Gaussian Representation for Deformable Image Registration
This study presented an optimization-based DIR ap-proach that employed an explicit Gaussian representation to achieveefficient DVF estimation, strong generalization, and high interpretabil-ity.
Jul 9, 2025
A Unified Generation-Registration Framework for Improved MR-based CT Synthesis in Proton Therapy
This study conclusively demonstrates that a holistic MR-based CT synthesis approach, integrating both image-to-image translation and deformable registration, significantly improves the precision and quality of sCT generation, particularly for the challenging body area with varied anatomic changes between corresponding MR and CT.
Aug 13, 2024
Neural Graphics Primitives-based Deformable Image Registration for On-the-fly Motion Extraction
In this study, we have successfully integrated NGP into DIR, a novel contribution that significantly enhances the accuracy and efficiency of medical image alignment as demonstrated on the DIR-lab dataset. The NGPDIR framework exhibits robust performance across various metrics, particularly in landmark alignment precision and the accommodation of anatomical sliding boundaries. This advancement not only propels the DIR field forward but also opens new avenues for real-time clinical applications, potentially transforming patient care with its rapid, reliable imaging capabilities.
Jul 8, 2024
Uncertainty-aware MR-based CT synthesis for robust proton therapy planning of brain tumour
The enhanced framework incorporates 3D uncertainty prediction and generates high-quality sCTs from MR images. The framework also facilitates conditioned robust optimisation, bolstering proton plan robustness against network prediction errors. The innovative feature of uncertainty visualisation and robust analyses contribute to evaluating sCT clinical utility for individual patients.
Feb 1, 2024