Iranian Journal of Medical Physics

Iranian Journal of Medical Physics

A Proposed Methodology for Magnetic Resonance Images' Geometrical Distortion Correction Intended To Use For Radiotherapy Planning Using a Large Inhouse Field of View

Document Type : Original Paper

Authors
Shahrokh Naseri 1
Mehdi Momennezhad 2
Atefeh Rostami 3
Hoda Zare 4
Kazem Anvari 5
Hamed Reza Sayah 6
Vajiheh Vejdani Noghreiyan 7
1 Department of medical physics, Faulty of medicine, Mashhad University of Medical Sciences, Mashhad, Iran
2 Mashhad University Of Medical sciences
3 Department of Medical Physics and Radiological Sciences, Sabzevar University of Medical Sciences, Sabzevar, Iran
4 Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
5 Department of Radiotherapy Oncology and Cancer Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
6 Kowsar MRI Center, Emam Reza Hospital, Mashhad, Iran
7 Medical Physics and Radiological Sciences Department, Sabzevar University of Medical Sciences
10.22038/ijmp.2022.55684.1920
Abstract
Introduction: Exquisite soft tissue contrast of magnetic resonance images (MRI) and the new combined radiotherapy system of MR-Linac have been the main impetus for applying MR imaging in radiotherapy. One limitation of MR-based radiotherapy is the geometric distortion of MR images that can generate errors in the contouring and dosimetry stages. This study aimed to evaluate and correct geometric distortion for radiotherapy applications.
Material and Methods: A large field of view (FOV) phantom develop using Perspex sheets and 325 plastic pipes. The quantification and correction of MR images' system-related geometric distortion are conducted for HASTE protocol by MATLAB and 3D slicer software in phantom and patient images. The effect of MRI images geometrical distortion was evaluated for ten patients undergoing body radiotherapy treatment.  CT images were used as a primary dataset to estimate the distortion map.
Results: The phantom investigation results indicate that in radial distances of < 13 cm (or FOVs < 25 cm), the amount of distortion is under 2 mm. Still, at more considerable radial distances, distortion may increase up to about 3.5 cm. MR images of Patients with lateral (LAT) and anterior-posterior (AP) diameters of more than 38 cm and 25 cm respectively, need to be corrected for geometric distortion.  
Conclusion: MR images' geometric precision in large FOVs is not sufficient for MRI only treatment planning of radiotherapy and further corrections are required. The B-spline deformable registration method can correct the MR geometric distortion until an acceptable range of 2 mm for radiotherapy applications.
Keywords
Geometrical Distortion Spatial Distortion Correction 3D Slicer Scatter Transform B
Spline Deformable Registration
Subjects
  1. Paulus DH, Oehmigen M, Grueneisen J, Umutlu L, Quick HH. Whole-body hybrid imaging concept for the integration of PET/MR into radiation therapy treatment planning. Physics in Medicine & Biology. 2016 Apr 7;61(9):3504.
  2. Schmidt MA, Payne GS. Radiotherapy planning using MRI. Physics in Medicine & Biology. 2015 Oct 28;60(22):R323.
  3. Metcalfe P, Liney GP, Holloway L, Walker A, Barton M, Delaney GP, Vinod S, Tome W. The potential for an enhanced role for MRI in radiation-therapy treatment planning. Technology in cancer research & treatment. 2013 Oct;12(5):429-46.
  4. Laruelo A. Integration of magnetic resonance spectroscopic imaging into the radiotherapy treatment planning (Doctoral dissertation, Université Paul Sabatier-Toulouse III).
  5. Dai YL, King AD. State of the art MRI in head and neck cancer. Clinical radiology. 2018 Jan 1;73(1):45-59.
  6. Leung D, Han X, Mikkelsen T, Nabors LB. Role of MRI in primary brain tumor evaluation. Journal of the National Comprehensive Cancer Network. 2014 Nov 1;12(11):1561-8.
