MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray

TL;DR

MedNeRF introduces a deep learning approach using neural radiance fields to reconstruct 3D-aware CT projections from a single X-ray, reducing radiation exposure while maintaining high-quality imaging.

Contribution

This work presents a novel neural radiance field architecture tailored for medical imaging, enabling 3D CT reconstruction from minimal 2D X-ray data.

Findings

High-fidelity 3D reconstructions from single X-ray images

Outperforms existing radiance field methods in medical imaging

Reduces radiation dose by enabling fewer X-ray views

Abstract

Computed tomography (CT) is an effective medical imaging modality, widely used in the field of clinical medicine for the diagnosis of various pathologies. Advances in Multidetector CT imaging technology have enabled additional functionalities, including generation of thin slice multiplanar cross-sectional body imaging and 3D reconstructions. However, this involves patients being exposed to a considerable dose of ionising radiation. Excessive ionising radiation can lead to deterministic and harmful effects on the body. This paper proposes a Deep Learning model that learns to reconstruct CT projections from a few or even a single-view X-ray. This is based on a novel architecture that builds from neural radiance fields, which learns a continuous representation of CT scans by disentangling the shape and volumetric depth of surface and internal anatomical structures from 2D images. Our model is trained on chest and knee datasets, and we demonstrate qualitative and quantitative high-fidelity renderings and compare our approach to other recent radiance field-based methods. Our code and link to our datasets are available at https://github.com/abrilcf/mednerf