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 Sundgren^1,4, 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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