  7. Price RG, Kim JP, Zheng W, Chetty IJ, Glide-Hurst C. Image guided radiation therapy using synthetic computed tomography images in brain cancer. International Journal of Radiation Oncology* Biology* Physics. 2016 Jul 15;95(4):1281-9.
  8. Balter J, Cao Y, Wang H, Huang K, Hsu SH, Requardt M, Shea SM. Optimizing MRI for radiation oncology: initial investigations. MAGNETOM Flash. 2013;45.
  9. Wang H, Du K, Qu J, Chandarana H, Das IJ. Dosimetric evaluation of magnetic resonance-generated synthetic CT for radiation treatment of rectal cancer. PLoS One. 2018 Jan 5;13(1):e0190883.
  10. Tyagi N, Fontenla S, Zelefsky M, Chong-Ton M, Ostergren K, Shah N, Warner L, Kadbi M, Mechalakos J, Hunt M. Clinical workflow for MR-only simulation and planning in prostate. Radiation Oncology. 2017 Dec;12(1):1-2.
  11. Wang C, Chao M, Lee L, Xing L. MRI-based treatment planning with electron density information mapped from CT images: a preliminary study. Technology in cancer research & treatment. 2008 Oct;7(5):341-7.
  12. Aldosary G, Szanto J, Holmes O, Lavigne B, Althobaity W, Sheikh A, Foottit C, Vandervoort E. Geometric inaccuracy and co-registration errors for CT, DynaCT and MRI images used in robotic stereotactic radiosurgery treatment planning. Physica Medica. 2020 Jan 1;69:212-22.
  13. Walker A, Liney G, Holloway L, Dowling J, Rivest‐Henault D, Metcalfe P. Continuous table acquisition MRI for radiotherapy treatment planning: distortion assessment with a new extended 3D volumetric phantom. Medical physics. 2015 Apr;42(4):1982-91.
  14. Adjeiwaah M, Bylund M, Lundman JA, Karlsson CT, Jonsson JH, Nyholm T. Quantifying the effect of 3T magnetic resonance imaging residual system distortions and patient-induced susceptibility distortions on radiation therapy treatment planning for prostate cancer. International Journal of Radiation Oncology* Biology* Physics. 2018 Feb 1;100(2):317-24.
  15. Price RG, Kadbi M, Kim J, Balter J, Chetty IJ, Glide‐Hurst CK. Characterization and correction of gradient nonlinearity induced distortion on a 1.0 T open bore MR‐SIM. Medical physics. 2015 Oct;42(10):5955-60.
  16. Weygand J, Fuller CD, Ibbott GS, Mohamed AS, Ding Y, Yang J, Hwang KP, Wang J. Spatial precision in magnetic resonance imaging–guided radiation therapy: the role of geometric distortion. International Journal of Radiation Oncology* Biology* Physics. 2016 Jul 15;95(4):1304-16.
  17. Paulson ES, Crijns SP, Keller BM, Wang J, Schmidt MA, Coutts G, van der Heide UA. Consensus opinion on MRI simulation for external beam radiation treatment planning. Radiotherapy and Oncology. 2016 Nov 1;121(2):187-92.
  18. Lundman JA, Johansson A, Olofsson J, Axelsson J, Larsson A, Nyholm T. Effect of gradient field nonlinearity distortions in MRI-based attenuation maps for PET reconstruction. Physica Medica. 2017 Mar 1;35:1-6.
  19. Price RG, Knight RA, Hwang KP, Bayram E, Nejad‐Davarani SP, Glide‐Hurst CK. Optimization of a novel large field of view distortion phantom for MR‐only treatment planning. Journal of applied clinical medical physics. 2017 Jul;18(4):51-61.
  20. Walker A, Liney G, Metcalfe P, Holloway L. MRI distortion: considerations for MRI based radiotherapy treatment planning. Australasian physical & engineering sciences in medicine. 2014 Mar;37(1):103-13.
  21. Wang D, Strugnell W, Cowin G, Doddrell DM, Slaughter R. Geometric distortion in clinical MRI systems: Part I: evaluation using a 3D phantom. Magnetic resonance imaging. 2004 Nov 1;22(9):1211-21.
  22. Torfeh T, Hammoud R, Perkins G, McGarry M, Aouadi S, Celik A, Hwang KP, Stancanello J, Petric P, Al-Hammadi N. Characterization of 3D geometric distortion of magnetic resonance imaging scanners commissioned for radiation therapy planning. Magnetic resonance imaging. 2016 Jun 1;34(5):645-53.
