Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Integrated MRI-guided radiotherapy — opportunities and challenges

Abstract

MRI can help to categorize tissues as malignant or non-malignant both anatomically and functionally, with a high level of spatial and temporal resolution. This non-invasive imaging modality has been integrated with radiotherapy in devices that can differentially target the most aggressive and resistant regions of tumours. The past decade has seen the clinical deployment of treatment devices that combine imaging with targeted irradiation, making the aspiration of integrated MRI-guided radiotherapy (MRIgRT) a reality. The two main clinical drivers for the adoption of MRIgRT are the ability to image anatomical changes that occur before and during treatment in order to adapt the treatment approach, and to image and target the biological features of each tumour. Using motion management and biological targeting, the radiation dose delivered to the tumour can be adjusted during treatment to improve the probability of tumour control, while simultaneously reducing the radiation delivered to non-malignant tissues, thereby reducing the risk of treatment-related toxicities. The benefits of this approach are expected to increase survival and quality of life. In this Review, we describe the current state of MRIgRT, and the opportunities and challenges of this new radiotherapy approach.

Key points

  • Radiotherapy approaches must balance the delivery of a therapeutically effective dose of radiation to target tissues whilst minimizing damage to the surrounding non-malignant tissue; however, anatomy and physiology are dynamic, making it challenging to accurately target tumours during radiotherapy.

  • MRI-guided radiotherapy (MRIgRT) uses MRI during the delivery of radiation, thus enabling more-precise cancer targeting and avoiding irradiating surrounding non-malignant tissues.

  • MRIgRT enables the differential targeting of tumour regions with higher doses of radiation than can be delivered using traditional approaches, providing new options to treat the most aggressive and resistant regions of the tumour.

  • MRI-guided linear accelerator systems, which are MRIgRT devices that integrate an MRI scanner and a linear accelerator, are rapidly being deployed in the clinic, with opportunities for outcome improvements and the challenges of added complexity and cost.

  • Rapid clinical utilization of MRIgRT is currently limited by the need for increased infrastructure, treatment and staffing costs, and the paucity of medium-term to long-term clinical data.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Comparison of MRIgRT and conventional X-ray-guided radiotherapy.
Fig. 2: Types of MRI–linac systems.
Fig. 3: Improving the spatial and temporal resolution of MRI.
Fig. 4: Functional biology-adapted treatment with MRIgRT to overcome therapeutic resistance.
Fig. 5: Future perspectives with MRIgRT.

References

  1. Barton, M. B. et al. Estimating the demand for radiotherapy from the evidence: a review of changes from 2003 to 2012. Radiother. Oncol. 112, 140–144 (2014).

    Article  PubMed  Google Scholar 

  2. Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209–249 (2021).

    Article  PubMed  Google Scholar 

  3. Batumalai, V. et al. Estimating the cost of radiotherapy for 5-year local control and overall survival benefit. Radiother. Oncol. 136, 154–160 (2019).

    Article  PubMed  Google Scholar 

  4. Chen, A. B., Neville, B. A., Sher, D. J., Chen, K. & Schrag, D. Survival outcomes after radiation therapy for stage III non-small-cell lung cancer after adoption of computed tomography-based simulation. J. Clin. Oncol. 29, 2305–2311 (2011).

    Article  PubMed  Google Scholar 

  5. Liao, Z. X. et al. Influence of technologic advances on outcomes in patients with unresectable, locally advanced non-small-cell lung cancer receiving concomitant chemoradiotherapy. Int. J. Radiat. Oncol. Biol. Phys. 76, 775–781 (2010).

    Article  PubMed  Google Scholar 

  6. Ball, D. et al. Stereotactic ablative radiotherapy versus standard radiotherapy in stage 1 non-small-cell lung cancer (TROG 09.02 CHISEL): a phase 3, open-label, randomised controlled trial. Lancet Oncol. 20, 494–503 (2019).

    Article  PubMed  Google Scholar 

  7. de Crevoisier, R. et al. Daily versus weekly prostate cancer image guided radiation therapy: phase 3 multicenter randomized trial. Int. J. Radiat. Oncol. Biol. Phys. 102, 1420–1429 (2018).

    Article  PubMed  Google Scholar 

  8. Mee, T. et al. Variations in demand across England for the magnetic resonance-linac technology, simulated utilising local-level demographic and cancer data in the Malthus project. Clin. Oncol. 33, e285–e294 (2021).

    Article  CAS  Google Scholar 

  9. Corradini, S. et al. ESTRO-ACROP recommendations on the clinical implementation of hybrid MR-linac systems in radiation oncology. Radiother. Oncol. 159, 146–154 (2021).

    Article  PubMed  Google Scholar 

  10. Henke, L. E. et al. Magnetic resonance image-guided radiotherapy (MRIgRT): a 4.5-year clinical experience. Clin. Oncol. 30, 720–727 (2018).

    Article  CAS  Google Scholar 

  11. Rosenberg, S. A. et al. A multi-institutional experience of MR-guided liver stereotactic body radiation therapy. Adv. Radiat. Oncol. 4, 142–149 (2019).

