Skip to main content

Role of MRI in Radiation Oncology

  • Chapter
  • First Online:
A Practical Guide to MR-Linac
  • 132 Accesses

Abstract

Magnetic resonance (MR) images are used frequently in the diagnosis of disease and also in radiation oncology. In many disease sites, MR has unequivocal superiority over other imaging modalities. This chapter deals with the evolution of MR in radiation oncology. Due to superior soft tissue contrast, MR can visualize tumors more easily than other methods and can help reduce the treatment margin; thus, it may reduce radiation toxicity. MR-Linac is a combination device with a linear accelerator and an MRI unit on a single gantry. Since its inception in 2010, MR-Linac is increasingly used in radiation oncology and is entering the mainstream. This chapter provides a rationale for the use of MR imaging and more so for MR-Linac in radiation oncology and the associated pros and cons.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. ACS. American Cancer Society. Cancer facts and figures 2023. https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/annual-cancer-facts-and-figures/2023/2023-cancer-facts-and-figures.pdf. 2023.

  2. Byhardt RW, Cox JD, Hornburg LG. Weekly localization films and detection of field placement errors. Int J Radiat Oncol Biol Phys. 1978;4:881–7.

    Article  CAS  PubMed  Google Scholar 

  3. Yan D, Vicini F, Wong J, Martinez A. Adaptive radiation therapy. Phys Med Biol. 1997;42:123–32.

    Article  CAS  PubMed  Google Scholar 

  4. Yan D, Lockman D, Brabbins D, Tyburski L, et al. An off-line strategy for constructing a patient-specific planning target volume in adaptive treatment process for prostate cancer. Int J Radiat Oncol Biol Phys. 2000;48:289–302.

    Article  CAS  PubMed  Google Scholar 

  5. Jaffray DA. Image-guided radiotherapy: from current concept to future perspectives. Nat Rev Clin Oncol. 2012;9:688–99.

    Article  CAS  PubMed  Google Scholar 

  6. Lattanzi J, McNeeley S, Pinover W, Horwitz E, et al. A comparison of daily CT localization to a daily ultrasound-based system in prostate cancer. Int J Radiat Oncol Biol Phys. 1999;43:719–25.

    Article  CAS  PubMed  Google Scholar 

  7. Lattanzi J, McNeely S, Hanlon A, Das I, et al. Daily CT localization for correcting portal errors in the treatment of prostate cancer. Int J Radiat Oncol Biol Phys. 1998;41:1079–86.

    Article  CAS  PubMed  Google Scholar 

  8. Wong JR, Grimm L, Uematsu M, Oren R, et al. Image-guided radiotherapy for prostate cancer by CT-linear accelerator combination: prostate movements and dosimetric considerations. Int J Radiat Oncol Biol Phys. 2005;61:561–9.

    Article  PubMed  Google Scholar 

  9. Wong JR, Gao Z, Uematsu M, Merrick S, et al. Interfractional prostate shifts: review of 1870 computed tomography (CT) scans obtained during image-guided radiotherapy using CT-on-rails for the treatment of prostate cancer. Int J Radiat Oncol Biol Phys. 2008;72:1396–401.

    Article  PubMed  Google Scholar 

  10. Gayou O, Miften M. Commissioning and clinical implementation of a mega-voltage cone beam CT system for treatment localization. Med Phys. 2007;34:3183–92.

    Article  PubMed  Google Scholar 

  11. Gayou O, Miften M. Comparison of mega-voltage cone-beam computed tomography prostate localization with online ultrasound and fiducial markers methods. Med Phys. 2007;35:531–8.

    Article  Google Scholar 

  12. Nielsen M, Bertelsen A, Westberg J, Jensen HR, et al. Cone beam CT evaluation of patient set-up accuracy as a QA tool. Acta Oncol. 2009;48:271–6.

    Article  PubMed  Google Scholar 

  13. Das IJ, Cheng CW, Cao M, Johnstone PAS. CT imaging parameters for inhomogeneity correction in radiation treatment planning. J Med Phys. 2016;41:1–11.

    Article  Google Scholar 

  14. Cusumano D, Lenkowicz J, Votta C, Boldrini L, et al. A deep learning approach to generate synthetic CT in low field MR-guided adaptive radiotherapy for abdominal and pelvic cases. Radiother Oncol. 2020;153:205–12.

