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

• Main Authors: Corona-Figueroa, Abril, Frawley, Jonathan, Taylor, Sam Bond, Bethapudi, Sarath, Shum, Hubert P. H., Willcocks, Chris G.
• Format: Conference Proceeding
• Language: English
• Subjects: Computed tomography; Deep learning; Ionizing radiation; Shape; Solid modeling; Surface reconstruction; Three-dimensional displays
• ISSN: 2694-0604
• DOI: 10.1109/EMBC48229.2022.9871757

Computed tomography (CT) is an effective med-ical 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 multi planar 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 qual-itative 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://qithub.com/abrilcf/mednerf Clinical relevance- Our model is able to infer the anatomical 3D structure from a few or a single-view X-ray showing future potential for reduced ionising radiation exposure during the imaging process.