    Article  PubMed  Google Scholar 

  12. Rudra, S. et al. Using adaptive magnetic resonance image-guided radiation therapy for treatment of inoperable pancreatic cancer. Cancer Med. 8, 2123–2132 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Finazzi, T. et al. Clinical outcomes of stereotactic MR-guided adaptive radiation therapy for high-risk lung tumors. Int. J. Radiat. Oncol. Biol. Phys. 107, 270–278 (2020).

    Article  PubMed  Google Scholar 

  14. Baumann, M. et al. Radiation oncology in the era of precision medicine. Nat. Rev. Cancer 16, 234 (2016).

    Article  CAS  PubMed  Google Scholar 

  15. Anastasi, G. et al. Patterns of practice for adaptive and real-time radiation therapy (POP-ART RT) part I: intra-fraction breathing motion management. Radiother. Oncol. 153, 79–87 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Fallone, B. et al. TU-C-M100F-01: development of a linac-MRI system for real-time ART [abstract]. Med. Phys. 34, 2547 (2007).

    Article  Google Scholar 

  17. Dale, B. M., Brown, M. A. & Semelka, R. C. MRI Basic Principles and Applications 103–125 (Wiley, 2015).

  18. Whelan, B., Oborn, B., Liney, G. & Keall, P. in MRI in Radiotherapy: Planning, Delivery, and Response Assessment (eds Gary, L. & van der Heide, U.) 155–168 (Springer, 2019).

  19. Lagendijk, J. J. et al. MRI/linac integration. Radiother. Oncol. 86, 25–29 (2008).

    Article  PubMed  Google Scholar 

  20. Bijman, R. et al. MR-linac radiotherapy–the beam angle selection problem. Front. Oncol. 11, 717681 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Smyth, G., Evans, P. M., Bamber, J. C. & Bedford, J. L. Recent developments in non-coplanar radiotherapy. Br. J. Radiol. 92, 20180908 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Paganelli, C. et al. MRI-guidance for motion management in external beam radiotherapy: current status and future challenges. Phys. Med. Biol. 63, 22tr03 (2018).

    Article  CAS  PubMed  Google Scholar 

  23. Nowee, M. E. et al. The impact of image acquisition time on registration, delineation and image quality for magnetic resonance guided radiotherapy of prostate cancer patients. Phys. Imaging Radiat. Oncol. 19, 85–89 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Piliero, M. A. et al. Patient-based low dose cone beam CT acquisition settings for prostate image-guided radiotherapy treatments on a Varian TrueBeam linear accelerator. Br. J. Radiol. 93, 20200412 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Paulson, E. S. et al. 4D-MRI driven MR-guided online adaptive radiotherapy for abdominal stereotactic body radiation therapy on a high field MR-Linac: implementation and initial clinical experience. Clin. Transl. Radiat. Oncol. 23, 72–79 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Tetar, S. U. et al. Clinical implementation of magnetic resonance imaging guided adaptive radiotherapy for localized prostate cancer. Phys. Imaging Radiat. Oncol. 9, 69–76 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Borman, P. T. S. et al. Characterization of imaging latency for real-time MRI-guided radiotherapy. Phys. Med. Biol. 63, 155023 (2018).

    Article  CAS  PubMed  Google Scholar 

  28. Keall, P. J. et al. AAPM Task Group 264: The safe clinical implementation of MLC tracking in radiotherapy. Med. Phys. 48, e44–e64 (2021).

    Article  PubMed  Google Scholar 

  29. Paganelli, C. et al. Image-based retrospective 4D MRI in external beam radiotherapy: a comparative study with a digital phantom. Med. Phys. 45, 3161–3172 (2018).

    Article  PubMed  Google Scholar 

  30. Gao, Y. et al. Accelerated 3D bSSFP imaging for treatment planning on an MRI-guided radiotherapy system. Med. Phys. 45, 2595–2602 (2018).

    Article  PubMed  Google Scholar 

  31. Feng, L. et al. XD-GRASP: Golden-angle radial MRI with reconstruction of extra motion-state dimensions using compressed sensing. Magn. Reson. Med. 75, 775–788 (2016).

    Article  PubMed  Google Scholar 

  32. Bruijnen, T., Stemkens, B., Lagendijk, J. J. W., van den Berg, C. A. T. & Tijssen, R. H. N. Multiresolution radial MRI to reduce IDLE time in pre-beam imaging on an MR-Linac (MR-RIDDLE). Phys. Med. Biol. 64, 055011 (2019).

    Article  PubMed  Google Scholar 

  33. Shchukina, A., Kasprzak, P., Dass, R., Nowakowski, M. & Kazimierczuk, K. Pitfalls in compressed sensing reconstruction and how to avoid them. J. Biomol. NMR 68, 79–98 (2017).

    Article  CAS  PubMed  Google Scholar 

  34. Chandra, S. S. et al. Deep learning in magnetic resonance image reconstruction. J. Med. Imaging Radiat. Oncol. https://doi.org/10.1111/1754-9485.13276 (2021).

    Article  PubMed  Google Scholar 

  35. Freedman, J. N. et al. Rapid 4D-MRI reconstruction using a deep radial convolutional neural network: Dracula. Radiother. Oncol. 159, 209–217 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Paganelli, C. et al. Time-resolved volumetric MRI in MRI-guided radiotherapy: an in silico comparative analysis. Phys. Med. Biol. 64, 185013 (2019).

