A Computational Investigation of DSC-MRI Signals From 3D Tissue Structures with Varied Cellular Features

Motivation: Dynamic susceptibility contrast (DSC) MRI is a robust method for imaging brain tumors, and there is potential to glean more clinically useful data than is obtained with standard-of-care DSC-MRI. Goal(s): We aimed to systematically investigate the effect of varied cellular features on the difference in DSC-MRI-derived ΔR2* time curves to evaluate the feasibility of recovering these features in real tissue. Approach: We generated 3D tissue structures of ellipsoids (determined by specified parameters and randomly distributed) and applied the finite difference finite perturber method to compute ΔR2*. Results: In general, ΔR2* increased then plateaued as cell volume fraction, aspect ratio, and size increased. Impact: We simulated T2*-weighted DSC-MRI signal for 3D tissue structures with varied cellular features to evaluate the feasibility of recovering these features in real tissue. Results suggested that cell volume fraction, aspect ratio, and size may be identifiable biomarkers.