Accurate estimation of intravoxel incoherent motion parameters based on implicit neural representation

Intravoxel incoherent motion (IVIM) diffusion-weighted imaging has important value in treatment response monitoring. However, traditional voxel-wise independent fitting methods are highly sensitive to noise and do not utilize spatial correlation, resulting in unstable parameter estimation. Although existing deep learning methods have shown improvements, they are still limited by local receptive fields.

To address this, we propose a two-stage IVIM parameter estimation framework based on Implicit Neural Representation (IVIM-INR).

Our IVIM-INR method achieves global spatial perception through coordinate encoding and enhances spatial context modeling by leveraging local 3D patch information from multi-b-value images. The first stage INR performs signal denoising, and the second stage INR accurately fits IVIM parameters.

Evaluation on brain digital phantoms, AAPM breast IVIM-dMRI Challenge data, and clinical Glioblastoma (GBM) patient data demonstrates significant advantages of the proposed method over existing techniques. In brain simulation data, when SNR = 50, the normalized mean absolute errors (NMAEs) in tumor regions were 0.16 $\pm$ 0.10 for $D_p$ , 0.02 $\pm$ 0.01 for $D_t$ , and 0.07 $\pm$ 0.05 for $F_p$ , all lower than comparison methods. In the tumor tissues of the 100 cases from the AAPM breast IVIM-dMRI challenge dataset, $F_p$ error was reduced by 58% compared to ConvNet. Intraclass correlation coefficient (ICC) analysis of real clinical data indicates that our method achieves the best performance with an ICC of $D_t$ in normal tissues reaching 0.629.

By combining INR's continuous function modeling capability with spatial-aware feature design, IVIM-INR overcomes the inherent limitations of traditional methods under noisy conditions, providing a more reliable tool for clinical IVIM quantitative analysis.