Landmark matching and B-spline implicit neural representations for diffusion-weighted imaging distortion correction
Year: 2026
Type: article
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.
Source: Physics in Medicine and Biology
Authors: Yunxiang Li, Yen-Peng Liao, Yan Dai, Jie Deng, You Zhang
Institution: The University of Texas Southwestern Medical Center
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,325
Awards: High-Precision Lung Radiotherapy by Intra-treatment Dynamic Cone-beam CT imaging and Dosimetry-guided Plan Adaptation, Accurate 4D Liver Tumor Localization for Radiotherapy using Contrast-Agent-Free X-ray Imaging and Liver Biomechanical Modeling, High-Precision Proton Therapy for Liver Cancer: Developing an End-to-end Strategy with Real-time Liver Tumor Localization and On-the-fly Plan Delivery Adaptation, Artificial Intelligence-Based Quality Assurance for Online Adaptive Radiotherapy
OpenAlex ID: W7127347589