Optimizing IVIM measurement on magnetic resonance linear accelerator using multi-b-value acquisition and physics-informed neural network.
Authors
Affiliations (5)
Affiliations (5)
- Department of Radiation Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No. 55, Section 4, South Renmin Road, Chengdu, 610054, Sichuan, China; Department of Radiation Oncology, Precision Radiation in Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, 610042, Sichuan, China.
- Department of Radiation Oncology, Precision Radiation in Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, 610042, Sichuan, China.
- Department of Oncology, School of Clinical Medicine, Southwest Medical University, Luzhou, 646000, Sichuan, China.
- School of Medical and Life Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, Sichuan, China.
- Department of Radiation Oncology, Precision Radiation in Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, 610042, Sichuan, China. Electronic address: [email protected].
Abstract
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. 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. 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). 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<sup>∗</sup>). 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. 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.