A two-dimensional (2D) systems biology-based discrete liver tissue model: A simulation study with implications for ultrasound elastography of liver fibrosis. (January 2019) - Wang, Yu; Jiang, Jingfeng
Abstract: Continuum tissue models that were often used to simulate or analyze the mechanical properties of tissues being imaged may not be biologically realistic. Our primary objective was to establish the feasibility of using systems biology to construct biologically relevant tissue models linking tissue structure, composition and architecture to the ultrasound measurements directly. The first application was designated to model fibrotic liver tissues. The proposed liver tissue model leveraged established histopathology knowledge of fibrotic liver tissues. Particularly, rules of systems biology derived from molecular histopathology were first implemented into an agent-based software platform SPARK to reflect progressions of liver fibrosis with/without steatosis. Then, microscopic compositions of tissues ( e.g. cellular components) were converted to computing grids (at the 50–100 μm scale) for wave simulations using an open-source K-Wave. To verify the physical soundness of the proposed model, virtual wave speed measurements ( i.e. shear wave speed [SWS] and the speed of sound [SOS]) were performed. Our initial results demonstrated that the simulated SWS values increased with the progression of liver fibrosis (from 1.5 m/s [Fibrosis stage 1] to 4 m/s [Fibrosis stage 4]). Similarly, the simulated SOS values were within the range of clinical data (from 1575 m/s [Fibrosis stage 0–3] to 1594 m/s [Fibrosis stage 4]). In summary, we found that those systems biology simulated fibrotic liver tissues with and without steatosis can reflect spatial characteristics of relevant histology. Also, their mechanical characteristics ( i.e. shear/compressional wave speed) were in good agreement with data reported in the clinical literature. Highlights: First systems biology-based tissue model for medical ultrasound. Excellent agreement between model-predicted results and clinical outcomes. Great potential toward building a medical imaging simulation platform leveraging biologically-realistic tissue models.