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 proof-of-concept study of direct magnetic resonance imaging-based proton dose calculation for brain tumors via neural networks with Monte Carlo-comparable accuracy
This study demonstrated the feasibility of MC-quality proton dose calculations directly from MR images for brain tumor patients, achieving comparable accuracy with faster computation and simplified implementation.
Jul 5, 2025
Diffusion Schrödinger bridge models for high-quality MR-to-CT synthesis for proton treatment planning
A diffusion Schrödinger bridge model for high-quality MR-to-CT synthesis for proton treatment planning.
Jan 1, 2025
Generating Synthetic Computed Tomography for Radiotherapy: SynthRAD2023 Challenge Report
SynthRAD2023 challenge report comparing synthetic CT generation methods for radiotherapy using multi-center data, evaluating both image similarity and dose-based metrics for MRI-to-CT and CBCT-to-CT tasks.
Oct 1, 2024
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
Continuous sPatial-Temporal Deformable Image Registration (CPT-DIR) for motion modelling in radiotherapy: beyond classic voxel-based methods
In summary, the innovative CPT-DIR approach, integrating principles of INR and LDDMM, represents a11 significant departure from traditional voxel-based methods in DIR. By adopting a paradigm of continuous motion modelling, we transcend the limitations inherent in voxel-based representations, offering a more robust, automatic and versatile solution. Leveraging spatial continuity, we effectively handle the intricacies of sliding organ boundaries, while temporal continuity alleviates the complexities associated with significant anatomical changes over time. The tangible benefits are evident in its superior performance compared to classic B-splines methods. CPT-DIR consistently achieves better performance by all kinds of evaluation matrices. Additionally, the efficiency gains are substantial, with registration times slashed by more than half.
May 1, 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