Integration of steady-state diffusion MRI with Neural Posterior Estimation (NPE) for post-mortem investigations
Post-mortem diffusion MRI plays a key role in investigative pipelines to characterise tissue microstructure, with long scan times facilitating the acquisition of datasets with improved spatial/angular resolution and reduced artefacts versus in vivo. Diffusion-weighted steady-state free precession (DW-SSFP) has emerged as a powerful technique for post-mortem imaging, achieving high SNR-efficiency and strong diffusion weighting in the challenging imaging environment of fixed tissue. However, the sophisticated signal forming mechanisms of DW-SSFP limit the integration of advanced microstructural models (e.g. incorporating time-dependence; Monte-Carlo simulations) with parameter estimation routines. Here, I investigate the integration of DW-SSFP with neural posterior estimation (NPE), a parameter inference technique leveraging concepts from Bayesian statistics and machine learning to directly estimate P({theta} | S) (i.e. the posterior distribution of parameters {theta} given signal S). A key challenge is that diffusion attenuation in DW-SSFP is dependent on tissue relaxation properties (T1/T2) and transmit inhomogeneity (B1), which must be incorporated into the NPE network for accurate modelling. By using NPE to estimate P({theta} | S,T1,T2,B1) (i.e. conditioning on S and known T1/T2/B1), using a Tensor representation, I demonstrate that NPE achieves accurate parameter estimation even in the presence of non-Gaussian (Rician) noise in low-SNR regimes. Comparisons with conventional non-linear least-squares (NLLS) using both synthetic and experimental DW-SSFP data (whole human post-mortem brain) give excellent agreement, with NPE providing 1000s of posterior samples in a matched evaluation time. Taken together, findings provide a framework to integrate advanced microstructural models with DW-SSFP, and an intuitive approach to incorporate conditional dependencies with NPE.