Marian Axente1, Ajay Paidi2, Rie Von Eyben1, Chuan Zeng3, Ali Bani-Hashemi2, Andreas Krauss4, Dimitre Hristov1. 1. Radiation Oncology, Stanford Hospital and Clinics, 875 Blake Wilbur Drive, Stanford, California 94305-5847. 2. Computed Tomography and Radiation Oncology Department, Siemens Medical Solutions USA, 757A Arnold Drive, Martinez, California 94553. 3. Radiation Oncology, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, Pennsylvania 19104. 4. Imaging and Therapy Division, Siemens AG, Healthcare Sector, Siemensstr. 1, Forcheim 91301, Germany.
Abstract
PURPOSE: To clinically evaluate an iterative metal artifact reduction (IMAR) algorithm prototype in the radiation oncology clinic setting by testing for accuracy in CT number retrieval, relative dosimetric changes in regions affected by artifacts, and improvements in anatomical and shape conspicuity of corrected images. METHODS: A phantom with known material inserts was scanned in the presence/absence of metal with different configurations of placement and sizes. The relative change in CT numbers from the reference data (CT with no metal) was analyzed. The CT studies were also used for dosimetric tests where dose distributions from both photon and proton beams were calculated. Dose differences and gamma analysis were calculated to quantify the relative changes between doses calculated on the different CT studies. Data from eight patients (all different treatment sites) were also used to quantify the differences between dose distributions before and after correction with IMAR, with no reference standard. A ranking experiment was also conducted to analyze the relative confidence of physicians delineating anatomy in the near vicinity of the metal implants. RESULTS: IMAR corrected images proved to accurately retrieve CT numbers in the phantom study, independent of metal insert configuration, size of the metal, and acquisition energy. For plastic water, the mean difference between corrected images and reference images was -1.3 HU across all scenarios (N = 37) with a 90% confidence interval of [-2.4, -0.2] HU. While deviations were relatively higher in images with more metal content, IMAR was able to effectively correct the CT numbers independent of the quantity of metal. Residual errors in the CT numbers as well as some induced by the correction algorithm were found in the IMAR corrected images. However, the dose distributions calculated on IMAR corrected images were closer to the reference data in phantom studies. Relative spatial difference in the dose distributions in the regions affected by the metal artifacts was also observed in patient data. However, in absence of a reference ground truth (CT set without metal inserts), these differences should not be interpreted as improvement/deterioration of the accuracy of calculated dose. With limited data presented, it was observed that proton dosimetry was affected more than photons as expected. Physicians were significantly more confident contouring anatomy in the regions affected by artifacts. While site specific preferences were detected, all indicated that they would consistently use IMAR corrected images. CONCLUSIONS: IMAR correction algorithm could be readily implemented in an existing clinical workflow upon commercial release. While residual errors still exist in IMAR corrected images, these images present with better overall conspicuity of the patient/phantom geometry and offer more accurate CT numbers for improved local dosimetry. The variety of different scenarios included herein attest to the utility of the evaluated IMAR for a wide range of radiotherapy clinical scenarios.
PURPOSE: To clinically evaluate an iterative metal artifact reduction (IMAR) algorithm prototype in the radiation oncology clinic setting by testing for accuracy in CT number retrieval, relative dosimetric changes in regions affected by artifacts, and improvements in anatomical and shape conspicuity of corrected images. METHODS: A phantom with known material inserts was scanned in the presence/absence of metal with different configurations of placement and sizes. The relative change in CT numbers from the reference data (CT with no metal) was analyzed. The CT studies were also used for dosimetric tests where dose distributions from both photon and proton beams were calculated. Dose differences and gamma analysis were calculated to quantify the relative changes between doses calculated on the different CT studies. Data from eight patients (all different treatment sites) were also used to quantify the differences between dose distributions before and after correction with IMAR, with no reference standard. A ranking experiment was also conducted to analyze the relative confidence of physicians delineating anatomy in the near vicinity of the metal implants. RESULTS: IMAR corrected images proved to accurately retrieve CT numbers in the phantom study, independent of metal insert configuration, size of the metal, and acquisition energy. For plastic water, the mean difference between corrected images and reference images was -1.3 HU across all scenarios (N = 37) with a 90% confidence interval of [-2.4, -0.2] HU. While deviations were relatively higher in images with more metal content, IMAR was able to effectively correct the CT numbers independent of the quantity of metal. Residual errors in the CT numbers as well as some induced by the correction algorithm were found in the IMAR corrected images. However, the dose distributions calculated on IMAR corrected images were closer to the reference data in phantom studies. Relative spatial difference in the dose distributions in the regions affected by the metal artifacts was also observed in patient data. However, in absence of a reference ground truth (CT set without metal inserts), these differences should not be interpreted as improvement/deterioration of the accuracy of calculated dose. With limited data presented, it was observed that proton dosimetry was affected more than photons as expected. Physicians were significantly more confident contouring anatomy in the regions affected by artifacts. While site specific preferences were detected, all indicated that they would consistently use IMAR corrected images. CONCLUSIONS: IMAR correction algorithm could be readily implemented in an existing clinical workflow upon commercial release. While residual errors still exist in IMAR corrected images, these images present with better overall conspicuity of the patient/phantom geometry and offer more accurate CT numbers for improved local dosimetry. The variety of different scenarios included herein attest to the utility of the evaluated IMAR for a wide range of radiotherapy clinical scenarios.
Authors: Ole Martin; Joel Aissa; Johannes Boos; Katrin Wingendorf; David Latz; Christian Buchbender; Susanne Gaspers; Christina Antke; Martin Sedlmair; Gerald Antoch; Benedikt M Schaarschmidt Journal: Br J Radiol Date: 2019-11-01 Impact factor: 3.039
Authors: Robert W Mutter; J Isabelle Choi; Rachel B Jimenez; Youlia M Kirova; Marcio Fagundes; Bruce G Haffty; Richard A Amos; Julie A Bradley; Peter Y Chen; Xuanfeng Ding; Antoinette M Carr; Leslie M Taylor; Mark Pankuch; Raymond B Mailhot Vega; Alice Y Ho; Petra Witt Nyström; Lisa A McGee; James J Urbanic; Oren Cahlon; John H Maduro; Shannon M MacDonald Journal: Int J Radiat Oncol Biol Phys Date: 2021-05-25 Impact factor: 8.013