NEAT-Net: An Unsupervised Cross-View Prior Inpainting Network for CBCT Metal Artifact Reduction.

Intraoperative Cone-Beam Computed Tomography (CBCT) facilitates intraoperative navigation for Minimally Invasive Spine Surgery (MISS). However, high-attenuation metal implants used in MISS often cause metal artifacts in the reconstructed CBCT images. Current algorithms do not consider the cross-view information in the projection-domain for metal artifact reduction (MAR).

Intra-Guided Dual-Energy Material Decomposition Method for Dual-Source CBCT Systems.

Dual-energy CBCT imaging plays a crucial role in advanced imaging applications due to its ability to quantify material components. Although there are multiple established systems for dual-energy imaging, they often come with high deployment costs. This study investigates the feasibility of dual-energy material decomposition on a less expensive dual-source CBCT system.

Dual-Source CBCT for Larger Longitudinal Coverage: System Design and Image Reconstruction.

The application of CBCT systems in intraoperative environments has become increasingly common, but concurrent CBCT systems are unsuitable for situations that require a large longitudinal imaging FoV, such as orthopedics. To increase longitudinal coverage, we developed a dual-source CBCT (DS-CBCT) system in which two ray sources are symmetrically placed along the central plane. After further analyzing its geometric characteristics, we propose an analytical reconstruction algorithm termed DT-FDK specialized for DS-CBCT, which combines cone-beam rebinning and two rays with smaller cone angles among the four conjugate rays to further suppress cone beam artifacts.

CBCT projection domain metal segmentation for metal artifact reduction using hessian-inspired dual-encoding network with guidance from segment anything model.

Background: Metal artifact is a prevailing factor reducing the image quality of cone-beam computed tomography (CBCT), which is a widely used medical imaging method. Existing metal artifact reduction (MAR) methods typically contain two steps: segmentation and interpolation. Recent MAR algorithms pay more attention to the interpolation of the metal traces, but metal segmentation is also challenging, especially for CBCT.

PDS-MAR: a fine-grained projection-domain segmentation-based metal artifact reduction method for intraoperative CBCT images with guidewires.

Since the invention of modern Computed Tomography (CT) systems, metal artifacts have been a persistent problem. Due to increased scattering, amplified noise, and limited-angle projection data collection, it is more difficult to suppress metal artifacts in cone-beam CT, limiting its use in human- and robot-assisted spine surgeries where metallic guidewires and screws are commonly used.To solve this problem, we present a fine-grained projection-domain segmentation-based metal artifact reduction (MAR) method termed PDS-MAR, in which metal traces are augmented and segmented in the projection domain before being inpainted using triangular interpolation.