    Article  CAS  PubMed  Google Scholar 

  37. Huttinga, N. R. F., van den Berg, C. A. T., Luijten, P. R. & Sbrizzi, A. MR-MOTUS: model-based non-rigid motion estimation for MR-guided radiotherapy using a reference image and minimal k-space data. Phys. Med. Biol. 65, 015004 (2020).

    Article  PubMed  Google Scholar 

  38. Rabe, M. et al. Porcine lung phantom-based validation of estimated 4D-MRI using orthogonal cine imaging for low-field MR-Linacs. Phys. Med. Biol. 66, 055006 (2021).

    Article  CAS  PubMed  Google Scholar 

  39. Wang, G., Ye, J. C. & De Man, B. Deep learning for tomographic image reconstruction. Nat. Mach. Intell. 2, 737–748 (2020).

    Article  Google Scholar 

  40. Zhu, B., Liu, J. Z., Cauley, S. F., Rosen, B. R. & Rosen, M. S. Image reconstruction by domain-transform manifold learning. Nature 555, 487–492 (2018).

    Article  CAS  PubMed  Google Scholar 

  41. Romaguera, L. V. et al. Prediction of in-plane organ deformation during free-breathing radiotherapy via discriminative spatial transformer networks. Med. Image Anal. 64, 101754 (2020).

    Article  PubMed  Google Scholar 

  42. Terpstra, M. L. et al. Deep learning-based image reconstruction and motion estimation from undersampled radial k-space for real-time MRI-guided radiotherapy. Phys. Med. Biol. 65, 155015 (2020).

    Article  PubMed  Google Scholar 

  43. Friedrich, F. et al. Stability of conventional and machine learning-based tumor auto-segmentation techniques using undersampled dynamic radial bSSFP acquisitions on a 0.35 T hybrid MR-linac system. Med. Phys. 48, 587–596 (2021).

    Article  CAS  PubMed  Google Scholar 

  44. Shen, L., Zhao, W. & Xing, L. Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning. Nat. Biomed. Eng. 3, 880–888 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  45. McIntosh, C. et al. Clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer. Nat. Med. 27, 999–1005 (2021).

    Article  CAS  PubMed  Google Scholar 

  46. Borman, P. T. S., Raaymakers, B. W. & Glitzner, M. ReconSocket: a low-latency raw data streaming interface for real-time MRI-guided radiotherapy. Phys. Med. Biol. 64, 185008 (2019).

    Article  CAS  PubMed  Google Scholar 

  47. Xue, H. et al. Automated inline analysis of myocardial perfusion MRI with deep learning. Radiol. Artif. Intell. 2, e200009 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Kurz, C. et al. Medical physics challenges in clinical MR-guided radiotherapy. Radiat. Oncol. 15, 93 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Tijssen, R. H. N. et al. MRI commissioning of 1.5T MR-linac systems–a multi-institutional study. Radiother. Oncol. 132, 114–120 (2019).

    Article  PubMed  Google Scholar 

  50. Kontaxis, C., Woodhead, P. L., Bol, G. H., Lagendijk, J. J. W. & Raaymakers, B. W. Proof-of-concept delivery of intensity modulated arc therapy on the Elekta Unity 1.5 T MR-linac. Phys. Med. Biol. 66, 04lt01 (2021).

    Article  CAS  PubMed  Google Scholar 

  51. Campbell-Washburn, A. E. et al. Opportunities in interventional and diagnostic imaging by using high-performance low-field-strength MRI. Radiology 293, 384–393 (2019).

    Article  PubMed  Google Scholar 

  52. Shan, S. et al. Geometric distortion characterization and correction for the 1.0 T Australian MRI-linac system using an inverse electromagnetic method. Med. Phys. 47, 1126–1138 (2020).

    Article  CAS  PubMed  Google Scholar 

  53. Weygand, J. et al. Spatial precision in magnetic resonance imaging-guided radiation therapy: the role of geometric distortion. Int. J. Radiat. Oncol. Biol. Phys. 95, 1304–1316 (2016).

    Article  PubMed  Google Scholar 

  54. Weiss, S. et al. A novel and rapid approach to estimate patient-specific distortions based on mDIXON MRI. Phys. Med. Biol. 64, 155002 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Bird, D. et al. A systematic review of the clinical implementation of pelvic magnetic resonance imaging-only planning for external beam radiation therapy. Int. J. Radiat. Oncol. Biol. Phys. 105, 479–492 (2019).

    Article  PubMed  Google Scholar 

  56. Datta, A., Aznar, M. C., Dubec, M., Parker, G. J. M. & O’Connor, J. P. B. Delivering functional imaging on the MRI-linac: current challenges and potential solutions. Clin. Oncol. 30, 702–710 (2018).

    Article  CAS  Google Scholar 

  57. Kooreman, E. S. et al. Feasibility and accuracy of quantitative imaging on a 1.5 T MR-linear accelerator. Radiother. Oncol. 133, 156–162 (2019).