    Article  CAS  PubMed  Google Scholar 

  15. Cusumano D, Placidi L, Teodoli S, Boldrini L, et al. On the accuracy of bulk synthetic CT for MR-guided online adaptive radiotherapy. Radiol Med. 2020;125:157–64.

    Article  PubMed  Google Scholar 

  16. Farjam R, Tyagi N, Deasy JO, Hunt MA. Dosimetric evaluation of an atlas-based synthetic CT generation approach for MR-only radiotherapy of pelvis anatomy. J Appl Clin Med Phys. 2019;20:101–9.

    Article  PubMed  Google Scholar 

  17. Han X. MR-based synthetic CT generation using a deep convolutional neural network method. Med Phys. 2017;44:1408–19.

    Article  CAS  PubMed  Google Scholar 

  18. Kim J, Garbarino K, Schultz L, Levin K, et al. Dosimetric evaluation of synthetic CT relative to bulk density assignment-based magnetic resonance-only approaches for prostate radiotherapy. Radiat Oncol. 2015;10:239.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Lei Y, Harms J, Wang T, Tian S, et al. MRI-based synthetic CT generation using semantic random forest with iterative refinement. Phys Med Biol. 2019;64:085001.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Tyagi N, Fontenla S, Zhang J, Cloutier M, et al. Dosimetric and workflow evaluation of first commercial synthetic CT software for clinical use in pelvis. Phys Med Biol. 2017;62:2961–75.

    Article  PubMed  Google Scholar 

  21. Spadea MF, Maspero M, Zaffino P, Seco J. Deep learning based synthetic-CT generation in radiotherapy and PET: a review. Med Phys. 2021;48:6537–66.

    Article  PubMed  Google Scholar 

  22. Spadea MF, Pileggi G, Zaffino P, Salome P, et al. Deep convolution neural network (DCNN) multiplane approach to synthetic CT generation from MR images-application in brain proton therapy. Int J Radiat Oncol Biol Phys. 2019;105:495–503.

    Article  CAS  PubMed  Google Scholar 

  23. Tang B, Wu F, Fu Y, Wang X, et al. Dosimetric evaluation of synthetic CT image generated using a neural network for MR-only brain radiotherapy. J Appl Clin Med Phys. 2021;22:55–62.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Wang H, Chandarana H, Block KT, Vahle T, et al. Dosimetric evaluation of synthetic CT for magnetic resonance-only based radiotherapy planning of lung cancer. Radiat Oncol. 2017;12:108.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Wang H, Du K, Qu J, Chandarana H, et al. Dosimetric evaluation of magnetic resonance generated synthetic CT for radiation treatment of rectal cancer. PLoS One. 2018;13:e019088.

    Google Scholar 

  26. ICRU Report 50. Prescribing, recording, and reporting photon beam therapy. Bethesda, MD: International Commission on Radiation Units and Measurements; 1993.

    Google Scholar 

  27. ICRU Report 62. Prescribing, recording, and reporting photon beam therapy (supplement to ICRU report 50). International Commission on Radiation Units and Measurements: Bethesda, MD; 1999.

    Google Scholar 

  28. ICRU Report 97. MRI-guided radiation therapy using MRI-linear accelerators. Bethesda, MD: International Commission on Radiation Units and Measurements; 2022.

    Google Scholar 

  29. de Mol van Otterloo SR, Christodouleas JP, Blezer ELA, Akhiat H, et al. The MOMENTUM study: an international registry for the evidence-based introduction of MR-guided adaptive therapy. Front Oncol. 2020;10:1328.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Christiansen RL, Dysager L, Hansen CR, Jensen HR, et al. Online adaptive radiotherapy potentially reduces toxicity for high-risk prostate cancer treatment. Radiother Oncol. 2022;167:165–71.

