Advances in magnetic resonance imaging guided radiation therapy_Journal of Biomedical Engineering

Authors：

XU Wenzhe ^1,3 , WANG Changjian ^1,3 , MA Yiming ^1,3 , FANG Chunfeng ² , GONG Hanshun ³ , ZHANG Gaolong ^1,4 , QU Baolin ^3,4 ,  XU Shouping ^3,4

1. School of Physics, Beihang University, Beijing 100191, P. R.China;
2. Department of Radiotherapy, Yizhou Tumor Hospital, Zhuozhou, Hebei 072750, P. R. China;
3. Department of Radiotherapy, PLA General Hospital, Beijing 100853, P. R. China;
4. Beijing Advanced Innovation Center for Big Data-based Precision Medicine, Beihang University, Beijing 100083, P. R. China;

Corresponding author：

Keywords：

magnetic resonance imaging; image-guided radiation therapy; utrecht model; magnetic resonance imaging guided radiation therapy; adaptive radiation therapy

DOI：

10.7507/1001-5515.202002029

Video：

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Image-guided radiation therapy using magnetic resonance imaging (MRI) is a new technology that has been widely studied and developed in recent years. The technology combines the advantages of MRI imaging, and can offer online real-time tracking of tumor and adjacent organs at risk, as well as real-time optimization of radiotherapy plan. In order to provide a comprehensive understanding of this technology, and to grasp the international development and trends in this field, this paper reviews and summarizes related researches, so as to make the researchers and clinical personnel in this field to understand recent status of this technology, and carry out corresponding researches. This paper summarizes the advantages of MRI and the research progress of MRI linear accelerator (MR-Linac), online guidance, adaptive optimization, and dosimetry-related research. Possible development direction of these technologies in the future is also discussed. It is expected that this review can provide a certain reference value for clinician and related researchers to understand the research progress in the field.

Citation： XU Wenzhe, WANG Changjian, MA Yiming, FANG Chunfeng, GONG Hanshun, ZHANG Gaolong, QU Baolin, XU Shouping. Advances in magnetic resonance imaging guided radiation therapy. Journal of Biomedical Engineering, 2021, 38(1): 161-168. doi: 10.7507/1001-5515.202002029 Copy

1.	Pollard J M, Wen Zhifei, Sadagopan R, et al. The future of image-guided radiotherapy will be MR guided. Br J Radiol, 2017, 90(173): 20160667.
2.	Winkel D, Bol G H, Kroon P S, et al. Adaptive radiotherapy: the Elekta Unity MR-linac concept. Clin Transl Radiat Oncol, 2019, 18: 54-59.
3.	Liney G P, Whelan B, Oborn B, et al. MRI-Linear accelerator radiotherapy systems. Clin Oncol, 2018, 30(11): 686-691.
4.	Kerkmeijer L, Maspero M, Meijer G J, et al. Magnetic resonance imaging only workflow for radiotherapy simulation and planning in prostate cancer. Clin Oncol, 2018, 30(11): 692-701.
5.	Mcwilliam A, Rowland B, van Herk M. The challenges of using MRI during radiotherapy. Clin Oncol (R Coll Radiol), 2018, 30(11): 680-685.
6.	Rai R, Kumar S, Batumalai V, et al. The integration of MRI in radiation therapy: collaboration of radiographers and radiation therapists. J Med Radiat Sci, 2017, 64(1): 61-68.
7.	Raaymakers B W, Jürgenliemk-Schulz I M, Bol G H, 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(23): L41-L50.
8.	Liney G P, Jelen U, Byrne H, et al. Technical Note: the first live treatment on a 1.0 Tesla inline MRI-linac. Med Phys, 2019, 46(7): 3254-3258.
9.	Lagendijk J J, Raaymakers B W, van Vulpen M. The magnetic resonance imaging-linac system. Semin Radiat Oncol, 2014, 24(3): 207-209.
10.	Keall P J, Barton M, Crozier S, et al. The Australian magnetic resonance imaging-linac program. Semin Radiat Oncol, 2014, 24(3): 203-206.
11.	Whelan B, Gierman S, Holloway L, et al. A novel electron accelerator for MRI-Linac radiotherapy. Med Phys, 2016, 43(3): 1285-1294.
12.	Begg J, George A, Alnaghy S J, et al. The Australian MRI-Linac program: measuring profiles and PDD in a horizontal beam. J Phys: Conf, 2017, 777: 012035.
13.	Bertelsen A S, Schytte T, Møller P K, et al. First clinical experiences with a high field 1.5 T MR linac. Acta Oncol, 2019, 58(10): 1352-1357.
14.	Wee C W, An H J, Kang H C, et al. Variability of gross tumor volume delineation for stereotactic body radiotherapy of the lung with Tri-⁶⁰Co magnetic resonance image-guided radiotherapy system (ViewRay): a comparative study with magnetic resonance-and computed tomography-based target delineation. Technol Cancer Res T, 2018, 17: 1533033818787383.
15.	应延辰, 陈华, 王昊, 等. ViewRay 磁共振引导放疗系统的研究进展. 中华放射医学与防护杂志, 2019, 39(4): 316-320.
16.	Raaijmakers A E, Raaymakers B W, Lagendijk J W, et al. 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(4): 1363-1376.
17.	Raaijmakers A E, Raaymakers B W, Meer S D, et al. 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(4): 929-939.
18.	Lühr A, Burigo L N, Gantz S, et al. Proton beam electron return effect: Monte Carlo simulations and experimental verification. Phys Med Biol, 2019, 64(3): 035012.
19.	Costa F, Doran S J, Hanson I M, et al. Investigating the effect of a magnetic field on dose distributions at phantom-air interfaces using PRESAGE® 3D dosimeter and Monte Carlo simulations. Phys Med Biol, 2018, 63(5): 05NT01.
20.	Han E Y, Wen Zhifei, Lee H J, et al. Measurement of electron return effect and skin dose reduction by a bolus in an anthropomorphic physical phantom under a magnetic resonance guided linear accelerator (MR-LINAC) system. Int J Med Phy, Clin EngRadiat Oncol, 2018, 7(3): 339-346.
21.	Raaijmakers A E, Hårdemark B, Raaymakers B W, et al. Dose optimization for the MRI-accelerator: IMRT in the presence of a magnetic field. Phys Med Biol, 2007, 52(23): 7045-7054.
22.	Bol G H, Lagendijk J J, Raaymakers B W. Compensating for the impact of non-stationary spherical air cavities on IMRT dose delivery in transverse magnetic fields. Phys Med Biol, 2015, 60(2): 755-768.
23.	Boldrini L, Cusumano D, Cellini F, et al. Online adaptive magnetic resonance guided radiotherapy for pancreatic cancer: state of the art, pearls and pitfalls. Radiat Oncol, 2019, 14(1): 71-76.
24.	Olberg S, Green O, CAI B, et al. Optimization of treatment planning workflow and tumor coverage during daily adaptive magnetic resonance image guided radiation therapy (MR-IGRT) of pancreatic cancer. Radiat Oncol, 2018, 13(1): 51.
25.	Boldrini L, Cellini F, Manfrida S, et al. Use of indirect target gating in magnetic resonance-guided liver stereotactic body radiotherapy: case report of an oligometastatic patient. Cureus, 2018, 10(3): e2292.
26.	Kontaxis C, Bol G H, 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(18): 7233-7248.
27.	Steinmann A, Alvarez P, Lee H, et al. MRIgRT dynamic lung motion thorax anthropomorphic QA phantom: design, development, reproducibility, and feasibility study. Med Phys, 2019, 46(11): 5124-5133.
28.	Ng S P, Koay E J. Current and emerging radiotherapy strategies for pancreatic adenocarcinoma: stereotactic, intensity modulated and particle radiotherapy. Ann Pancreat Cancer, 2018, 1: 22.
29.	Massaccesi M, Cusumano D, Boldrini L, et al. A new frontier of image guidance. Organs at risk avoidance with MRI-guided respiratory-gated intensity modulated radiotherapy. J Appl Clin Med Phys, 2019, 20(6): 194-198.
30.	El-Bared N, Portelance L, Spieler B O, et al. Dosimetric benefits and practical pitfalls of daily online adaptive MRI-guided stereotactic radiation therapy for pancreatic cancer. Pract Radiat Oncol, 2019, 9(1): e46-e54.
31.	Yang R, Santos D M, Fallone B, et al. A novel transport sweep architecture for efficient deterministic patient dose calculations in MRI-guided radiotherapy. Phys Med Biol, 2019, 64: 185012.
32.	Mateo C, Knutsen P M, Tsai P S, et al. Entrainment of arteriole vasomotor fluctuations by neural activity is a basis of blood-oxygenation-level-dependent "resting-state" connectivity. Neuron, 2017, 96(4): 936-948.
33.	Dieterich S, Green O, Booth J. SBRT targets that move with respiration. Phys Med, 2018, 56: 19-24.
34.	Cao Y, Tseng C L, Balter J M, et al. MR-guided radiation therapy: transformative technology and its role in the central nervous system. Neuro Oncol, 2017, 19(suppl_2): ii16-ii29.
35.	Hassanien O A, Abouelkheir R T, El-Ghar E I, et al. Dynamic contrast-enhanced magnetic resonance imaging as a diagnostic tool in the assessment of tumour angiogenesis in urinary bladder cancer. Can Assoc Radiol J, 2019, 70(3): 254-263.
36.	Khan K A, Jain S K, Sinha V D, et al. Preoperative diffusion tensor imaging: a landmark modality for predicting the outcome and characterization of supratentorial intra-axial brain tumors. World Neurosurg, 2019, 124: e540-e551.
37.	Peltenburg B, Schakel T, Terhaard C, et al. Functional diffusion maps to assess treatment response in head and neck tumors. Radiotherapy and Oncology, 2018, 127(suppl 1): S1159-S1160.
38.	Macmanus M, Everitt S, Schimek-Jasch T, et al. Anatomic, functional and molecular imaging in lung cancer precision radiation therapy: treatment response assessment and radiation therapy personalization. Transl Lung Cancer Res, 2017, 6(6): 670-688.
39.	Chin S, Eccles C L, Mcwilliam A, et al. Magnetic resonance-guided radiation therapy: a review. J Med Imaging Radiat Oncol, 2020, 64(1): 163-177.
40.	Noble D J, Burnet N G. The future of image-guided radiotherapy-is image everything?. Br J Radiol, 2018, 91(187): 20170894.
41.	黄伟, LI X A, 李宝生. MRI 引导的自适应放疗技术进展. 中华放射肿瘤学杂志, 2017, 26(7): 819-822.

1. Pollard J M, Wen Zhifei, Sadagopan R, et al. The future of image-guided radiotherapy will be MR guided. Br J Radiol, 2017, 90(173): 20160667.
2. Winkel D, Bol G H, Kroon P S, et al. Adaptive radiotherapy: the Elekta Unity MR-linac concept. Clin Transl Radiat Oncol, 2019, 18: 54-59.
3. Liney G P, Whelan B, Oborn B, et al. MRI-Linear accelerator radiotherapy systems. Clin Oncol, 2018, 30(11): 686-691.
4. Kerkmeijer L, Maspero M, Meijer G J, et al. Magnetic resonance imaging only workflow for radiotherapy simulation and planning in prostate cancer. Clin Oncol, 2018, 30(11): 692-701.
5. Mcwilliam A, Rowland B, van Herk M. The challenges of using MRI during radiotherapy. Clin Oncol (R Coll Radiol), 2018, 30(11): 680-685.
6. Rai R, Kumar S, Batumalai V, et al. The integration of MRI in radiation therapy: collaboration of radiographers and radiation therapists. J Med Radiat Sci, 2017, 64(1): 61-68.
7. Raaymakers B W, Jürgenliemk-Schulz I M, Bol G H, 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(23): L41-L50.
8. Liney G P, Jelen U, Byrne H, et al. Technical Note: the first live treatment on a 1.0 Tesla inline MRI-linac. Med Phys, 2019, 46(7): 3254-3258.
9. Lagendijk J J, Raaymakers B W, van Vulpen M. The magnetic resonance imaging-linac system. Semin Radiat Oncol, 2014, 24(3): 207-209.
10. Keall P J, Barton M, Crozier S, et al. The Australian magnetic resonance imaging-linac program. Semin Radiat Oncol, 2014, 24(3): 203-206.
11. Whelan B, Gierman S, Holloway L, et al. A novel electron accelerator for MRI-Linac radiotherapy. Med Phys, 2016, 43(3): 1285-1294.
12. Begg J, George A, Alnaghy S J, et al. The Australian MRI-Linac program: measuring profiles and PDD in a horizontal beam. J Phys: Conf, 2017, 777: 012035.
13. Bertelsen A S, Schytte T, Møller P K, et al. First clinical experiences with a high field 1.5 T MR linac. Acta Oncol, 2019, 58(10): 1352-1357.
14. Wee C W, An H J, Kang H C, et al. Variability of gross tumor volume delineation for stereotactic body radiotherapy of the lung with Tri-⁶⁰Co magnetic resonance image-guided radiotherapy system (ViewRay): a comparative study with magnetic resonance-and computed tomography-based target delineation. Technol Cancer Res T, 2018, 17: 1533033818787383.
15. 应延辰, 陈华, 王昊, 等. ViewRay 磁共振引导放疗系统的研究进展. 中华放射医学与防护杂志, 2019, 39(4): 316-320.
16. Raaijmakers A E, Raaymakers B W, Lagendijk J W, et al. 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(4): 1363-1376.
17. Raaijmakers A E, Raaymakers B W, Meer S D, et al. 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(4): 929-939.
18. Lühr A, Burigo L N, Gantz S, et al. Proton beam electron return effect: Monte Carlo simulations and experimental verification. Phys Med Biol, 2019, 64(3): 035012.
19. Costa F, Doran S J, Hanson I M, et al. Investigating the effect of a magnetic field on dose distributions at phantom-air interfaces using PRESAGE® 3D dosimeter and Monte Carlo simulations. Phys Med Biol, 2018, 63(5): 05NT01.
20. Han E Y, Wen Zhifei, Lee H J, et al. Measurement of electron return effect and skin dose reduction by a bolus in an anthropomorphic physical phantom under a magnetic resonance guided linear accelerator (MR-LINAC) system. Int J Med Phy, Clin EngRadiat Oncol, 2018, 7(3): 339-346.
21. Raaijmakers A E, Hårdemark B, Raaymakers B W, et al. Dose optimization for the MRI-accelerator: IMRT in the presence of a magnetic field. Phys Med Biol, 2007, 52(23): 7045-7054.
22. Bol G H, Lagendijk J J, Raaymakers B W. Compensating for the impact of non-stationary spherical air cavities on IMRT dose delivery in transverse magnetic fields. Phys Med Biol, 2015, 60(2): 755-768.
23. Boldrini L, Cusumano D, Cellini F, et al. Online adaptive magnetic resonance guided radiotherapy for pancreatic cancer: state of the art, pearls and pitfalls. Radiat Oncol, 2019, 14(1): 71-76.
24. Olberg S, Green O, CAI B, et al. Optimization of treatment planning workflow and tumor coverage during daily adaptive magnetic resonance image guided radiation therapy (MR-IGRT) of pancreatic cancer. Radiat Oncol, 2018, 13(1): 51.
25. Boldrini L, Cellini F, Manfrida S, et al. Use of indirect target gating in magnetic resonance-guided liver stereotactic body radiotherapy: case report of an oligometastatic patient. Cureus, 2018, 10(3): e2292.
26. Kontaxis C, Bol G H, 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(18): 7233-7248.
27. Steinmann A, Alvarez P, Lee H, et al. MRIgRT dynamic lung motion thorax anthropomorphic QA phantom: design, development, reproducibility, and feasibility study. Med Phys, 2019, 46(11): 5124-5133.
28. Ng S P, Koay E J. Current and emerging radiotherapy strategies for pancreatic adenocarcinoma: stereotactic, intensity modulated and particle radiotherapy. Ann Pancreat Cancer, 2018, 1: 22.
29. Massaccesi M, Cusumano D, Boldrini L, et al. A new frontier of image guidance. Organs at risk avoidance with MRI-guided respiratory-gated intensity modulated radiotherapy. J Appl Clin Med Phys, 2019, 20(6): 194-198.
30. El-Bared N, Portelance L, Spieler B O, et al. Dosimetric benefits and practical pitfalls of daily online adaptive MRI-guided stereotactic radiation therapy for pancreatic cancer. Pract Radiat Oncol, 2019, 9(1): e46-e54.
31. Yang R, Santos D M, Fallone B, et al. A novel transport sweep architecture for efficient deterministic patient dose calculations in MRI-guided radiotherapy. Phys Med Biol, 2019, 64: 185012.
32. Mateo C, Knutsen P M, Tsai P S, et al. Entrainment of arteriole vasomotor fluctuations by neural activity is a basis of blood-oxygenation-level-dependent "resting-state" connectivity. Neuron, 2017, 96(4): 936-948.
33. Dieterich S, Green O, Booth J. SBRT targets that move with respiration. Phys Med, 2018, 56: 19-24.
34. Cao Y, Tseng C L, Balter J M, et al. MR-guided radiation therapy: transformative technology and its role in the central nervous system. Neuro Oncol, 2017, 19(suppl_2): ii16-ii29.
35. Hassanien O A, Abouelkheir R T, El-Ghar E I, et al. Dynamic contrast-enhanced magnetic resonance imaging as a diagnostic tool in the assessment of tumour angiogenesis in urinary bladder cancer. Can Assoc Radiol J, 2019, 70(3): 254-263.
36. Khan K A, Jain S K, Sinha V D, et al. Preoperative diffusion tensor imaging: a landmark modality for predicting the outcome and characterization of supratentorial intra-axial brain tumors. World Neurosurg, 2019, 124: e540-e551.
37. Peltenburg B, Schakel T, Terhaard C, et al. Functional diffusion maps to assess treatment response in head and neck tumors. Radiotherapy and Oncology, 2018, 127(suppl 1): S1159-S1160.
38. Macmanus M, Everitt S, Schimek-Jasch T, et al. Anatomic, functional and molecular imaging in lung cancer precision radiation therapy: treatment response assessment and radiation therapy personalization. Transl Lung Cancer Res, 2017, 6(6): 670-688.
39. Chin S, Eccles C L, Mcwilliam A, et al. Magnetic resonance-guided radiation therapy: a review. J Med Imaging Radiat Oncol, 2020, 64(1): 163-177.
40. Noble D J, Burnet N G. The future of image-guided radiotherapy-is image everything?. Br J Radiol, 2018, 91(187): 20170894.
41. 黄伟, LI X A, 李宝生. MRI 引导的自适应放疗技术进展. 中华放射肿瘤学杂志, 2017, 26(7): 819-822.

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