Evaluation of a Metal Artifact Reduction Algorithm for Image Reconstruction on a Novel CBCT Platform
Abby Yashayaeva1, Robert Lee MacDonald1,2,3, James Robar1,2,3
1Department of Physics and Atmospheric Sciences, Dalhousie University, Halifax, Canada.
Journal of Applied Clinical Medical Physics
|September 17, 2024
Summary
A new metal artifact reduction (MAR) algorithm for Varian's HyperSight system significantly reduces artifacts in cone-beam CT (CBCT) images caused by metal implants. This improved image quality is beneficial for patients with metallic prostheses.
Area of Science:
- Medical Imaging
- Radiological Physics
- Image Reconstruction
Background:
- Metal implants in patients undergoing computed tomography (CT) scans cause significant artifacts, distorting Hounsfield unit (HU) values and compromising image quality.
- These artifacts can obscure critical anatomical details, potentially impacting diagnosis and treatment planning.
- Existing metal artifact reduction (MAR) techniques may have limitations in fully addressing these distortions.
Purpose of the Study:
- To characterize and evaluate a novel metal artifact reduction (MAR) algorithm developed for the HyperSight imaging system.
- To assess the effectiveness of this MAR algorithm in reconstructing artifact-free cone-beam CT (CBCT) images in the presence of metal implants.
Main Methods:
- Three phantoms with common medical metal implants (solid water block with metal samples, Advanced Electron Density phantom with metal rods, hip prostheses in water) were imaged.
- CBCT images were reconstructed using both the novel MAR algorithm and the iCBCT Acuros algorithm on the HyperSight system.
- Image quality was quantitatively assessed using metrics like signal-to-noise ratio (SNR), artifact index (AI), structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and mean-square error (MSE) compared to artifact-free references.
Main Results:
- The novel MAR algorithm demonstrated significantly improved similarity to baseline images compared to iCBCT Acuros for most high-density materials, showing better performance in SNR, AI, SSIM, PSNR, and MSE.
- MAR images showed mean Hounsfield units returning to expected background levels closer to the metal samples, with lower standard deviation at all distances.
- A significant reduction in artifact volume was observed with the MAR algorithm for metal samples (excluding aluminum) and hip prostheses.
Conclusions:
- Varian's HyperSight MAR reconstruction algorithm effectively reduces metal artifact metrics in CBCT images.
- The characterized MAR algorithm shows promise for improving image quality in patients with metal implants.
- These findings support the clinical utility of MAR reconstruction for enhancing diagnostic accuracy in the presence of metallic hardware.
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