An Iterative Reconstruction Method for Dental Cone-Beam Computed Tomography with a Truncated Field of View

TL;DR

This paper introduces a two-stage iterative reconstruction method for dental CBCT with truncated FOVs, using implicit neural representations to generate priors and correct projection data, significantly reducing artifacts and improving image quality.

Contribution

It presents a novel two-stage approach combining INR-based priors and iterative correction to mitigate truncation artifacts in dental CBCT reconstruction.

Findings

Effective suppression of truncation artifacts demonstrated

Improved image quality over traditional methods

Two-grid approach reduces computational burden

Abstract

In dental cone-beam computed tomography (CBCT), compact and cost-effective system designs often use small detectors, resulting in a truncated field of view (FOV) that does not fully encompass the patient's head. In iterative reconstruction approaches, the discrepancy between the actual projection and the forward projection within the truncated FOV accumulates over iterations, leading to significant degradation in the reconstructed image quality. In this study, we propose a two-stage approach to mitigate truncation artifacts in dental CBCT. In the first stage, we employ Implicit Neural Representation (INR), leveraging its superior representation power, to generate a prior image over an extended region so that its forward projection fully covers the patient's head. To reduce computational and memory burdens, INR reconstruction is performed with a coarse voxel size. The forward projection of this prior image is then used to estimate the discrepancy due to truncated FOV in the measured projection data. In the second stage, the discrepancy-corrected projection data is utilized in a conventional iterative reconstruction process within the truncated region. Our numerical results demonstrate that the proposed two-grid approach effectively suppresses truncation artifacts, leading to improved CBCT image quality.