Challenges and opportunities of integrating imaging and mathematical modelling to interrogate biological processes

Abstract

Advances in biological imaging have accelerated our understanding of human physiology in both health and disease. As these advances have developed, the opportunities gained by integrating with cutting-edge mathematical models have become apparent yet remain challenging. Combined imaging-modelling approaches provide unprecedented opportunity to correlate data on tissue architecture and function, across length and time scales, to better understand the mechanisms that underpin fundamental biology and also to inform clinical decisions. Here we discuss the opportunities and challenges of such approaches, providing literature examples across a range of organ systems. Given the breadth of the field we focus on the intersection of continuum modelling and in vivo imaging applied to the vasculature and blood flow, though our rationale and conclusions extend widely. We propose three key research pillars (image acquisition, image processing, mathematical modelling) and present their respective advances as well as future opportunity via better integration. Multidisciplinary efforts that develop imaging and modelling tools concurrently, and share them open-source with the research community, provide exciting opportunity for advancing these fields.

Keywords: Biophysical modelling; Multiscale; Validation.

Fig. 1

Synergetic relationships between imaging and mathematical modelling. Each rectangle represents a technology used to study a biological system (image acquisition, image processing, mathematical modelling). Each modality has an associated set of challenges (ellipses) that can be overcome by integrating all three disciplines to form an iterative cycle, motivated by the need to better understand complex biological systems.

Fig. 2

The Imaging-Modelling pipeline applied to tumours. Image acquisition, segmentation and numerical simulation used by (Stamatelos et al., 2014) to predict blood flow at the scale of a whole tumour whilst keeping the details of the microarchitecture. Here we include data from Fig. 1, 6 in (Stamatelos et al., 2014), with permission from the publisher.