OBJECTIVE: One approach to improving image quality of CT is to use metal artefact reduction image processing, such as single-energy metal artefact reduction (SEMAR). To quantify the impact of image correction on the quality of carbon-ion dose distribution, treatment planning using SEMAR was evaluated. METHODS: Using a head phantom into which metal screws could be inserted, we acquired standard planning CT images. We calculated dose distributions using phantom images with and without metal added, and with and without SEMAR. Hounsfield unit (HU) and dose distribution variation of these images with and without SEMAR were measured using metal-free image subtraction. We similarly analysed the image data sets of two patients with head and neck cancer who had dental implants. RESULTS: HU difference between metal-containing images and metal-free images without and with SEMAR were -79.5 ± 97.2 HU and -1.4 ± 19.5 HU on severe artefact area, respectively. The range of dose distribution difference from the prescribed dose between uncorrected and SEMAR-corrected images varied from -19.5% to -3.4% within planning target volume (PTV). PTV-D95 (%) for uncorrected and SEMAR-corrected image data were 82.4% and 95.4%, respectively. For data in patients with metal dental work, PTV-D95 (%) for uncorrected and SEMAR-corrected data were 92.2% and 92.5% (Patient 1), and 90.9% and 95.7% (Patient 2), respectively. CONCLUSION: SEMAR algorithm shows promise in improving CT image quality and in ensuring an accurate representation of dose distribution. ADVANCES IN KNOWLEDGE: SEMAR may improve treatment accuracy without the need for dental implant extraction in patients with head and neck cancer.
OBJECTIVE: One approach to improving image quality of CT is to use metal artefact reduction image processing, such as single-energy metal artefact reduction (SEMAR). To quantify the impact of image correction on the quality of carbon-ion dose distribution, treatment planning using SEMAR was evaluated. METHODS: Using a head phantom into which metal screws could be inserted, we acquired standard planning CT images. We calculated dose distributions using phantom images with and without metal added, and with and without SEMAR. Hounsfield unit (HU) and dose distribution variation of these images with and without SEMAR were measured using metal-free image subtraction. We similarly analysed the image data sets of two patients with head and neck cancer who had dental implants. RESULTS: HU difference between metal-containing images and metal-free images without and with SEMAR were -79.5 ± 97.2 HU and -1.4 ± 19.5 HU on severe artefact area, respectively. The range of dose distribution difference from the prescribed dose between uncorrected and SEMAR-corrected images varied from -19.5% to -3.4% within planning target volume (PTV). PTV-D95 (%) for uncorrected and SEMAR-corrected image data were 82.4% and 95.4%, respectively. For data in patients with metal dental work, PTV-D95 (%) for uncorrected and SEMAR-corrected data were 92.2% and 92.5% (Patient 1), and 90.9% and 95.7% (Patient 2), respectively. CONCLUSION: SEMAR algorithm shows promise in improving CT image quality and in ensuring an accurate representation of dose distribution. ADVANCES IN KNOWLEDGE: SEMAR may improve treatment accuracy without the need for dental implant extraction in patients with head and neck cancer.
Authors: Fabian Bamberg; Alexander Dierks; Konstantin Nikolaou; Maximilian F Reiser; Christoph R Becker; Thorsten R C Johnson Journal: Eur Radiol Date: 2011-01-20 Impact factor: 5.315