Characterizing local dose perturbations due to gas cavities in magnetic resonance-guided radiotherapy.
IGRT
MR-guided radiotherapy
MRgRT
cancer
dose
image-guided radiotherapy
radiotherapy
Journal
Medical physics
ISSN: 2473-4209
Titre abrégé: Med Phys
Pays: United States
ID NLM: 0425746
Informations de publication
Date de publication:
Jun 2020
Jun 2020
Historique:
received:
09
10
2019
revised:
27
02
2020
accepted:
27
02
2020
pubmed:
8
3
2020
medline:
15
5
2021
entrez:
8
3
2020
Statut:
ppublish
Résumé
Due to differences in attenuation and the electron return effect (ERE), the presence of gas can increase the risk of toxicity in organs at risk (OAR) during magnetic resonance-guided radiotherapy (MRgRT). Current adaptive MRgRT workflows using density overrides negate gas from the dose calculation, meaning that the effects of ERE around gas are not taken into account. In order to achieve an accurate adaptive MRgRT treatment, we should be able to quickly evaluate whether gas present during treatment causes dose constraint violation during an MRgRT fraction. We propose an analytic method for predicting dose perturbations caused by air cavities in OARs during MRgRT. Ten virtual water phantoms were created: nine containing a centrally located spherical air cavity and a reference phantom without an air cavity. Monte Carlo dose calculations were produced to irradiate the phantoms with a single 7 MV photon beam under the influence of a 1.5 T transverse magnetic field (Monaco 5.19.02 Treatment Panning System (TPS) (Elekta AB, Stockholm, Sweden)). Dose distributions of the phantoms with and without air cavities were compared. We used a spherical coordinate system originating in the center of the cavity to sample the dose distributions and calculate the dose perturbation as a result of the presence of each air cavity, ∆D%(θ,Φ) Both ERE and differences in attenuation contribute toward the total dose effects of air cavities in MRgRT. Whereas ERE dominates close to the surface of the cavities, attenuation effects dominate at distances >0.5 cm from the cavities. We showed that dose effects around a spherical air cavity (≤1 cm from the surface) due to ERE fit a modulated sinusoidal function with mean (RE) ≤-1.4E-5% and root mean square error (rms) (RE) ≤4.1%. Effects due to attenuation differences fit a Gaussian function with mean (RE) ≤0.7% and rms (RE) ≤1.8%. Our general equation, which we verified using multiple sizes of spherical and cylindrical air cavity, fits Monte Carlo simulated data with mean (RE) ≤±0.9% and rms (RE) ≤6.9%. We show that local dose perturbations around unplanned spherical air cavities during MRgRT can be well characterized analytically. We present an equation that can be incorporated into the clinical workflow to allow for fast evaluation of dose effects of unplanned gas. We also envision this method contributing to the clinical implementation of real time adaptive radiotherapy (ART) for MRgRT using MRI planning.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2484-2494Subventions
Organisme : European Association of National Metrology Institutes
Organisme : European Metrology Programme for Innovation and Research
ID : R120635
Organisme : Cancer Research UK
ID : A21993
Pays : United Kingdom
Informations de copyright
© 2020 American Association of Physicists in Medicine.
Références
Raaymakers BW, Jürgenliemk-Schulz IM, Bol GH, et al. First patients treated with a 1.5 T MRI-Linac: clinical proof of concept of a high-precision, high-field MRI guided radiotherapy treatment. Phys Med Biol. 2017;62:L41-L50.
Acharya S, Fischer-Valuck BW, Kashani R, et al. Online magnetic resonance image guided adaptive radiation therapy: first clinical applications. Int J Radiat Oncol Biol Phys. 2016;94:394-403.
Cree A, Livsey J, Barraclough L, et al. The potential value of MRI in external-beam radiotherapy for cervical cancer. Clin Oncol. 2018;30:737-750.
Boldrini L, Cusumano D, Cellini F, Azario L, Mattiucci GC, Valentini V. Online adaptive magnetic resonance guided radiotherapy for pancreatic cancer: state of the art, pearls and pitfalls. Radiat Oncol. 2019;14:71.
van Vulpen M, van den Berg CAT, Moman MR, van der Heide UA. Difficulties and potential of correlating local recurrences in prostate cancer with the delivered local dose. Radiother Oncol. 2009;93:180-184.
Corradini S, Alongi F, Andratschke N, et al. MR-guidance in clinical reality: current treatment challenges and future perspectives. Radiat. Oncol. 2019;14, 92. https://doi.org/10.1186/s13014-019-1308-y.
White IM, Scurr E, Wetscherek A, et al. Realizing the potential of magnetic resonance image guided radiotherapy in gynaecological and rectal cancer. Br J Radiol. 2019;92:20180670.
Hunt A, Hansen VN, Oelfke U, Nill S, Hafeez S. Adaptive radiotherapy enabled by MRI Guidance. Clin Oncol. 2018;30:711-719.