    Article  CAS  PubMed  Google Scholar 

  31. Kishan AU, Ma TM, Lamb JM, Casado M, et al. Magnetic resonance imaging-guided vs computed tomography-guided stereotactic body radiotherapy for prostate cancer: the MIRAGE randomized clinical trial. JAMA Oncol. 2023;9:365–73.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Alongi F, Rigo M, Figlia V, Nicosia L, et al. 1.5T MR-guided daily-adaptive SBRT for prostate cancer: preliminary report of toxicity and quality of life of the first 100 patients. J Pers Med. 2022;12:1982.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Nierer L, Eze C, da Silva MV, Braun J, et al. Dosimetric benefit of MR-guided online adaptive radiotherapy in different tumor entities: liver, lung, abdominal lymph nodes, pancreas and prostate. Radiat Oncol. 2022;17:53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Michalet M, Bettaïeb O, Khalfi S, Ghorbel A, et al. Stereotactic MR-guided radiotherapy for adrenal gland metastases: first clinical results. J Clin Med. 2022;12:291.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Chuong MD, Bryant J, Mittauer KE, Hall M, et al. Ablative 5-fraction stereotactic magnetic resonance-guided radiation therapy with on-table adaptive replanning and elective nodal irradiation for inoperable pancreas cancer. Pract Radiat Oncol. 2021;11:134–47.

    Article  PubMed  Google Scholar 

  36. Chuong MD, Herrera R, Kaiser A, Rubens M, et al. Induction chemotherapy and ablative stereotactic magnetic resonance image-guided adaptive radiation therapy for inoperable pancreas cancer. Front Oncol. 2022;12:888462.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Hounsfield GN. Computerized transverse axial scanning (tomography): part I. Description of system. Br J Radiol. 1973;46:1016–22.

    Article  CAS  PubMed  Google Scholar 

  38. Hounsfield GN. Nobel Award address. Computed medical imaging. Med Phys. 1980;7:283–90.

    Article  CAS  PubMed  Google Scholar 

  39. Das IJ, McGee KP, Desobrey GE. The digitally reconstructed radiograph. In: Coia LR, Schultheiss TE, Hanks GE, editors. A practical guide to CT simulation. Madison, WI: Advanced Medical Publishing; 1995. p. 39–50.

    Google Scholar 

  40. Coia LR, Schultheiss TE, Hanks GE. A practical guide to CT simulation. Madison, WI: Advanced Medical Publishing; 1995.

    Google Scholar 

  41. Damadian R. Tumor detection by nuclear magnetic resonance. Science. 1971;171:1151–5.

    Article  CAS  PubMed  Google Scholar 

  42. Bitar R, Leung G, Perng R, Tadros S, et al. MR pulse sequences: what every radiologist wants to know but is afraid to ask. Radiographics. 2006;26:513–37.

    Article  PubMed  Google Scholar 

  43. Das IJ, Sagreiya H, Yadav P, Allen BD. Basics of MR imaging for the radiation oncologist. In: Ozyar E, Onal C, Hackett SL, editors. MR Linac radiotherapy, a new personalized treatment approach. London: Academic Press; 2022. p. 5–32.

    Google Scholar 

  44. Jacobs MA, Ibrahim TS, Ouwerkerk R. AAPM/RSNA physics tutorials for residents: MR imaging: brief overview and emerging applications. Radiographics. 2007;27:1213–29.

    Article  PubMed  Google Scholar 

  45. Srinivasan S, Dasgupta A, Chatterjee A, Baheti A, et al. The promise of magnetic resonance imaging in radiation oncology practice in the management of brain, prostate, and GI malignancies. JCO Glob Oncol. 2022;8:e2100366.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Baissalov R, Sandison GA, Donnelly BJ, Saliken JC, et al. Suppression of high-density artefacts in x-ray CT images using temporal digital subtraction with application to cryotherapy. Phys Med Biol. 2000;45:N53–9.

    Article  CAS  PubMed  Google Scholar 

  47. Wei J, Sandison GA, Hsi WC, Ringor M, et al. Dosimetric impact of a CT metal artifact suppression algorithm for proton, electron and photon therapies. Phys Med Biol. 2006;51:5183–97.

    Article  PubMed  Google Scholar 

  48. Lewis M, Reid K, Toms AP. Reducing the effects of metal artefact using high keV monoenergetic reconstruction of dual energy CT (DECT) in hip replacements. Skelet Radiol. 2013;42:275–82.

    Article  Google Scholar 

  49. Lell MM, Meyer E, Kuefner MA, May MS, et al. Normalized metal artifact reduction in head and neck computed tomography. Investig Radiol. 2012;47:415–21.

    Article  Google Scholar 

  50. Andersson KM, Nowik P, Persliden J, Thunberg P, et al. Metal artefact reduction in CT imaging of hip prostheses—an evaluation of commercial techniques provided by four vendors. Br J Radiol. 2015;88:20140473.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Ojala J, Kapanen M, Sipila P, Hyodynmaa S, et al. The accuracy of Acuros XB algorithm for radiation beams traversing a metallic hip implant - comparison with measurements and Monte Carlo calculations. J Appl Clin Med Phys. 2014;15:162–76.

    Article  PubMed Central  Google Scholar 

  52. Higashigaito K, Angst F, Runge VM, Alkadhi H, et al. Metal artifact reduction in pelvic computed tomography with hip prostheses: comparison of virtual monoenergetic extrapolations from dual-energy computed tomography and an iterative metal artifact reduction algorithm in a phantom study. Investig Radiol. 2015;50:828–34.

    Article  Google Scholar 

  53. Mullins JP, Grams MP, Herman MG, Brinkmann DH, et al. Treatment planning for metals using an extended CT number scale. J Appl Clin Med Phys. 2016;17:179–88.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Carrasco P, Jornet N, Duch MA, Panettieri V, et al. Comparison of dose calculation algorithms in slab phantoms with cortical bone equivalent heterogeneities. Med Phys. 2007;34:3323–33.

    Article  CAS  PubMed  Google Scholar 

  55. Yadav P, Chang SX, Cheng CW, DesRosiers CM, et al. Dosimetric evaluation of high-Z inhomogeneity used for hip prosthesis: a multi-institutional collaborative study. Phys Med. 2022;95:148–55.

    Article  PubMed  Google Scholar 

  56. Das IJ, McGee KP, Tyagi N, Wang H. Role and future of MRI in radiation oncology. Br J Radiol. 2019;92:20180505.

    Article  PubMed  Google Scholar 

  57. Moore-Palhares D, Ho L, Lu L, Chugh B, et al. Clinical implementation of magnetic resonance imaging simulation for radiation oncology planning: 5 year experience. Radiat Oncol. 2023;18:27.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Otazo R, Lambin P, Pignol JP, Ladd ME, et al. MRI-guided radiation therapy: an emerging paradigm in adaptive radiation oncology. Radiology. 2021;298:248–60.

    Article  PubMed  Google Scholar 

  59. Tenhunen M, Korhonen J, Kapanen M, Seppala T, et al. MRI-only based radiation therapy of prostate cancer: workflow and early clinical experience. Acta Oncol. 2018;28:1–6.

    Google Scholar 

  60. Pollard JM, Wen Z, Sadagopan R, Wang J, et al. The future of image-guided radiotherapy will be MR guided. Br J Radiol. 2017;90:20160667.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Chandarana H, Wang H, Tijssen RHN, Das IJ. Emerging role of MRI in radiation therapy. J Magn Reson Imaging. 2018;48:1468–78.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Nousiainen K, Santurio GV, Lundahl N, Cronholm R, et al. Evaluation of MRI-only based online adaptive radiotherapy of abdominal region on MR-linac. J Appl Clin Med Phys. 2023;24:e13838.

    Article  PubMed  Google Scholar 

  63. Ramsey CR, Oliver AL. Magnetic resonance imaging based digitally reconstructed radiographs, virtual simulation, and three-dimensional treatment planning for brain neoplasms. Med Phys. 1998;25:1928–34.

    Article  CAS  PubMed  Google Scholar 

  64. Ramsey CR, Arwood D, Scaperoth D, Oliver AL. Clinical application of digitally-reconstructed radiographs generated from magnetic resonance imaging for intracranial lesions. Int J Radiat Oncol Biol Phys. 1999;45:797–802.

    Article  CAS  PubMed  Google Scholar 

  65. Bayouth JE, Low DA, Zaidi H. MRI-linac systems will replace conventional IGRT systems within 15 years. Med Phys. 2019;46:3753–6.

    Article  PubMed  Google Scholar 

  66. Kramer S, Kusner D, Gunn WG. Clinical experience with the Jefferson Hospital radiotherapy simulator. Radiology. 1966;87:134–6.

    Article  CAS  PubMed  Google Scholar 

  67. Mutic S, Palta JR, Butker EK, Das IJ, et al. Quality assurance for computed-tomography simulators and the computed-tomography-simulation process: report of the AAPM Radiation Therapy Committee Task Group No. 66. Med Phys. 2003;30:2762–92.

    Article  PubMed  Google Scholar 

  68. Mah D, Steckner M, Palacio E, Mitra R, et al. Characteristics and quality assurance of a dedicated open 0.23 T MRI for radiation therapy simulation. Med Phys. 2002;29:2541–7.

    Article  PubMed  Google Scholar 

  69. Kapanen M, Collan J, Beule A, Seppälä T, et al. Commissioning of MRI-only based treatment planning procedure for external beam radiotherapy of prostate. Magn Reson Med. 2013;70:127–35.

    Article  PubMed  Google Scholar 

  70. Glide-Hurst CK, Paulson ES, McGee K, Tyagi N, et al. Task group 284 report: magnetic resonance imaging simulation in radiotherapy: considerations for clinical implementation, optimization, and quality assurance. Med Phys. 2021;48:e636–70.

    Article  PubMed  Google Scholar 

  71. Glide-Hurst CK, Wen N, Hearshen D, Kim J, et al. Initial clinical experience with a radiation oncology dedicated open 1.0T MR-simulation. J Appl Clin Med Phys. 2015;16:5201.

    Article  PubMed  Google Scholar 

  72. Tyagi N, Fontenla S, Zelefsky M, Chong-Ton M, et al. Clinical workflow for MR-only simulation and planning in prostate. Radiat Oncol. 2017;12:119.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Tyagi N, Zelefsky MJ, Wibmer A, Zakian K, et al. Clinical experience and workflow challenges with magnetic resonance-only radiation therapy simulation and planning for prostate cancer. Phys Imaging Radiat Oncol. 2020;16:43–9.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Cao M, Padgett KR, Rong Y. Are in-house diagnostic MR physicists necessary for clinical implementation of MRI guided radiotherapy? J Appl Clin Med Phys. 2017;18:6–9.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Kagawa K, Lee WR, Schultheiss TE, Hunt MA, et al. Initial clinical assessment of CT-MRI image fusion software in localization of the prostate for 3D conformal radiation therapy. Int J Radiat Oncol Biol Phys. 1997;38:319–25.

    Article  CAS  PubMed  Google Scholar 

  76. Roach M 3rd, Faillace-Akazawa P, Malfatti C, Holland J, et al. Prostate volumes defined by magnetic resonance imaging and computerized tomographic scans for three-dimensional conformal radiotherapy. Int J Radiat Oncol Biol Phys. 1996;35:1011–8.

    Article  PubMed  Google Scholar 

  77. Guo L, Shen S, Harris E, Wang Z, et al. A tri-modality image fusion method for target delineation of brain tumors in radiotherapy. PLoS One. 2014;9:e112187.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Basson L, Jarraya H, Escande A, Cordoba A, et al. Chest magnetic resonance imaging decreases inter-observer variability of gross target volume for lung tumors. Front Oncol. 2019;9:690.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Batumalai V, Burke S, Roach D, Lim K, et al. Impact of dosimetric differences between CT and MRI derived target volumes for external beam cervical cancer radiotherapy. Br J Radiol. 2020;93:20190564.

    Article  PubMed  PubMed Central  Google Scholar 

  80. den Hartogh MD, Philippens ME, van Dam IE, Kleynen CE, et al. MRI and CT imaging for preoperative target volume delineation in breast-conserving therapy. Radiat Oncol. 2014;9:63.

    Article  Google Scholar 

  81. Lee E, Park W, Ahn SH, Cho JH, et al. Interobserver variation in target volume for salvage radiotherapy in recurrent prostate cancer patients after radical prostatectomy using CT versus combined CT and MRI: a multicenter study (KROG 13-11). Radiat Oncol J. 2018;36:11–6.

    Article  PubMed  Google Scholar 

  82. White I, Hunt A, Bird T, Settatree S, et al. Interobserver variability in target volume delineation for CT/MRI simulation and MRI-guided adaptive radiotherapy in rectal cancer. Br J Radiol. 2021;94:20210350.

    Article  PubMed  PubMed Central  Google Scholar 

  83. Zhang H, Fu C, Fan M, Lu L, et al. Reduction of inter-observer variability using MRI and CT fusion in delineating of primary tumor for radiotherapy in lung cancer with atelectasis. Front Oncol. 2022;12:841771.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Weltens C, Menten J, Feron M, Bellon E, et al. Interobserver variations in gross tumor volume delineation of brain tumors on computed tomography and impact of magnetic resonance imaging. Radiother Oncol. 2001;60:49–59.

    Article  CAS  PubMed  Google Scholar 

  85. Aoyama H, Shirato H, Nishioka T, Hashimoto S, et al. Magnetic resonance imaging system for three-dimensional conformal radiotherapy and its impact on gross tumor volume delineation of central nervous system tumors. Int J Radiat Oncol Biol Phys. 2001;50:821–7.

    Article  CAS  PubMed  Google Scholar 

  86. Aslian H, Sadeghi M, Mahdavi SR, Babapour Mofrad F, et al. Magnetic resonance imaging-based target volume delineation in radiation therapy treatment planning for brain tumors using localized region-based active contour. Int J Radiat Oncol Biol Phys. 2013;87:195–201.

    Article  PubMed  Google Scholar 

  87. Raman S, Chin L, Erler D, Atenafu EG, et al. Impact of magnetic resonance imaging on gross tumor volume delineation in non-spine bony metastasis treated with stereotactic body radiation therapy. Int J Radiat Oncol Biol Phys. 2018;102:735–43.

    Article  PubMed  Google Scholar 

  88. Dalah E, Moraru I, Paulson E, Erickson B, et al. Variability of target and normal structure delineation using multimodality imaging for radiation therapy of pancreatic cancer. Int J Radiat Oncol Biol Phys. 2014;89:633–40.

    Article  PubMed  Google Scholar 

  89. Vorwerk H, Beckmann G, Bremer M, Degen M, et al. The delineation of target volumes for radiotherapy of lung cancer patients. Radiother Oncol. 2009;91:455–60.

    Article  PubMed  Google Scholar 

  90. Cazzaniga L, Marinoni M, Bossi A, Bianchi E, et al. Interphysician variability in defining the planning target volume in the irradiation of prostate and seminal vesicles. Radiother Oncol. 1998;28:293–6.

    Article  Google Scholar 

  91. Tsang Y, Hoskin P, Spezi E, Landau D, et al. Assessment of contour variability in target volumes and organs at risk in lung cancer radiotherapy. Tech Innov Patient Support Radiat Oncol. 2019;10:8–12.

    Article  PubMed  PubMed Central  Google Scholar 

  92. Unkelbach J, Bortfeld T, Cardenas CE, Gregoire V, et al. The role of computational methods for automating and improving clinical target volume definition. Radiother Oncol. 2020;153:15–25.

    Article  PubMed  Google Scholar 

  93. Das IJ, Compton JJ, Bajaj A, Johnstone PA. Intra- and inter-physician variability in target volume delineation in radiation therapy. J Radiat Res. 2021;62:1083–9.

    Google Scholar 

  94. Wong J, Fong A, McVicar N, Smith S, et al. Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning. Radiother Oncol. 2020;144:152–8.

    Article  PubMed  Google Scholar 

  95. Wong J, Huang V, Wells D, Giambattista J, et al. Implementation of deep learning-based auto-segmentation for radiotherapy planning structures: a workflow study at two cancer centers. Radiat Oncol. 2021;16:101.

    Article  PubMed  PubMed Central  Google Scholar 

  96. Gooding MJ, Smith AJ, Tariq M, Aljabar P, et al. Comparative evaluation of autocontouring in clinical practice: a practical method using the Turing test. Med Phys. 2018;45:5105–15.

    Article  PubMed  Google Scholar 

  97. Loap P, De Marzi L, Kirov K, Servois V, et al. Development of simplified auto-segmentable functional cardiac atlas. Pract Radiat Oncol. 2022;12:533–8.

    Article  PubMed  Google Scholar 

  98. Rhee DJ, Akinfenwa CPA, Rigaud B, Jhingran A, et al. Automatic contouring QA method using a deep learning-based autocontouring system. J Appl Clin Med Phys. 2022;23:e13647.

    Article  PubMed  PubMed Central  Google Scholar 

  99. Kawula M, Hadi I, Nierer L, Vagni M, et al. Patient-specific transfer learning for auto-segmentation in adaptive 0.35 T MRgRT of prostate cancer: a bi-centric evaluation. Med Phys. 2023;50:1573–85.