    Article  PubMed  Google Scholar 

  58. Nejad-Davarani, S. P. et al. Rapid multicontrast brain imaging on a 0.35T MR-linac. Med. Phys. 47, 4064–4076 (2020).

    Article  PubMed  Google Scholar 

  59. Thorwarth, D. et al. Quantitative magnetic resonance imaging on hybrid magnetic resonance linear accelerators: perspective on technical and clinical validation. Phys. Imaging Radiat. Oncol. 16, 69–73 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Chan, R. W. et al. Chemical exchange saturation transfer MRI in central nervous system tumours on a 1.5 T MR-linac. Radiother. Oncol. 162, 140–149 (2021).

    Article  CAS  PubMed  Google Scholar 

  61. van Houdt, P. J. et al. Integration of quantitative imaging biomarkers in clinical trials for MR-guided radiotherapy: conceptual guidance for multicentre studies from the MR-Linac Consortium Imaging Biomarker Working Group. Eur. J. Cancer 153, 64–71 (2021).

    Article  PubMed  CAS  Google Scholar 

  62. O’Connor, J. P., Robinson, S. P. & Waterton, J. C. Imaging tumour hypoxia with oxygen-enhanced MRI and BOLD MRI. Br. J. Radiol. 92, 20180642 (2019).

    Article  PubMed  Google Scholar 

  63. Halle, C. et al. Hypoxia-induced gene expression in chemoradioresistant cervical cancer revealed by dynamic contrast-enhanced MRI. Cancer Res. 72, 5285–5295 (2012).

    Article  CAS  PubMed  Google Scholar 

  64. Le Bihan, D. What can we see with IVIM MRI? Neuroimage 187, 56–67 (2019).

    Article  PubMed  Google Scholar 

  65. Chen, Z. et al. The performance of intravoxel-incoherent motion diffusion-weighted imaging derived hypoxia for the risk stratification of prostate cancer in peripheral zone. Eur. J. Radiol. 125, 108865 (2020).

    Article  PubMed  Google Scholar 

  66. Kooreman, E. S. et al. Daily intravoxel incoherent motion (IVIM) in prostate cancer patients during MR-guided radiotherapy–a multicenter study. Front. Oncol. 11, 705964 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Bonavia, R., Inda, M. M., Cavenee, W. K. & Furnari, F. B. Heterogeneity maintenance in glioblastoma: a social network. Cancer Res. 71, 4055–4060 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Chédeville, A. L. & Madureira, P. A. The role of hypoxia in glioblastoma radiotherapy resistance. Cancers, https://doi.org/10.3390/cancers13030542 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Castellano, A. et al. Advanced imaging techniques for radiotherapy planning of gliomas. Cancers, https://doi.org/10.3390/cancers13051063 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Kim, M. M. et al. Response assessment during chemoradiation using a hypercellular/hyperperfused imaging phenotype predicts survival in patients with newly diagnosed glioblastoma. Neuro Oncol. 23, 1537–1546 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Yan, D., Vicini, F., Wong, J. & Martinez, A. Adaptive radiation therapy. Phys. Med. Biol. 42, 123–132 (1997).

    Article  CAS  PubMed  Google Scholar 

  72. Glide-Hurst, C. K. et al. Adaptive radiation therapy (ART) strategies and technical considerations: a state of the ART review from NRG Oncology. Int. J. Radiat. Oncol. Biol. Phys. 109, 1054–1075 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Dawson, L. A., Eccles, C. & Craig, T. Individualized image guided iso-NTCP based liver cancer SBRT. Acta Oncol. 45, 856–864 (2006).

    Article  PubMed  Google Scholar 

  74. Liu, M. et al. Individual isotoxic radiation dose escalation based on V20 and advanced technologies benefits unresectable stage III non-small cell lung cancer patients treated with concurrent chemoradiotherapy: long term follow-up. Oncotarget 8, 51848–51858 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Vargas, C. et al. Phase II dose escalation study of image-guided adaptive radiotherapy for prostate cancer: use of dose-volume constraints to achieve rectal isotoxicity. Int. J. Radiat. Oncol. Biol. Phys. 63, 141–149 (2005).

    Article  PubMed  Google Scholar 

  76. Ahunbay, E. E., Peng, C., Godley, A., Schultz, C. & Li, X. A. An on-line replanning method for head and neck adaptive radiotherapy. Med. Phys. 36, 4776–4790 (2009).

    Article  PubMed  Google Scholar 

  77. Liu, F., Ahunbay, E., Lawton, C. & Li, X. A. Assessment and management of interfractional variations in daily diagnostic-quality-CT guided prostate-bed irradiation after prostatectomy. Med. Phys. 41, 031710 (2014).

    Article  PubMed  Google Scholar 

  78. El-Bared, N. et al. Dosimetric benefits and practical pitfalls of daily online adaptive MRI-guided stereotactic radiation therapy for pancreatic cancer. Pract. Radiat. Oncol. 9, e46–e54 (2019).

    Article  PubMed  Google Scholar 

  79. Henke, L. et al. Phase I trial of stereotactic MR-guided online adaptive radiation therapy (SMART) for the treatment of oligometastatic or unresectable primary malignancies of the abdomen. Radiother. Oncol. 126, 519–526 (2018).

    Article  PubMed  Google Scholar 

  80. Bruynzeel, A. M. E. et al. A prospective single-arm phase 2 study of stereotactic magnetic resonance guided adaptive radiation therapy for prostate cancer: early toxicity results. Int. J. Radiat. Oncol. Biol. Phys. 105, 1086–1094 (2019).

