Lagendijk JJ, Raaymakers BW, Raaijmakers AJ, et al. MRI/linac integration. Radiother Oncol. 2008;86(1):25–9.

Article  PubMed  Google Scholar 

Winkel D, Bol GH, Kroon PS, et al. Adaptive radiotherapy: the elekta unity MR-linac concept. Clin Transl Radiat Oncol. 2019;18:54–9.

PubMed  PubMed Central  Google Scholar 

Sonke JJ, Aznar M, Rasch C. Adaptive radiotherapy for anatomical changes. Semin Radiat Oncol. 2019;29(3):245–57.

Article  PubMed  Google Scholar 

Ezzell GA, Burmeister JW, Dogan N, et al. IMRT commissioning: multiple institution planning and dosimetry comparisons, a report from AAPM task group 119. Med Phys. 2009;36(11):5359–73.

Article  PubMed  Google Scholar 

Miften M, Olch A, Mihailidis D, et al. Tolerance limits and methodologies for IMRT measurement-based verification QA: recommendations of AAPM task group no. 218. Med Phys. 2018;45(4):e53–83.

Article  PubMed  Google Scholar 

Raaijmakers AJ, Raaymakers BW, Lagendijk JJ. 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(7):1363–76.

Article  CAS  PubMed  Google Scholar 

Nachbar M, Mönnich D, Boeke S, et al. Partial breast irradiation with the 1.5 T MR-linac: first patient treatment and analysis of electron return and stream effects. Radiother Oncol. 2020;145:30–5.

Article  PubMed  Google Scholar 

Chen GP, Ahunbay E, Li XA. Technical note: development and performance of a software tool for quality assurance of online replanning with a conventional Linac or MR-Linac. Med Phys. 2016;43(4):1713.

Article  PubMed  Google Scholar 

Graves SA, Snyder JE, Boczkowski A, et al. Commissioning and performance evaluation of RadCalc for the elekta unity MRI-linac. J Appl Clin Med Phys. 2019;20(12):54–62.

Article  PubMed  PubMed Central  Google Scholar 

Li Y, Wang B, Ding S, et al. Feasibility of using a commercial collapsed cone dose engine for 1.5T MR-LINAC online independent dose verification. Phys Med. 2020;80:288–96.

Article  PubMed  Google Scholar 

Ahmad SB, Sarfehnia A, Paudel MR, et al. Evaluation of a commercial MRI Linac based monte carlo dose calculation algorithm with GEANT4. Med Phys. 2016;43(2):894–907.

Article  PubMed  Google Scholar 

Friedel M, Nachbar M, Mönnich D, Dohm O, Thorwarth D. Development and validation of a 1.5 T MR-linac full accelerator head and cryostat model for monte carlo dose simulations. Med Phys. 2019;46(11):5304–13.

Article  CAS  PubMed  Google Scholar 

Li Y, Ding S, Wang B, Liu H, Huang X, Song T. Extension and validation of a GPU-monte carlo dose engine gDPM for 1.5 T MR-LINAC online independent dose verification. Med Phys. 2021;48(10):6174–83.

Article  PubMed  Google Scholar 

Cheng B, Xu Y, Li S, et al. Development and clinical application of a GPU-based monte carlo dose verification module and software for 1.5 T MR-LINAC. Med Phys. 2023;50(5):3172–83.

Article  PubMed  Google Scholar 

Interian Y, Rideout V, Kearney VP, et al. Deep nets vs expert designed features in medical physics: an IMRT QA case study. Med Phys. 2018;45(6):2672–80.

Article  PubMed  Google Scholar 

Tomori S, Kadoya N, Takayama Y, et al. A deep learning-based prediction model for gamma evaluation in patient-specific quality assurance. Med Phys. 2018. https://doi.org/10.1002/mp.13112.

Article  PubMed  Google Scholar 

Tomori S, Kadoya N, Kajikawa T, et al. Systematic method for a deep learning-based prediction model for gamma evaluation in patient-specific quality assurance of volumetric modulated arc therapy. Med Phys. 2021;48(3):1003–18.

Article  PubMed  Google Scholar 

Matsuura T, Kawahara D, Saito A, Yamada K, Ozawa S, Nagata Y. A synthesized gamma distribution-based patient-specific VMAT QA using a generative adversarial network. Med Phys. 2023;50(4):2488–98.

Article  PubMed  Google Scholar 

Hao Y, Zhang X, Wang J, Zhao T, Sun B. Improvement of IMRT QA prediction using imaging-based neural architecture search. Med Phys. 2022;49(8):5236–43.

