Metal artifacts have been a challenge in computed tomography (CT) for nearly four decades. Despite intensive research in this area, challenges still exist in commercial metal artifact reduction (MAR) solutions. MAR is particularly important for radiation therapy and proton therapy treatment planning because metal artifacts not only degrade the outline of tumors and sensitive organs, but also introduce errors in stopping power estimation, compromising dose prediction accuracy. In this study, we developed a MAR approach that combines hardware and algorithmic innovations to systematically tackle the challenge of metal artifacts in radiation therapy. We propose to operate the X-ray tube at exceptionally high voltage and the detector DAS with adaptive triggering rate to prevent photon starvation in the CT raw data, followed by physics-based sinogram domain precorrection and model-based iterative reconstruction to correct the metal artifacts. We performed an end-to-end simulation of the integrated MAR approach with advanced hardware and algorithmic solutions. We simulated 700mAs/140 kVp and 550mAs/180 kVp CT scans, 984 views, with and without adaptive triggering, of an image volume based on the Visible Human Project CT data set, and after inserting two Titanium hip prostheses. The results demonstrated that the proposed MAR scheme can effectively eliminate metal artifacts and improve the accuracy of proton therapy planning. The dosimetric evaluation showed that with the proposed MAR solution, the error in range calculation was reduced from 7 mm to <1 mm.
Yannan Jin, Drosoula Giantsoudi, Lin Fu, Joost Verburg, Lars Gjesteby, Ge Wang, Harald Paganetti, and Bruno De Man, "Metal artifact reduction for radiation therapy: a simulation study," Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 105730Q (Presented at SPIE Medical Imaging: February 13, 2018; Published: 9 March 2018); https://doi.org/10.1117/12.2293840.
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