    Article  CAS  PubMed  Google Scholar 

  81. Dearnaley, D. et al. Conventional versus hypofractionated high-dose intensity-modulated radiotherapy for prostate cancer: 5-year outcomes of the randomised, non-inferiority, phase 3 CHHiP trial. Lancet Oncol. 17, 1047–1060 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Aluwini, S. et al. Hypofractionated versus conventionally fractionated radiotherapy for patients with prostate cancer (HYPRO): acute toxicity results from a randomised non-inferiority phase 3 trial. Lancet Oncol. 16, 274–283 (2015).

    Article  PubMed  Google Scholar 

  83. Archambault, Y. et al. Making on-line adaptive radiotherapy possible using artificial intelligence and machine learning for efficient daily re-planning. Med. Phys. Int. J. 8, 77–86 (2020).

    Google Scholar 

  84. Keall, P. et al. Real-time image guided ablative prostate cancer radiation therapy: results from the TROG 15.01 SPARK trial. Int. J. Radiat. Oncol. Biol. Phys. 107, 530–538 (2020).

    Article  PubMed  Google Scholar 

  85. Caillet, V. et al. MLC tracking for lung SABR reduces planning target volumes and dose to organs at risk. Radiother. Oncol. 124, 18–24 (2017).

    Article  PubMed  Google Scholar 

  86. Gargett, M., Haddad, C., Kneebone, A., Booth, J. T. & Hardcastle, N. Clinical impact of removing respiratory motion during liver SABR. Radiat. Oncol. 14, 93 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Sandler, H. M. et al. Reduction in patient-reported acute morbidity in prostate cancer patients treated with 81-Gy intensity-modulated radiotherapy using reduced planning target volume margins and electromagnetic tracking: assessing the impact of margin reduction study. Urology 75, 1004–1008 (2010).

    Article  PubMed  Google Scholar 

  88. Colvill, E. et al. Multileaf collimator tracking improves dose delivery for prostate cancer radiation therapy: results of the first clinical trial. Int. J. Radiat. Oncol. Biol. Phys. 92, 1141–1147 (2015).

    Article  PubMed  Google Scholar 

  89. Lovelock, D. M., Messineo, A. P., Cox, B. W., Kollmeier, M. A. & Zelefsky, M. J. Continuous monitoring and intrafraction target position correction during treatment improves target coverage for patients undergoing SBRT prostate therapy. Int. J. Radiat. Oncol. Biol. Phys. 91, 588–594 (2015).

    Article  PubMed  Google Scholar 

  90. Shimizu, S. et al. Use of an implanted marker and real-time tracking of the marker for the positioning of prostate and bladder cancers. Int. J. Radiat. Oncol. Biol. Phys. 48, 1591–1597 (2000).

    Article  CAS  PubMed  Google Scholar 

  91. King, C. R. et al. Stereotactic body radiotherapy for localized prostate cancer: interim results of a prospective phase II clinical trial. Int. J. Radiat. Oncol. Biol. Phys. 73, 1043–1048 (2009).

    Article  PubMed  Google Scholar 

  92. Kupelian, P. et al. Multi-institutional clinical experience with the Calypso system in localization and continuous, real-time monitoring of the prostate gland during external radiotherapy. Int. J. Radiat. Oncol. Biol. Phys. 67, 1088–1098 (2007).

    Article  PubMed  Google Scholar 

  93. O’Shea, T. et al. Review of ultrasound image guidance in external beam radiotherapy part II: intra-fraction motion management and novel applications. Phys. Med. Biol. 61, R90 (2016).

    Article  PubMed  CAS  Google Scholar 

  94. Keall, P. J., Barton, M. & Crozier, S. The Australian magnetic resonance imaging-linac program. Semin. Radiat. Oncol. 24, 203–206 (2014).

    Article  PubMed  Google Scholar 

  95. Mutic, S. & Dempsey, J. F. The ViewRay system: magnetic resonance-guided and controlled radiotherapy. Semin. Radiat. Oncol. 24, 196–199 (2014).

    Article  PubMed  Google Scholar 

  96. Raaymakers, B. W. et al. Integrating a 1.5 T MRI scanner with a 6 MV accelerator: proof of concept. Phys. Med. Biol. 54, N229–N237 (2009).

    Article  CAS  PubMed  Google Scholar 

  97. Fallone, B. G. The rotating biplanar linac-magnetic resonance imaging system. Semin. Radiat. Oncol. 24, 200–202 (2014).

    Article  PubMed  Google Scholar 

  98. Al-Ward, S. M. et al. The development of a 4D treatment planning methodology to simulate the tracking of central lung tumors in an MRI-linac. J. Appl. Clin. Med. Phys. 19, 145–155 (2018).

    Article  PubMed  Google Scholar 

  99. Placidi, L. et al. Quantitative analysis of MRI-guided radiotherapy treatment process time for tumor real-time gating efficiency. J. Appl. Clin. Med. Phys. 21, 70–79 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  100. van Sörnsen de Koste, J. R. et al. MR-guided gated stereotactic radiation therapy delivery for lung, adrenal, and pancreatic tumors: a geometric analysis. Int. J. Radiat. Oncol. Biol. Phys. 102, 858–866 (2018).

