Hayate Washio1, Shingo Ohira2, Yoshinori Funama3, Masahiro Morimoto4, Kentaro Wada5, Masashi Yagi6, Hiroaki Shimamoto7, Yuhei Koike8, Yoshihiro Ueda4, Tsukasa Karino9, Shoki Inui10, Yuya Nitta4, Masayoshi Miyazaki4, Teruki Teshima4. 1. Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan; Graduate School of Health Sciences, Kumamoto University, Kumamoto, Japan. 2. Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan. Electronic address: ueyama-si@mc.pref.osaka.jp. 3. Department of Medical Physics, Faculty of Life Science, Kumamoto University, Kumamoto, Japan. 4. Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan. 5. Department of Radiation Oncology, Sakai City Medical Center, Osaka, Japan. 6. Department of Carbon Ion Radiotherapy, Osaka University Graduate School of Medicine, Suita, Japan. 7. Department of Oral and Maxillofacial Radiology, Osaka University Graduate School of Dentistry, Suita, Japan; Department of Radiation Oncology, Osaka University Graduate School of Medicine, Suita, Japan. 8. Department of Radiology, Kansai Medical University, Osaka, Japan. 9. Department of Radiology, Osaka Women`s and Children`s Hospital, Osaka, Japan. 10. Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan; Department of Medical Physics and Engineering, Osaka University Graduate School of Medicine, Suita, Japan.
Abstract
PURPOSE: To investigate whether a novel iterative cone-beam computed tomography (CBCT) reconstruction algorithm reduces metal artifacts in head and neck patient images. METHOD: An anthropomorphic phantom and 35 patients with dental metal prostheses or implants were analyzed. All CBCT images were acquired using a TrueBeam linear accelerator and reconstructed with a Feldkamp-Davis-Kress algorithm-based CBCT (FDK-CBCT) and an iterative CBCT algorithm. The mean Hounsfield unit (HU) and standard deviation values were measured on the tongue near the metal materials and the unaffected region as reference values. The artifact index (AI) was calculated. For objective image analysis, the HU value and AI were compared between FDK-CBCT and iterative CBCT images in phantom and clinical studies. Subjective image analyses of metal artifact scores and soft tissue visualizations were conducted using a five-point scale by two reviewers in the clinical study. RESULTS: The HU value and AI showed significant artifact reduction for the iterative CBCT than for the FDK-CBCT images (phantom study: 389.8 vs.-10.3 for HU value, 322.9 vs. 96.2 for AI, FDK-CBCT vs. iterative CBCT, respectively; clinical study: 210.3 vs. 69.0 for HU value, 149.6 vs. 70.7 for AI). The subjective scores in the clinical patient study were improved in the iterative CBCT images (metal artifact score: 1.1 vs. 2.9, FDK-CBCT vs. iterative CBCT, respectively; soft tissue visualization: 1.8 vs. 3.6). CONCLUSIONS: The iterative CBCT reconstruction algorithm substantially reduced metal artifacts caused by dental metal prostheses and improved soft tissue visualization compared to FDK-CBCT in phantom and clinical studies.
PURPOSE: To investigate whether a novel iterative cone-beam computed tomography (CBCT) reconstruction algorithm reduces metal artifacts in head and neck patient images. METHOD: An anthropomorphic phantom and 35 patients with dental metal prostheses or implants were analyzed. All CBCT images were acquired using a TrueBeam linear accelerator and reconstructed with a Feldkamp-Davis-Kress algorithm-based CBCT (FDK-CBCT) and an iterative CBCT algorithm. The mean Hounsfield unit (HU) and standard deviation values were measured on the tongue near the metal materials and the unaffected region as reference values. The artifact index (AI) was calculated. For objective image analysis, the HU value and AI were compared between FDK-CBCT and iterative CBCT images in phantom and clinical studies. Subjective image analyses of metal artifact scores and soft tissue visualizations were conducted using a five-point scale by two reviewers in the clinical study. RESULTS: The HU value and AI showed significant artifact reduction for the iterative CBCT than for the FDK-CBCT images (phantom study: 389.8 vs.-10.3 for HU value, 322.9 vs. 96.2 for AI, FDK-CBCT vs. iterative CBCT, respectively; clinical study: 210.3 vs. 69.0 for HU value, 149.6 vs. 70.7 for AI). The subjective scores in the clinical patient study were improved in the iterative CBCT images (metal artifact score: 1.1 vs. 2.9, FDK-CBCT vs. iterative CBCT, respectively; soft tissue visualization: 1.8 vs. 3.6). CONCLUSIONS: The iterative CBCT reconstruction algorithm substantially reduced metal artifacts caused by dental metal prostheses and improved soft tissue visualization compared to FDK-CBCT in phantom and clinical studies.