# Spectral CT metal artifact correction

> **NIH NIH R01** · MARQUETTE UNIVERSITY · 2022 · $277,478

## Abstract

PROJECT SUMMARY
Radiation therapy treatment planning can be severely impacted by the presence of metal objects such as
implants and orthopaedic hardware. Metal objects cause artifacts in computed tomography (CT) images that
obscure anatomical structures and alter the CT numbers, both of which are critical to estimate accurately for the
purpose of planning radiation therapy. These uncertainties can cause underdosing of tumors and overdosing of
healthy tissue. Existing metal artifact reduction techniques do not fully mitigate all artifacts created by the metal
objects and are known to introduce new artifacts. This project will develop a spectral CT imaging method to
reduce metal artifacts while maintaining CT number accuracy and soft tissue contrast. We propose to reduce
metal artifacts in CT imaging by using state-of-the-art acquisition techniques, combined with an optimization-
based reconstruction framework. We developed a constrained `one-step' spectral CT image reconstruction
(cOSSCIR) algorithm in previous work and preliminary studies demonstrate feasibility of the proposed algorithm
to reduce metal artifacts to <8 HU error. The incorporation of physical effects into the data model is one method
by which the algorithm reduces metal artifacts. The optimization framework developed by our group uniquely
incorporates constraints that mitigate undersampling due to unreliable measurements that pass through metal
and also enable acquisition approaches that will reduce the number of unreliable measurements. The methods
are designed to correct metal artifacts broadly and automatically without requiring knowledge of the implant
material. The project objective to reduce metal artifacts while maintaining soft tissue contrast and CT number
accuracy will be achieved by further developing the cOSSCIR algorithm and investigating its application to both
dual-kV and photon-counting spectral acquisition methods using simulations, phantom experiments, and clinical
photon-counting CT datasets. The algorithm will also be evaluated relative to task of radiation therapy planning
for prostate cancer in the presence of hip prostheses using simulations and phantom experiments. The
developed spectral CT metal artifact correction method will be compared to gold-standard images and an
established metal artifact reduction technique. Successful completion of the project aims will result in a method
to reduce metal artifacts in CT images while maintaining soft tissue contrast and CT number accuracy that has
been validated on simulated and experimental phantom data.

## Key facts

- **NIH application ID:** 10372913
- **Project number:** 5R01EB023968-04
- **Recipient organization:** MARQUETTE UNIVERSITY
- **Principal Investigator:** Taly Gilat Schmidt
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $277,478
- **Award type:** 5
- **Project period:** 2019-05-01 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10372913

## Citation

> US National Institutes of Health, RePORTER application 10372913, Spectral CT metal artifact correction (5R01EB023968-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10372913. Licensed CC0.

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