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Abstract #4451

Mapping cell shape and cell density by diffusion variance decomposition (DIVIDE): Towards rapid non-invasive diagnostic and prognostic assessment of tumors

Danielle van Westen1, Filip Szczepankiewicz2, Karin Bryskhe3, Pia Sundgren1,4, and Markus Nilsson1

1Diagnostic Radiology, Lund University, Lund, Sweden, 2Medical Radiation Physics, Lund University, Lund, Sweden, 3CR Development, Lund, Sweden, 4Center for Imaging and function, Skane University hospital, Sweden

In this work we present a novel approach based on tensor-valued diffusion encoding to quantify tumor tissue characteristics such as cell shape and cell density variation — features considered in histopathology. This approach constitutes a promising framework for non-invasive diagnostic and prognostic assessment of tumors that may improve patient care by non-invasively capturing histopathological information on cell shape and cell density heterogenity.

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