Ceng C, Ahunbay E, Chen GGTY, Anderson S, Lawton C, Li XA. Characterising interfraction variations and their dosimetric effects in prostate cancer radiotherapy. Int J Radiat Oncol Biol Phys. 2011;79:909-914.
Winkel D, Bol GH, Kroon PS, et al. Adaptive radiotherapy: the Elekta Unity MR-linac concept. Clin Transl Radiat Oncol. 2019;18:54-59.
Hsu SH, Zawisza I, O'Grady K, Peng Q, Tomé WA. Towards abdominal MRI-based treatment planning using population-based Hounsfield units for bulk density assignment. Phys Med Biol. 2018;63:155003.
Hoogcarspel SJ, Van der Velden JM, Lagendijk JJWW, van Vulpen M, Raaymakers BW. The feasibility of utilizing pseudo CT-data for online MRI based treatment plan adaptation for a stereotactic radiotherapy treatment of spinal bone metastases. Phys Med Biol. 2014;59:7383-7391.
Jonsson JH, Karlsson MGM, Karlsson MGM, Nyholm T. Treatment planning using MRI data: an analysis of the dose calculation accuracy for different treatment regions. Radiat Oncol. 2010;5:62.
Chuter RW, Pollitt A, Whitehurst P, MacKay RI, van Herk M, McWilliam A. Assessing MR-linac radiotherapy robustness for anatomical changes in head and neck cancer. Phys Med Biol. 2018;63:125020.
Shortall J, Vasquez Osorio E, Van Herk M. EP-1810: assessing the dose significance of unplanned rectal filling in pelvic MR Guided Radiotherapy. Radiother Oncol. 2018;127:S973-S974.
Chen X, Prior P, Chen G-P, Schultz CJ, Li XA. Technical Note: Dose effects of 1.5 T transverse magnetic field on tissue interfaces in MRI-guided radiotherapy. Med Phys. 2016;43:4797-4802.
Raaijmakers AJE, Raaymakers BW, Van Der Meer S, Lagendijk JJW. Integrating a MRI scanner with a 6 MV radiotherapy accelerator: impact of the surface orientation on the entrance and exit dose due to the transverse magnetic field. Phys Med Biol. 2007;52:929-939.
Shortall J, Vasquez Osorio E, Chuter R. et al. Assessing localised dosimetric effects due to unplanned gas cavities during pelvic MR-guided Radiotherapy using Monte Carlo simulations. Med Phys. 2019;46:5807-5815.
Paudel MR, Kim A, Sarfehnia A, et al. Experimental evaluation of a GPU-based Monte Carlo dose calculation algorithm in the Monaco treatment planning system. J Appl Clin Med Phys. 2016;17:230-241
Uilkema S, van der Heide U, Sonke J-J, Moreau M, van Triest B, Nijkamp J. A 1.5 T transverse magnetic field in radiotherapy of rectal cancer: Impact on the dose distribution. Med Phys. 2015;42:7182.
Raaijmakers AJE, Raaymakers BW, Lagendijk JJW. Magnetic-field-induced dose effects in MR-guided radiotherapy systems: dependence on the magnetic field strength. Phys Med Biol. 2008;53:909-923.
Raaijmakers AJE, Raaymakers BW, Lagendijk JJW. Integrating a MRI scanner with a 6 MV radiotherapy accelerator: dose increase at tissue-air interfaces in a lateral magnetic field due to returning electrons. Phys Med Biol. 2005;50:1363-1376.
Kontaxis C, Bol GH, Lagendijk JJW, Raaymakers BW. A new methodology for inter- and intrafraction plan adaptation for the MR-linac. Phys Med Biol. 2015;60:7485-7497.
Kontaxis C, Bol GH, Stemkens B, et al. Towards fast online intrafraction replanning for free-breathing stereotactic body radiation therapy with the MR-linac. Phys Med Biol. 2017;62:7233-7248.
Seif F, Bayatiani MR, Hamidi S, Kargaran M. Investigating the effect of air cavities of sinuses on the radiotherapy dose distribution using Monte Carlo Method. J Biomed Phys Eng. 2019;9:121-126.
Behrens CF. Dose build-up behind air cavities for Co-60, 4, 6 and 8 MV. Measurements and Monte Carlo simulations. Phys Med Biol. 2006;51:5937-5950.
Meijer GJ, Van Den Brink M, Hoogeman MS, Meinders J, Lebesque JV. Dose-wall histograms and normalized dose-surface histograms for the rectum: a new method to analyze the dose distribution over the rectum in conformal radiotherapy. Int J Radiat Oncol Biol Phys. 1999;45:1073-1080.
Jadon R, Pembroke CA, Hanna CL, et al. A systematic review of organ motion and image-guided strategies in external beam radiotherapy for cervical cancer. Clin Oncol. 2014;26:185-196.