    Article  PubMed  Google Scholar 

  101. Glitzner, M., Woodhead, P. L., Borman, P. T. S., Lagendijk, J. J. W. & Raaymakers, B. W. Technical note: MLC-tracking performance on the Elekta unity MRI-linac. Phys. Med. Biol. 64, 15nt02 (2019).

    Article  CAS  PubMed  Google Scholar 

  102. Ge, Y., O’Brien, R. T., Shieh, C. C., Booth, J. T. & Keall, P. J. Toward the development of intrafraction tumor deformation tracking using a dynamic multi-leaf collimator. Med. Phys. 41, 061703 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  103. Liu, P. Z. Y. et al. First experimental investigation of simultaneously tracking two independently moving targets on an MRI-linac using real-time MRI and MLC tracking. Med. Phys. 47, 6440–6449 (2020).

    Article  PubMed  Google Scholar 

  104. Elekta. 100th Elekta Unity MR-Linac goes to St George’s Hospital in New Zealand. https://ir.elekta.com/investors/press-releases/2021/100th-elekta-unity-mr-linac-goes-to-st-georges-hospital-in-new-zealand/ (2021).

  105. de Mol van Otterloo, S. R. et al. The MOMENTUM study: an international registry for the evidence-based introduction of MR-guided adaptive therapy. Front Oncol. 10, 1328 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  106. ViewRay. 10,000th patient receives treatment with ViewRay’s MRIdian system. https://www.prnewswire.com/news-releases/10-000th-patient-receives-treatment-with-viewrays-mridian-system-301121514.html (2020).

  107. Atun, R. et al. Expanding global access to radiotherapy. Lancet Oncol. 16, 1153–1186 (2015).

    Article  PubMed  Google Scholar 

  108. McRobbie, D. W. Essentials of MRI Safety (Wiley-Blackwell, 2020).

  109. Glide-Hurst, C. K. et al. Task Group 284 report: magnetic resonance imaging simulation in radiotherapy: considerations for clinical implementation, optimization, and quality assurance. Med. Phys. https://doi.org/10.1002/mp.14695 (2021).

    Article  PubMed  Google Scholar 

  110. Speight, R. et al. IPEM topical report: An international IPEM survey of MRI use for external beam radiotherapy treatment planning. Phys. Med. Biol. https://doi.org/10.1088/1361-6560/abe9f7 (2021).

    Article  PubMed  Google Scholar 

  111. Gach, H. M. et al. Implementation of magnetic resonance safety program for radiation oncology. Pract. Radiat. Oncol. 12, e49–e55 (2022).

    Article  PubMed  Google Scholar 

  112. Kanal, E. et al. ACR guidance document on MR safe practices: 2013. J. Magn. Reson. Imaging 37, 501–530 (2013).

    Article  PubMed  Google Scholar 

  113. Parikh, N. R. et al. Time-driven activity-based costing comparison of CT-guided versus MR-guided SBRT. JCO Oncol. Pract. 16, e1378–e1385 (2020).

    Article  PubMed  Google Scholar 

  114. Schumacher, L. D., Dal Pra, A., Hoffe, S. E. & Mellon, E. A. Toxicity reduction required for MRI-guided radiotherapy to be cost-effective in the treatment of localized prostate cancer. Br. J. Radiol. 93, 20200028 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  115. Tree, A. C., Huddart, R. & Choudhury, A. Magnetic resonance-guided radiotherapy–can we justify more expensive technology? Clin. Oncol. 30, 677–679 (2018).

    Article  CAS  Google Scholar 

  116. Dunlop, A. et al. Daily adaptive radiotherapy for patients with prostate cancer using a high field MR-linac: initial clinical experiences and assessment of delivered doses compared to a C-arm linac. Clin. Transl. Radiat. Oncol. 23, 35–42 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  117. Winkel, D. et al. Target coverage and dose criteria based evaluation of the first clinical 1.5T MR-linac SBRT treatments of lymph node oligometastases compared with conventional CBCT-linac treatment. Radiother. Oncol. 146, 118–125 (2020).

    Article  CAS  PubMed  Google Scholar 

  118. van Dams, R. et al. Ablative radiotherapy for liver tumors using stereotactic MRI-guidance: a prospective phase I trial. Radiother. Oncol. https://doi.org/10.1016/j.radonc.2021.06.005 (2021).

    Article  PubMed  Google Scholar 

  119. Parikh, P., Low, D., Green, O. L. & Lee, P. P. Stereotactic MR-guided on-table adaptive radiation therapy (SMART) for locally advanced pancreatic cancer [abstract]. J. Clin. Oncol. 38 (Suppl. 4), TPS786 (2020).

    Article  Google Scholar 

  120. Henke, L. E. et al. Stereotactic MR-guided online adaptive radiation therapy (SMART) for ultracentral thorax malignancies: results of a phase 1 trial. Adv. Radiat. Oncol. 4, 201–209 (2019).

