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Abstract #3677

Automated tumour definition in diffusion imaging from Gaussian mixture modelling of intrinsically-registered PET data in breast cancer: pilot study

Maren Marie Andreassen1, Tone Bathen1,2, Pål Erik Goa3,4, Steinar Lundgren5,6, Roja Hedayati5,6, Torill Sjøbakk1, Hans Petter Eikesdal7,8, and Neil P. Jerome1,4

1Department of Circulation and Medical Imaging, NTNU, Norwegian University of Science and Technology, Trondheim, Norway, 2St.Olavs Hospital, Trondheim University Hospital, Trondheim, Norway, 3Department of Physics, NTNU, Norwegian University of Science and Technology, Trondheim, Norway, 4Department of Radiology and Nuclear Medicine, Trondheim University Hospital, Trondheim, Norway, 5Department of Clinical and Molecular Medicine, NTNU, Norwegian University of Science and Technology, Trondheim, Norway, 6Department of Oncology, Trondheim University Hospital, Trondheim, Norway, 7Section of Oncology, Department of Clinical Science, University of Bergen, Bergen, Norway, 8Department of Oncology, Haukeland University Hospital, Bergen, Norway

Simultaneous positron emission tomography and magnetic resonance imaging (PET/MRI) is an emerging technique in breast cancer practice, allowing collection of morphologic parameters in addition to real-time metabolism. Though feasibility has been demonstrated, the best approach to utilize PET/MRI has not yet been validated. This study focus on evaluating a Gaussian mixture model (GMM) based segmentation technique from PET images with intrinsic MRI registration as a proxy for regions-of-interest (ROIs) manually drawn on post contrast images. The application of the method has been evaluated in a neoadjuvant treatment response assessment setting using apparent diffusion coefficient (ADC) values.

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