Article  CAS  PubMed  Google Scholar 

Snyder JE, St-Aubin J, Yaddanapudi S, et al. Commissioning of a 1.5T elekta unity MR-linac: a single institution experience. J Appl Clin Med Phys. 2020;21(7):160–72.

Article  PubMed  PubMed Central  Google Scholar 

Tozuka R, Kadoya N, Tomori S, et al. Improvement of deep learning prediction model in patient-specific QA for VMAT with MLC leaf position map and patient’s dose distribution. J Appl Clin Med Phys. 2023;24(10): e14055.

Article  PubMed  PubMed Central  Google Scholar 

Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 2014:arXiv:1409.1556. https://doi.org/10.48550/arXiv.1409.1556. https://ui.adsabs.harvard.edu/abs/2014arXiv1409.1556S. Accessed 1 Sept 2014.

Quintero P, Benoit D, Cheng Y, Moore C, Beavis A. Machine learning-based predictions of gamma passing rates for virtual specific-plan verification based on modulation maps, monitor unit profiles, and composite dose images. Phys Med Biol. 2022. https://doi.org/10.1088/1361-6560/aca38a.

Article  PubMed  Google Scholar 

Reynolds M, Fallone BG, Rathee S. Dose response of selected solid state detectors in applied homogeneous transverse and longitudinal magnetic fields. Med Phys. 2014;41(9): 092103.

Article  CAS  PubMed  Google Scholar 

Ellefson ST, Culberson WS, Bednarz BP, DeWerd LA, Bayouth JE. An analysis of the ArcCHECK-MR diode array’s performance for ViewRay quality assurance. J Appl Clin Med Phys. 2017;18(4):161–71.

Article  PubMed  PubMed Central  Google Scholar 

Huang Y, Pi Y, Ma K, et al. Virtual patient-specific quality assurance of IMRT Using UNet++: classification, gamma passing rates prediction, and dose difference prediction. Front Oncol. 2021;11: 700343.

Article  PubMed  PubMed Central  Google Scholar 

Nelms BE, Zhen H, Tomé WA. Per-beam, planar IMRT QA passing rates do not predict clinically relevant patient dose errors. Med Phys. 2011;38(2):1037–44.

Article  PubMed  PubMed Central  Google Scholar 

Fredh A, Scherman JB, Fog LS, Munck af Rosenschöld P. Patient QA systems for rotational radiation therapy: a comparative experimental study with intentional errors. Med Phys. 2013;40(3):031716.

Article  PubMed  Google Scholar 

Tsekas G, Bol GH, Raaymakers BW, Kontaxis C. DeepDose: a robust deep learning-based dose engine for abdominal tumours in a 1.5 T MRI radiotherapy system. Phys Med Biol. 2021;66(6):065017.

Article  CAS  PubMed  Google Scholar 

Tsekas G, Bol GH, Raaymakers BW. Robust deep learning-based forward dose calculations for VMAT on the 1.5T MR-linac. Phys Med Biol. 2022;67(22):225020.

Article  CAS  Google Scholar 

Delfs B, Schoenfeld AA, Poppinga D, et al. Magnetic fields are causing small, but significant changes of the radiochromic EBT3 film response to 6 MV photons. Phys Med Biol. 2018;63(3): 035028.

Article  PubMed  Google Scholar 

Steinmann A, O’Brien D, Stafford R, et al. Investigation of TLD and EBT3 performance under the presence of 1.5T, 0.35T, and 0T magnetic field strengths in MR/CT visible materials. Med Phys. 2019;46(7):3217–26.

Article  CAS  PubMed  PubMed Central  Google Scholar 

de Vries JHW, Seravalli E, Houweling AC, et al. Characterization of a prototype MR-compatible Delta4 QA system in a 1.5 tesla MR-linac. Phys Med Biol. 2018;63(2):02NT02.

Article  PubMed  Google Scholar 

Barten DLJ, Hoffmans D, Palacios MA, Heukelom S, van Battum LJ. Suitability of EBT3 GafChromic film for quality assurance in MR-guided radiotherapy at 0.35 T with and without real-time MR imaging. Phys Med Biol. 2018;63(16):165014.

Article  PubMed  Google Scholar 

Desai V, Bayouth J, Smilowitz J, Yadav P. A clinical validation of the MR-compatible delta. J Appl Clin Med Phys. 2021;22(4):82–91.

Article  PubMed  PubMed Central  Google Scholar