RepMedGraf: Re-parameterization Medical Generated Radiation Field for Improved 3D Image Reconstruction

In the field of medical imaging, 3D image reconstruction has emerged as a crucial technique for accurate disease diagnosis. Neural Radiance Field (NeRF) has shown promise in generating high-quality 3D models through deep learning, but its application in medical imaging remains limited. This paper presents RepMedGraf, an improved model addressing the limitations of NeRF. RepMedGraf utilizes a generator based on lightweight RepVGG Blocks instead of MedNeRF model. By doing so, the risk of overfitting is reduced, and training efficiency is improved. The proposed method is trained on publicly available chest and knee datasets. Comparative evaluations are conducted based on the generated radiation fields, demonstrating the effectiveness and quality of the 3D models produced by RepMedGraf. The results highlight the potential of RepMedGraf as a valuable tool in medical imaging for enhanced diagnostic accuracy and improved patient care.