BACKGROUND AND PURPOSES: To quantify the cold or hot spot induced in IMRT treatment plans due to the presence of metal artifact in CT image data sets stemming from dental work. PATIENTS AND METHODS: Metal artifact corrected image data sets of five patients have been analyzed. IMRT plans were generated using five different planning image data sets: (a) uncorrected (UC) (b) homogeneous uncorrected (HUC), (c) sinogram completion corrected (SCC), (d) minimum value corrected (MVC), and (e) image set (d) subsequently corrected with a streak artifacts reduction algorithm (SAR-MVC). The SAR-MVC data set is assumed to be the closest approximation to the absence of metal artifacts and has therefore been taken as the reference image data set. An IMRT plan was generated for each of the image datasets (a)-(e). The resulting IMRT treatment plans for data sets (a)-(d) were then projected onto the reference data set (e) and recalculated. The reference dose distribution (e) was then subtracted from these recalculated dose distributions. Using dose difference analysis, the cold and hot spots in organs at risk (OARs) and the target volumes (TVs) were quantified. RESULTS: When compared to the reference dose distribution, the UC, HUC, and SCC plans exhibited hot spots showing on average more than 1.0 Gy hot dose in the left and right parotids. For the UC, HUC, and SCC recalculated plans, subvolumes of the clinical target volumes (CTV) were under dosed on average by more than 0.9 Gy. On the other hand, the MVC plan showed less than 0.3 Gy hot dose in both parotids, and the cold dose in the CTVs were reduced by up to 0.8 Gy. CONCLUSIONS: The presence of dental metal artifacts in head and neck planning CT data sets can lead to relative hot spots in OARs and relative cold spots in regions of the TVs when compared to the reference data set that more closely approximates the patient anatomy. This effect can be reduced if a simple minimum value correction (MVC) method for the dental metal artifacts is employed.
BACKGROUND AND PURPOSES: To quantify the cold or hot spot induced in IMRT treatment plans due to the presence of metal artifact in CT image data sets stemming from dental work. PATIENTS AND METHODS: Metal artifact corrected image data sets of five patients have been analyzed. IMRT plans were generated using five different planning image data sets: (a) uncorrected (UC) (b) homogeneous uncorrected (HUC), (c) sinogram completion corrected (SCC), (d) minimum value corrected (MVC), and (e) image set (d) subsequently corrected with a streak artifacts reduction algorithm (SAR-MVC). The SAR-MVC data set is assumed to be the closest approximation to the absence of metal artifacts and has therefore been taken as the reference image data set. An IMRT plan was generated for each of the image datasets (a)-(e). The resulting IMRT treatment plans for data sets (a)-(d) were then projected onto the reference data set (e) and recalculated. The reference dose distribution (e) was then subtracted from these recalculated dose distributions. Using dose difference analysis, the cold and hot spots in organs at risk (OARs) and the target volumes (TVs) were quantified. RESULTS: When compared to the reference dose distribution, the UC, HUC, and SCC plans exhibited hot spots showing on average more than 1.0 Gy hot dose in the left and right parotids. For the UC, HUC, and SCC recalculated plans, subvolumes of the clinical target volumes (CTV) were under dosed on average by more than 0.9 Gy. On the other hand, the MVC plan showed less than 0.3 Gy hot dose in both parotids, and the cold dose in the CTVs were reduced by up to 0.8 Gy. CONCLUSIONS: The presence of dental metal artifacts in head and neck planning CT data sets can lead to relative hot spots in OARs and relative cold spots in regions of the TVs when compared to the reference data set that more closely approximates the patient anatomy. This effect can be reduced if a simple minimum value correction (MVC) method for the dental metal artifacts is employed.
Authors: Sabah Falek; Rajesh Regmi; Joel Herault; Melanie Dore; Anthony Vela; Pauline Dutheil; Cyril Moignier; Pierre-Yves Marcy; Julien Drouet; Arnaud Beddok; Noah E Letwin; Joel Epstein; Upendra Parvathaneni; Juliette Thariat Journal: Support Care Cancer Date: 2022-05-05 Impact factor: 3.359
Authors: Colin Arrowsmith; Reza Reiazi; Mattea L Welch; Michal Kazmierski; Tirth Patel; Aria Rezaie; Tony Tadic; Scott Bratman; Benjamin Haibe-Kains Journal: Phys Imaging Radiat Oncol Date: 2021-04-21
Authors: David Gergely Kovacs; Laura A Rechner; Ane L Appelt; Anne K Berthelsen; Junia C Costa; Jeppe Friborg; Gitte F Persson; Jens Peter Bangsgaard; Lena Specht; Marianne C Aznar Journal: Radiother Oncol Date: 2017-10-16 Impact factor: 6.280