Abstract #0262

Perfusion-supervoxels for DCE-MRI based tumor subregion assessment

Benjamin John Irving ¹, Jolanta Mirecka¹, Ana L Gomes², Danny Allen², Paul Kinchesh², Veerle Kersemans², Stuart Gilchrist², Sean Smart², Julia A Schnabel³, Sir J Michael Brady², and Michael Chappell¹

¹Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom, ²Department of Oncology, University of Oxford, Oxford, United Kingdom, ³Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom

Tumors exhibit chaotic and leaky vasculature, which leads to variations in perfusion, and regions of edema, hypoxia and necrosis. We develop a method to extract perfusion-supervoxels, regions of locally similar perfusion, and use these regions with k-means clustering to define tumor subregions that are robust to noise and outliers. This method offers a number of advantages over both mean tumor parameters and voxelwise clustering.

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