral normal appearing white matter (NAWM), to yield the normalized CBV (nCBV). The measured nCBV is highly operator dependent, so we present a method for automating the determination of NAWM relCBV in order to reduce the operator dependence of the hot spot and other analytic methods.