Characterization of intra-axial neoplasms by histogram analysis of total tumor volume from MR-derived cerebral blood volume maps
Emblem K, Due-Tonnessen P, Jacobsen E, Nedregaard B, Hald J, Bjornerud A
Rikshospitalet University Hospital
One limitation with tumor grading based on relative cerebral blood volume (rCBV) is the user dependent selection of tumor hot-spots which is critical for correct tumor grading. We propose an alternative method based on histogram analysis of the entire tumor volume, using the histogram shape and frequency distribution to differentiate high-grade from low-grade tumors in rCBV maps derived from first-pass GRE-EPI perfusion MR. The initial results of this ongoing study suggest that this may be an improved alternative to grading brain tumors from rCBV maps with less user bias.