# Reducing Metal Artifacts in Clinical X-Ray CT via Image Reconstruction Techniques > **NIH NIH R15** · UTAH VALLEY UNIVERSITY · 2022 · $373,782 ## Abstract Title: Reducing Metal Artifacts in Clinical X-Ray CT via Image Reconstruction Techniques Abstract: Metallic implants such as dental fillings, surgical clips, coils, wires, and orthopedic hardware inside the patient body are very helpful in providing patients with better health benefits. On the other hand, they may cause artifacts in many medical imaging modalities such as in x-ray CT and MRI. Even though significant advances in both hardware and software have been made over the years, metal artifacts in x-ray CT scans are still troublesome. The metal artifacts appear as dark shadows and bright streaks. These artifacts, if not corrected, can severely degrade the image quality and decrease the diagnostic value of the clinical examination. X-ray generation in an x-ray tube is very inefficient; the efficiency is much less than 1%. A high photon count requirement results in the acceptance of a wide energy spectrum, which is contributed by both Bremsstrahlung and characteristic radiation. Metallic objects inside the patient body can cause photon starvation and beam hardening. These effects are nonlinear and difficult to establish an exact mathematical model for them. One of the state-of-the-art remedies is to use the dual-energy CT to measure two sets of projections, and then by using a mathematical model and combining these two data sets to estimate a set of synthetic monoenergetic x-ray measurements. Another one of the remedies is the use of a metal artifact reduction (MAR) algorithm that replaces the corrupted projection measurements in the detector with interpolation from neighboring uncorrupted projections. In data starvation situations, the dual-energy methods are not effective. The current MAR algorithms are still immature, and they may create new artifacts while trying to suppress the metal artifacts. This is a renewal application of our previous R15 grant entitled “Fast and Robust Low-Dose X-Ray CT Image Reconstruction,” in which we have successfully developed image reconstruction algorithms to combat noise. Some of our ideas formed during the last three years can be further advanced into new ideas for metal artifact reduction in x-ray CT. In this R15 renewal, we will focus on new metal artifact reduction (MAR) algorithm development based on conventional single-energy data acquisition. The innovation is the new way to set up the objective functions for optimization. The uniqueness of our objective functions is that they do not have a data fidelity term; they only contain the Bayesian terms. The Bayesian terms are formed from the metal artifact features. A gradient descent algorithm is used to optimize the objective functions and a new set of un-corrupted measurements are estimated. The final image is reconstructed by the filtered backprojection (FBP) algorithm. The proposed algorithms are cost-effective. We hypothesize that the proposed methods will be more effective than the state-of-the-art MAR methods available for commercial CT scanner... ## Key facts - **NIH application ID:** 10330750 - **Project number:** 2R15EB024283-03 - **Recipient organization:** UTAH VALLEY UNIVERSITY - **Principal Investigator:** Gengsheng Lawrence Zeng - **Activity code:** R15 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $373,782 - **Award type:** 2 - **Project period:** 2018-05-01 → 2026-05-05 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10330750 ## Citation > US National Institutes of Health, RePORTER application 10330750, Reducing Metal Artifacts in Clinical X-Ray CT via Image Reconstruction Techniques (2R15EB024283-03). Retrieved via AI Analytics 2026-06-03 from https://api.ai-analytics.org/grant/nih/10330750. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*