Non-invasive measurement of intra-tumoral fluid dynamics with localized convolutional function regression

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a routine method to non-invasively quantify perfusion dynamics in tissues. The standard practice for analyzing DCE-MRI data is to fit an ordinary differential equation to each voxel. Recent advances in data science provide an opportunity to move beyond existing methods to obtain more accurate measurements of fluid properties. Here we present localized convolutional function regression, simultaneously measuring interstitial fluid velocity, diffusion, and perfusion in 3D. We validate the method computationally and experimentally, demonstrating accurate measurement of fluid dynamics in situ and in vivo. Applying the method to human MRIs, we observe tissue-specific differences in fluid dynamics, with an increased fluid velocity in breast cancer as compared to brain cancer. One-Sentence Summary A physics-informed computational method enables accurate and efficient measurement of fluid dynamics in individual patient tumors and demonstrates differences between tissues. ### Competing Interest Statement The methodologies described herein are disclosed and claimed in a pending patent application co-owned by City of Hope and Virginia Polytechnic Institute and State University listing RTW, JRC, RCR, and JMM as co-inventors. RTW, JRC, CS, RCR, and JMM own stake in Cairina Inc.