Optimizing IVIM measurement on magnetic resonance linear accelerator using multi-b-value acquisition and physics-informed neural network.

Background

Accurate quantification of intravoxel incoherent motion (IVIM) parameters on the magnetic resonance linear accelerator (MR-Linac) is challenging due to low signal-to-noise ratio and time constraints.

Purpose

This study aimed to evaluate the impact of b-value acquisition strategies and estimation algorithms on IVIM parameter measurement to guide reliable IVIM quantification on the MR-Linac.

Methods

A digital phantom with high number of signals averaged (high-NSA) and multi-b-value signals was constructed to evaluate estimation accuracy using root mean square error (RMSE). Ten healthy volunteers underwent four repeated diffusion-weighted imaging (DWI) scans, consisting of two high-NSA DWIs and two multi-b-value DWIs to assess repeatability via the within-subject coefficient of variation (wCV). IVIM parameters from high-NSA DWI were estimated using Segment fitting, while multi-b-value DWI data were processed using least squares (LS), Bayesian estimation, and physics-informed neural network (PINN).

Results

All IVIM parameters from multi-b-value DWI exhibited lower wCVs than those from high-NSA DWI, except for the LS-derived diffusion coefficient (D). Multi-b-value DWI combined with PINN achieved rapid and highly repeatable estimation. In the digital phantom, high-NSA DWI with Segment fitting yielded the highest RMSE for the pseudo-diffusion coefficient (D∗). LS and Bayesian methods resulted in higher D RMSEs and required longer fitting times. In contrast, multi-b-value DWI + PINN achieved the lowest RMSEs across all parameters with minimal computational cost.

Conclusion

The combination of multi-b-value acquisition and PINN provided a robust and accurate framework for IVIM parameter estimation on the MR-Linac and showed potential for integration into MR-Linac quantitative imaging workflows.