Dentomaxillofacial CBCT: Clinical Challenges for Indication-oriented Imaging

Abstract

This critical review discusses the clinical challenges for patient-specific and indication-oriented dentomaxillofacial cone beam computed tomography (CBCT). Large variations among units and protocols may lead to variable degrees of diagnostic and three-dimensional model accuracy, impacting both specific diagnostic tasks and treatment planning. Particular indications, whether diagnostic or therapeutic, may give rise to very specific challenges with regard to CBCT unit and parameter setup, considering the required image quality, segmentation accuracy, and artifact level. Considering that dental materials are in the field of view needed for diagnosis or treatment planning, artifact expression is a dominant factor in proper CBCT selection. The heterogeneity of dental CBCT units and performances may highly impact the scientific results. Thus research findings cannot be simply generalized as published evidence, and a demonstrated clinical applicability for a specific indication should not be simply extrapolated from one CBCT unit to another.

Comparison of deep learning segmentation and multigrader-annotated mandibular canals of multicenter CBCT scans

“…The generalisation capability of the artificial intelligence algorithms in radiology is considered to be one of challenges, due to large variances between the imaging parameters, such as protocols, technical solutions, field of view, imaging parameters, and voxel sizes, as well as heterogeneities such as patient anatomy and pathology 2 . In addition, the CBCT image quality is affected by patient movement and metallic artefacts caused by dental or oral surgical materials [17][18][19] . Despite this, we observed that the deep learning system had similar performance across the different imaging devices as well as a variety of patient specific heterogeneities.…”

Quantitative evaluation of artifact expression of mesoporous calcium silicate nanoparticles as a new promising root canal sealer in cone-beam computed tomography

“…CBCT has excellent help in diagnosing, treating and evaluating pulp diseases, but its image quality is often affected by many factors such as artifacts. Studies have shown that differences between the actual physical conditions of the measurement equipment and the simpli ed mathematical assumptions used for 3D reconstruction lead to artifacts [20] . Other factors, such as initial reconstruction, artifact reduction algorithm, projection polychromatic, patient movement, FOV and photon diffraction, all affect the generation of artifacts [2] .…”

Utility of spectral CT with orthopedic metal artifact reduction algorithms for 125I seeds implantation in mediastinal and hepatic tumors

“…The original data were imported to post-processing workstation (Intellispace Portal, Philips Healthcare) to generate spectral-based images (SBIs) with and without the O-MAR algorithm ( 8 ). Then the SBIs were divided into conventional image (CI), VMI with 6 levels of 50, 70, 90, 110, 130, and 150 keV, CI combined with O-MAR (CI + O-MAR), and VMI combined with O-MAR (VMI + O-MAR).…”

“…Photon starvation occurs due to the considerable absorption of low-energy photons by high-density and high-atomic-number metallic implants. This phenomenon leads to an increase in noise and loss of projection data, ultimately resulting in the formation of hypodense artifacts in reconstructed images ( 8 ). Beam-hardening artifacts are caused by a phenomenon in which the high density and high atomic number of metallic objects cause the absorption of a significant number of low-energy photons when exposed to X-rays ( 9 ).…”