    Article  CAS  PubMed  Google Scholar 

  100. Fast MF, Eiben B, Menten MJ, Wetscherek A, et al. Tumour auto-contouring on 2d cine MRI for locally advanced lung cancer: a comparative study. Radiother Oncol. 2017;125:485–91.

    Article  PubMed  PubMed Central  Google Scholar 

  101. Liang F, Qian P, Su KH, Baydoun A, et al. Abdominal, multi-organ, auto-contouring method for online adaptive magnetic resonance guided radiotherapy: an intelligent, multi-level fusion approach. Artif Intell Med. 2018;90:34–41.

    Article  PubMed  Google Scholar 

  102. Feng L, Grimm R, Block KT, Chandarana H, et al. Golden-angle radial sparse parallel MRI: combination of compressed sensing, parallel imaging, and golden-angle radial sampling for fast and flexible dynamic volumetric MRI. Magn Reson Med. 2014;72:707–17.

    Article  PubMed  Google Scholar 

  103. Nepal P, Bagga B, Feng L, Chandarana H. Respiratory motion management in abdominal MRI: radiology in training. Radiology. 2023;306:47–53.

    Article  PubMed  Google Scholar 

  104. Curtis AD, Cheng HM. Primer and historical review on rapid cardiac CINE MRI. J Magn Reson Imaging. 2022;55:373–88.

    Article  PubMed  Google Scholar 

  105. Akino Y, Oh RJ, Masai N, Shiomi H, et al. Evaluation of potential internal target volume of liver tumors using cine-MRI. Med Phys. 2014;41:111704.

    Article  PubMed  Google Scholar 

  106. Ho VB, Foo TK. Impact of “Cine MR imaging: potential for the evaluation of cardiovascular function”. AJR Am J Roentgenol. 2006;187:605–8.

    Article  PubMed  Google Scholar 

  107. Kim T, Wu Y, Ji Z, Gach HM, et al. Technical note: Institutional solution of clinical cine MRI for tumor motion evaluation in radiotherapy. J Appl Clin Med Phys. 2022;23:e13650.

    Article  PubMed  PubMed Central  Google Scholar 

  108. Winkel D, Bol GH, Kroon PS, van Asselen B, et al. Adaptive radiotherapy: the Elekta Unity MR-linac concept. Clin Transl Radiat Oncol. 2019;18:54–9.

    PubMed  PubMed Central  Google Scholar 

  109. Wald C, Luchs J, Davila J, Lozano K, et al. Residents’ preceptions of MRI training in the United States. J Am Coll Radiol. 2004;1:331–7.

    Article  PubMed  Google Scholar 

  110. McGee KP, Tyagi N, Bayouth JE, Cao M, et al. Findings of the AAPM Ad Hoc committee on magnetic resonance imaging in radiation therapy: Unmet needs, opportunities, and recommendations. Med Phys. 2021;48:4523–31.

    Article  PubMed  Google Scholar 

  111. Singer L, Rosenberg SA. The impact of MRI on radiation oncology graduate medical education. J Am Coll Radiol. 2019;16:859–63.

    Article  PubMed  Google Scholar 

  112. Hasford F, Mumuni AN, Trauernicht C, Ige TA, et al. A review of MRI studies in Africa with special focus on quantitative MRI: historical development, current status and the role of medical physicists. Phys Med. 2022;103:46–58.

    Article  PubMed  Google Scholar 

  113. Hall WA, Paulson ES, van der Heide UA, Fuller CD, et al. The transformation of radiation oncology using real-time magnetic resonance guidance: a review. Eur J Cancer. 2019;122:42–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Cahoon G, Skehan K, Elwadia D, Rai R. The current and future role of the MRI radiographer in radiation oncology: a collaborative, experiential reflection on the Australian rollout of dedicated MRI simulators. J Med Radiat Sci. 2023;70(Suppl 2):107–13.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Indra J. Das .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Das, I.J., Yadav, P., Alongi, F., Mittal, B.B. (2024). Role of MRI in Radiation Oncology. In: Das, I.J., Alongi, F., Yadav, P., Mittal, B.B. (eds) A Practical Guide to MR-Linac. Springer, Cham. https://doi.org/10.1007/978-3-031-48165-9_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-48165-9_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48164-2

  • Online ISBN: 978-3-031-48165-9

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics