# Field-of-View Extension in Dental Cone-Beam CT via Implicit Neural Representations and Diffusion Model-Based Refinement

arXiv: https://arxiv.org/abs/2609.28110 · HTML: https://mlsanity.com/paper/2609.28110/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 23 Sep 2026.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 9 of 9)

- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [NAF: Neural Attenuation Fields for Sparse-View CBCT Reconstruction](https://mlsanity.com/paper/2209.14540/) (2022)
- [fastWDM3D: Fast and Accurate 3D Healthy Tissue Inpainting](https://mlsanity.com/paper/2507.13146/) (2025)
- [Body Composition Assessment with Limited Field-of-view Computed Tomography: A Semantic Image Extension Perspective](https://mlsanity.com/paper/2207.06551/) (2022)
- [Diffusion-based Generative Image Outpainting for Recovery of FOV-Truncated CT Images](https://mlsanity.com/paper/2406.04769/) (2024)
- [NeRF-based CBCT Reconstruction needs Normalization and Initialization](https://mlsanity.com/paper/2506.19742/) (2025)
- [An Iterative Reconstruction Method for Dental Cone-Beam Computed Tomography with a Truncated Field of View](https://mlsanity.com/paper/2508.07618/) (2025)
- [Fast and accurate sparse-view CBCT reconstruction using meta-learned neural attenuation field and hash-encoding regularization](https://mlsanity.com/paper/2312.01689/) (2023)
- [Efficient Image-to-Image Schr"odinger Bridge for CT Field of View Extension](https://mlsanity.com/paper/2508.11211/) (2025)

## Abstract (arXiv)

Dental cone-beam computed tomography (CBCT) systems often employ detector configurations that provide a truncated field of view (FOV) that only captures a small part of the patient's anatomy. In this work, we aim to reconstruct an extended FOV using projections of truncated FOV scans. To this end, we propose a three-stage framework that consists of (1) an implicit neural representation (INR) for estimating missing parts of the truncated projection data, (2) an iterative reconstruction for generating a secondary volumetric image with improved anatomical consistency and (3) a fast diffusion model for image enhancement. The proposed approach combines the strengths of continuous representations, physics-based reconstruction and generative refinement within a unified pipeline for truncated CBCT imaging. Experimental results demonstrate that the method effectively reduces truncation artifacts, improves the reconstruction of structures extending beyond the original FOV and produces images with enhanced quality. Our code is publicly available at https://github.com/SusanneSchaub/CBCT-FOV-Extension.