# Landmark matching and B-spline implicit neural representations for diffusion-weighted imaging distortion correction

## TL;DR

This paper introduces a new method to correct geometric distortions in diffusion-weighted imaging, improving accuracy for medical applications like radiation therapy.

## Contribution

A novel framework combining landmark matching and B-spline neural representations for robust and accurate DWI distortion correction.

## Key findings

- The proposed method achieved Dice coefficients of 0.919 in brain datasets and 0.926 in abdominal datasets.
- On simulated data, it outperformed others with PSNR of 25.912 dB, NCC of 0.911, and SSIM of 0.888.

## Abstract

Objective. Geometric distortions in diffusion-weighted imaging (DWI) compromise accurate tumor delineation and spatial localization, limiting its utility in radiation therapy planning and response monitoring. These distortions can be corrected through multimodal registration between distorted DWI and undistorted anatomical images, while conventional mutual information-based optimization often fails due to local minima and produces non-smooth, physically implausible deformations. Approach. This study proposes a landmark matching B-spline implicit neural representation framework for DWI distortion correction. The method integrates anatomical correspondences from a foundation landmark matching model with B-spline parameterized deformation fields to overcome local minima inherent in mutual information optimization. The framework employs Fourier-encoded multi-layer perceptrons to model B-spline deformation fields while ensuring physically plausible transformations, enabling robust multimodal registration between distorted DWI and anatomical references. Main results. Evaluation on brain and abdominal datasets demonstrated superior performance compared to established methods. The proposed approach achieved average Dice coefficients of 0.919 ± 0.038 (brain) and 0.926 ± 0.032 (abdomen), significantly outperforming all baseline methods. On simulated data, our method achieved an average PSNR of 25.912 ± 3.148 dB, NCC of 0.911 ± 0.137, and SSIM of 0.888 ± 0.107, the best among all methods. Significance. By combining the regularization properties of B-spline parameterization with the cross-modal matching capabilities of foundation models, our method achieves more accurate correction of geometric distortions in DWI, with the potential to enhance the precision of intra/post-radiotherapy assessment.