UMedNeRF: Uncertainty-aware Single View Volumetric Rendering for Medical Neural Radiance Fields
This addresses a safety issue in clinical medicine by potentially lowering radiation doses, but it is incremental as it builds on existing neural radiance field approaches.
The paper tackles the problem of generating CT-like volumetric renderings from single X-ray images to reduce patient radiation exposure, achieving results comparable to other methods on knee and chest datasets.
In the field of clinical medicine, computed tomography (CT) is an effective medical imaging modality for the diagnosis of various pathologies. Compared with X-ray images, CT images can provide more information, including multi-planar slices and three-dimensional structures for clinical diagnosis. However, CT imaging requires patients to be exposed to large doses of ionizing radiation for a long time, which may cause irreversible physical harm. In this paper, we propose an Uncertainty-aware MedNeRF (UMedNeRF) network based on generated radiation fields. The network can learn a continuous representation of CT projections from 2D X-ray images by obtaining the internal structure and depth information and using adaptive loss weights to ensure the quality of the generated images. Our model is trained on publicly available knee and chest datasets, and we show the results of CT projection rendering with a single X-ray and compare our method with other methods based on generated radiation fields.