Faculty Advisor(s)
Elaine Halesey
Abstract
This project explains the artifact reduction capabilities of dual-energy computed tomography (DECT) systems, and how to maximize this when imaging patients with metallic hardware, such as spinal implants. Implants consisting of titanium alloys, cobalt alloys, or stainless steel, present difficulties when imaging due to their likelihood to cause significant shading and streaking image artifacts. These artifacts are mainly the result of beam hardening and photon starvation from the x-ray beam’s interaction with dense, metallic structures. Dual-energy image acquisition allows for the potential to utilize basis material decomposition to virtually simulate a single energy acquired image. This process, known as monoenergetic extrapolation, significantly reduces beam hardening artifacts; however, the problem of photon starvation still poses an issue. To combat this, dual-energy acquired images must be further processed through additional artifact reduction software to produce optimal images for diagnosis. A study comparing DECT images reconstructed with an iterative metal artifact reconstruction algorithm (DE iMAR), DECT images reconstructed with a virtual monochromatic imaging algorithm (DE Mono+), and DECT images reconstructed with a combination of DE iMAR and DE mono+ (DE iMAR Mono+), was analyzed concluding that CT technologists should utilize DE iMAR Mono+ when imaging patients with metallic implants to produce optimal images.
Publication Date
2021
Document Type
Poster
Department
Medical Imaging
Keywords
Dual-Energy Computed Tomography (DECT), Metallic Implant Imaging, Metallic Artifact Reduction, Beam Hardening Artifacts, Photon Starvation Artifacts, iterative Metal Artifact Reconstruction Algorithm (iMAR), Virtual Monochromatic Imaging Algorithm (Mono+)
Disciplines
Medicine and Health Sciences
Recommended Citation
Kinney, Kevin, "Metallic Artifact Reduction Capabilities of Dual-Energy Computed Tomography (DECT)" (2021). Student Research Poster Presentations 2021. 71.
https://digitalcommons.misericordia.edu/research_posters2021/71