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Abstract #1947

An Empirical DSC-MRI Data Model Including First-Pass, Recirculation and Leakage Components Fully Characterises Signal Changes in Tumours and Normal Brain

Matthew R. Orton1, James A. d'Arcy2, Keiko Miyazaki2, Dow-Mu Koh3, David J. Collins4, Martin O. Leach2

1CR-UK and EPSRC Cancer Imaging Centre , Institute of Cancer Research, Sutton, Surrey, United Kingdom; 2CR-UK and EPSRC Cancer Imaging Centre, Institute of Cancer Research, Sutton, Surrey, United Kingdom; 3Department of Radiology, Royal Marsden Hospital, Sutton, Surrey, United Kingdom; 4Clinical MRI Unit, Royal Marsden Hospital, Sutton, Surrey, United Kingdom

DSC-MRI methods are designed to measure vascular properties by analyzing the first-pass curves obtained from dynamic T2*-weighted imaging. The presence of recirculation and leakage will bias these measures, which can be reduced by cropping the data to exclude these features. However, useful information may be contained in the cropped data and manually selecting the cropping point is time consuming. In this abstract we present an empirically motivated model that can be fitted to DSC-MRI data that avoids the need to define cut-off times and accurately fits all the data from all pixels, including first-pass, recirculation and leakage components.

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