Simulation-informed evaluation of microvascular parameter mapping for diffusion MR imaging of solid tumours
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Purpose
We aim to inform the design of new diffusion MRI (dMRI) approaches for microvasculature mapping that enhance the biological specificity of imaging towards cancer.
Methods
We adopted simulation-informed modelling of the vascular dMRI signal. We synthesised signals from 1500 synthetic vascular networks, for a variety of protocols (flow-compensated (FC), non-compensated (NC), hybrid), featuring different b samplings and diffusion times. We estimated the number of independent, recoverable signal degrees of freedom in presence of noise (signal-to-noise ratio of 5), and ranked 12 microvascular metrics depending on the quality of their estimation. Lastly, we demonstrated the feasibility of estimating the top-ranking metrics on 3T dMRI of a healthy volunteer and of a metastatic colorectal cancer (CRC) patient.
Results
Both NC and FC synthetic vascular signals exhibit complex behaviour, e.g., non-zero kurtosis and diffusion time dependence. Two independent degrees of freedom appear recoverable from directionally-averaged vascular signals (SNR of 5). Mean volumetric flow rate q m and an Apparent Network Branching (ANB) index maximise correlations between ground truth and estimated values in silico . Their estimation is proposed for in vivo imaging, and demonstrated herein. In the patient, both q m and ANB detect re-vascularisation after 3 months of targeted therapy against liver metastases, consistently with Intra-Voxel Incoherent Motion (IVIM) metrics.
Conclusions
Simulation-based modelling of the vas-cular dMRI signal informs the design of promising approaches for in vivo microvasculature characterisation.