  23. Wang D, Doddrell DM, Cowin G. A novel phantom and method for comprehensive 3-dimensional measurement and correction of geometric distortion in magnetic resonance imaging. Magnetic resonance imaging. 2004 May 1;22(4):529-42.
  24. Wang D, Strugnell W, Cowin G, Doddrell DM, Slaughter R. Geometric distortion in clinical MRI systems: Part II: correction using a 3D phantom. Magnetic resonance imaging. 2004 Nov 1;22(9):1223-32..
  25. Damyanovich AZ, Rieker M, Zhang B, Bissonnette JP, Jaffray DA. Design and implementation of a 3D-MR/CT geometric image distortion phantom/analysis system for stereotactic radiosurgery. Physics in Medicine & Biology. 2018 Mar 27;63(7):075010.
  26. Rostami A, Naseri S, Momennezhad M, Zare H, Anvari K, Badkhor HS. Geometric distortion evaluation of magnetic resonance images by a new large field of view phantom for magnetic resonance based radiotherapy purposes. International Journal of Radiation Research. 2020 Oct 1;18(4):733-42.
  27. Tyagi N, Fontenla S, Zhang J, Cloutier M, Kadbi M, Mechalakos J, Zelefsky M, Deasy J, Hunt M. Dosimetric and workflow evaluation of first commercial synthetic CT software for clinical use in pelvis. Physics in Medicine & Biology. 2017 Mar 17;62(8):2961.
  28. Wyawahare MV, Patil PM, Abhyankar HK. Image registration techniques: an overview. International Journal of Signal Processing, Image Processing and Pattern Recognition. 2009 Sep;2(3):11-28
  29. Hill DL, Batchelor PG, Holden M, Hawkes DJ. Medical image registration. Physics in medicine & biology. 2001 Mar 1;46(3):R1.
  30. Rueckert D, Sonoda LI, Hayes C, Hill DL, Leach MO, Hawkes DJ. Nonrigid registration using free-form deformations: application to breast MR images. IEEE transactions on medical imaging. 1999 Aug;18(8):712-21.
  31. Fedorov A., Beichel R., Kalpathy-Cramer J., Finet J., Fillion-Robin J-C., Pujol S., Bauer C., Jennings D., Fennessy F.M., Sonka M., Buatti J., Aylward S.R., Miller J.V., Pieper S., Kikinis R. 3D Slicer as an Image Computing Platform for the Quantitative Imaging Network. Magn Reson Imaging. 2012 Nov;30(9):1323-41.
  32. MATLAB 2018a, The MathWorks, Inc., Natick, Massachusetts, United States.
  33. Vanel D. MRI of bone metastases: the choice of the sequence. Cancer Imaging. 2004;4(1):30.
  34. Koivula L. Magnetic resonance imaging-based radiation therapy treatment planning.
  35. Stanescu T, Hans-Sonke J, Stavrev P, Fallone BG. 3T MR-based treatment planning for radiotherapy of brain lesions. Radiology and Oncology. 2006 Jun 1;40(2).
  36. Stanescu T, Jans HS, Pervez N, Stavrev P, Fallone BG. A study on the magnetic resonance imaging (MRI)-based radiation treatment planning of intracranial lesions. Physics in Medicine & Biology. 2008 Jun 17;53(13):3579.
  37. Andreasen D, Van Leemput K, Hansen RH, Andersen JA, Edmund JM. Patch‐based generation of a pseudo CT from conventional MRI sequences for MRI‐only radiotherapy of the brain. Medical physics. 2015 Apr;42(4):1596-605.
  38. Chen L, Price RA, Nguyen TB, Wang L, Li JS, Qin L, Ding M, Palacio E, Ma CM, Pollack A. Dosimetric evaluation of MRI-based treatment planning for prostate cancer. Physics in medicine & biology. 2004 Oct 29;49(22):5157.
  39. Jonsson JH, Akhtari MM, Karlsson MG, Johansson A, Asklund T, Nyholm T. Accuracy of inverse treatment planning on substitute CT images derived from MR data for brain lesions. Radiat Oncol. 2015;10(1):13.
  40. Chen L, Price R, Wang L, Li J, Qin L, Mcneeley Sh, et al. MRI-BASED TREATMENT PLANNING FOR RADIOTHERAPY: DOSIMETRIC VERIFICATION FOR PROSTATE IMRT. Int J Radiat Oncol Biol Phys. 2004.60(2);636–
  41. Avants BB, Tustison N, Song G. Advanced normalization tools (ANTS). Insight j. 2009 Jun 4;2(365):1-35.

 

 

 

 

 

Iranian Journal of Medical Physics
Volume 19, Issue 5
September and October 2022
Pages 305-314

  • Receive Date 13 February 2021
  • Revise Date 01 February 2022
  • Accept Date 08 February 2022