    Article  PubMed  Google Scholar 

  121. Finazzi, T. et al. Delivery of magnetic resonance-guided single-fraction stereotactic lung radiotherapy. Phys. Imaging Radiat. Oncol. 14, 17–23 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  122. Brand, D. H. et al. Intensity-modulated fractionated radiotherapy versus stereotactic body radiotherapy for prostate cancer (PACE-B): acute toxicity findings from an international, randomised, open-label, phase 3, non-inferiority trial. Lancet Oncol. 20, 1531–1543 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  123. Widmark, A. et al. Ultra-hypofractionated versus conventionally fractionated radiotherapy for prostate cancer: 5-year outcomes of the HYPO-RT-PC randomised, non-inferiority, phase 3 trial. Lancet 394, 385–395 (2019).

    Article  PubMed  Google Scholar 

  124. de Mol van Otterloo, S. R. et al. Patterns of care, tolerability, and safety of the first cohort of patients treated on a novel high-field MR-linac within the MOMENTUM study: initial results from a prospective multi-institutional registry. Int. J. Radiat. Oncol. Biol. Phys. 111, 867–875 (2021).

    Article  PubMed  Google Scholar 

  125. Bohoudi, O. et al. Fast and robust online adaptive planning in stereotactic MR-guided adaptive radiation therapy (SMART) for pancreatic cancer. Radiother. Oncol. 125, 439–444 (2017).

    Article  CAS  PubMed  Google Scholar 

  126. Kontaxis, C., Bol, G. H., Lagendijk, J. J. & Raaymakers, B. W. A new methodology for inter- and intrafraction plan adaptation for the MR-linac. Phys. Med. Biol. 60, 7485–7497 (2015).

    Article  PubMed  Google Scholar 

  127. Kontaxis, C., Bol, G. H., Lagendijk, J. J. W. & Raaymakers, B. W. DeepDose: towards a fast dose calculation engine for radiation therapy using deep learning. Phys. Med. Biol. 65, 075013 (2020).

    Article  CAS  PubMed  Google Scholar 

  128. Mohajer, J. et al. Feasibility of MR-guided ultrahypofractionated radiotherapy in 5, 2 or 1 fractions for prostate cancer. Clin. Transl. Radiat. Oncol. 26, 1–7 (2021).

    Article  PubMed  Google Scholar 

  129. Kooreman, E. S. et al. ADC measurements on the Unity MR-linac–a recommendation on behalf of the Elekta Unity MR-linac consortium. Radiother. Oncol. 153, 106–113 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  130. Pang, Y., Royle, G. & Manolopoulos, S. Functional imaging for dose painting in radiotherapy. Preprint at arxiv https://arxiv.org/abs/2011.11531 (2020).

  131. Thorwarth, D. Biologically adapted radiation therapy. Z. fur Medizinische Phys. 28, 177–183 (2018).

    Article  Google Scholar 

  132. Kupelian, P. & Sonke, J. J. Magnetic resonance-guided adaptive radiotherapy: a solution to the future. Semin. Radiat. Oncol. 24, 227–232 (2014).

    Article  PubMed  Google Scholar 

  133. Hoffmann, A. et al. MR-guided proton therapy: a review and a preview. Radiat. Oncol. 15, 129 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  134. Oborn, B. M. et al. Future of medical physics: real-time MRI-guided proton therapy. Med. Phys. 44, e77–e90 (2017).

    Article  CAS  PubMed  Google Scholar 

  135. Gantz, S., Hietschold, V. & Hoffmann, A. L. Characterization of magnetic interference and image artefacts during simultaneous in-beam MR imaging and proton pencil beam scanning. Phys. Med. Biol. 65, 215014 (2020).

    Article  CAS  PubMed  Google Scholar 

  136. Schellhammer, S. M. et al. Integrating a low-field open MR scanner with a static proton research beam line: proof of concept. Phys. Med. Biol. 63, 23lt01 (2018).

    Article  CAS  PubMed  Google Scholar 

  137. Durante, M. & Loeffler, J. S. Charged particles in radiation oncology. Nat. Rev. Clin. Oncol. 7, 37–43 (2010).

    Article  PubMed  Google Scholar 

  138. Takahashi, Y. et al. Heavy ion irradiation inhibits in vitro angiogenesis even at sublethal dose. Cancer Res. 63, 4253–4257 (2003).

    CAS  PubMed  Google Scholar 

  139. Akino, Y. et al. Carbon-ion beam irradiation effectively suppresses migration and invasion of human non-small-cell lung cancer cells. Int. J. Radiat. Oncol. Biol. Phys. 75, 475–481 (2009).

    Article  CAS  PubMed  Google Scholar 

  140. Buizza, G. et al. Improving the characterization of meningioma microstructure in proton therapy from conventional apparent diffusion coefficient measurements using Monte Carlo simulations of diffusion MRI. Med. Phys. 48, 1250–1261 (2021).

    Article  PubMed  Google Scholar 

  141. Barton, M. B., Pham, T. T. & Harris, G. in MRI for Radiotherapy: Planning, Delivery, and Response Assessment (eds Liney, G. P. & van der Heide, U.) 191–201 (Springer, 2019).

  142. Pham, T. T., Liney, G. P., Wong, K. & Barton, M. B. Functional MRI for quantitative treatment response prediction in locally advanced rectal cancer. Br. J. Radiol. 90, 20151078 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  143. Bruynzeel, A. M. E. & Lagerwaard, F. J. The role of biological dose-escalation for pancreatic cancer. Clin. Transl. Radiat. Oncol. 18, 128–130 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  144. Vaupel, P. Tumor microenvironmental physiology and its implications for radiation oncology. Semin. Radiat. Oncol. 14, 198–206 (2004).

    Article  PubMed  Google Scholar 

  145. Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

    Article  CAS  PubMed  Google Scholar 

  146. Shieh, C. C. et al. SPARE: sparse-view reconstruction challenge for 4D cone-beam CT from a 1-min scan. Med. Phys. 46, 3799–3811 (2019).

    Article  PubMed  Google Scholar 

  147. Padilla, L., Havnen-Smith, A., Cerviño, L. & Al-Hallaq, H. A. A survey of surface imaging use in radiation oncology in the United States. J. Appl. Clin. Med. Phys. 20, 70–77 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

P.J.K. acknowledges funding from the Australian Government National Health and Medical Research Council (NHMRC) Investigator (1194004) and Program (1132471) grant schemes. P.Z.Y.L. receives support from the Cancer Institute of New South Wales (NSW) (fellowship ECF/1032). A.C.T. acknowledges research funding from Cancer Research UK (grants C7224/A28724 and C33589/A28284). D.E.J.W. acknowledges support from the Cancer Institute of NSW (fellowship ECF/1015). B.W. acknowledges support from the NHMRC Early Career Fellowship scheme (1163010). A.C.T.’s involvement is supported by the UK National Institute for Health Research (NIHR) Biomedical Research Centre at The Royal Marsden NHS Foundation Trust and the Institute of Cancer Research, London. The views expressed are those of the authors and not necessarily those of the NIHR or the UK Department of Health and Social Care (A.C.T.). All authors thank H. Ball and J. Johnson (both at the University of Sydney) for help with manuscript and artwork preparation, respectively.

Author information

Authors and Affiliations

Authors

Contributions

All authors made a substantial contribution to the discussion of content, wrote the manuscript, and reviewed and/or edited the manuscript before submission.

Corresponding author

Correspondence to Paul J. Keall.

Ethics declarations

Competing interests

P.J.K. is an inventor on two patents relating to MRI–linac systems: US#8,331,531 and US#9,099,271. A.C.T. receives research funding from Accuray, Elekta and Varian, and honoraria and travel assistance from Elekta. U.A.v.d.H. receives research funding from Elekta and Philips Healthcare. The other authors declare no competing interests.

Peer review

Peer review information

Nature Reviews Clinical Oncology thanks K. Kirkby, J. Lagendijk, D. Low and R. Orecchia for their contribution to the peer review of this work.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Clinical Trials.gov: https://clinicaltrials.gov/

Glossary

MRI

Widely used medical imaging procedure that measures magnetic properties of tissue. Diseased tissues, such as cancer, often have different magnetic properties compared with surrounding healthy tissues, enabling them to be precisely located. The location of the diseased tissue within the body is used for targeting disease with radiation beams during radiotherapy.

MRI-guided radiotherapy

(MRIgRT). Process of exploiting the soft-tissue imaging capacity and flexibility of MRI for targeting areas of pathology, predominantly cancer, with radiation. Although MRI scanners have had a long standing role in radiation therapy for pretreatment imaging of cancer and to measure treatment response, in the context of this Review, MRIgRT refers to the rapidly growing application of MRI guidance whilst a patient is receiving radiotherapy. This functionality is enabled by use of an MRI-guided linear accelerator.

MRI-guided linear accelerator

(MRI–linac). MRIgRT device that integrates an MRI scanner and linear accelerator. Most standard (non-MRI) linear accelerators used for cancer treatment have X-ray imaging systems to combine imaging with the delivery of radiation.

Linacs

Portmanteau term for linear accelerator, a device that accelerates charged particles to create X-rays and deliver radiotherapy.

Gantry

The part of the linear accelerator that the radiation beam is mounted on, which rotates 360 degrees. This rotation allows many possible angles of the radiation beam to enter the patient. Typically, 5–11 beam angles are used in each patient treatment.

Tumour tracking

Imaging the tumour in real time to enable improved radiation beam tumour targeting by either gating the radiation beam or having the radiation beam continuously follow the moving tumour by adjusting the multi-leaf collimator beam shaping device.

Beam gating

Turning the radiation beam on when the tumour moves within a predefined treatment boundary and turning the beam off when the tumour moves outside the treatment boundary. Beam gating protects non-malignant tissues from irradiation if a target moves from its expected treatment position.

Organs-at-risk

(OARs). Non-malignant structures within the human body located sufficiently close to a tumour to be at risk of damage from the radiation delivered during radiotherapy.

Geometric fidelity

The degree of exactness with which structures within reconstructed images replicate the size, shape and location of patient anatomy. MRIgRT demands a high level of geometric fidelity to ensure that radiation beams are accurately targeted.

Collimator

The part of the linear accelerator that shapes the radiation beam to conform to the tumour target.

Phantoms

Physical devices used to assist measurements of image quality, dose delivery and other scientific investigations for MRIgRT.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Keall, P.J., Brighi, C., Glide-Hurst, C. et al. Integrated MRI-guided radiotherapy — opportunities and challenges. Nat Rev Clin Oncol 19, 458–470 (2022). https://doi.org/10.1038/s41571-022-00631-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41571-022-00631-3

This article is cited by

Search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer