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Evaluation of the ability of the Brainlab Elements Cranial Distortion Correction algorithm to correct clinically relevant MRI distortions for cranial SRT

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Abstract

Purpose

Cranial stereotactic radiotherapy (SRT) requires highly accurate lesion delineation. However, MRI can have significant inherent geometric distortions. We investigated how well the Elements Cranial Distortion Correction algorithm of Brainlab (Munich, Germany) corrects the distortions in MR image-sets of a phantom and patients.

Methods

A non-distorted reference computed tomography image-set of a CIRS Model 603-GS (CIRS, Norfolk, VA, USA) phantom was acquired. Three-dimensional T1-weighted images were acquired with five MRI scanners and reconstructed with vendor-derived distortion correction. Some were reconstructed without correction to generate heavily distorted image-sets. All MR image-sets were corrected with the Brainlab algorithm relative to the computed tomography acquisition. CIRS Distortion Check software measured the distortion in each image-set. For all uncorrected and corrected image-sets, the control points that exceeded the 0.5-mm clinically relevant distortion threshold and the distortion maximum, mean, and standard deviation were recorded. Empirical cumulative distribution functions (eCDF) were plotted. Intraclass correlation coefficient (ICC) was calculated. The algorithm was evaluated with 10 brain metastases using Dice similarity coefficients (DSC).

Results

The algorithm significantly reduced mean and standard deviation distortion in all image-sets. It reduced the maximum distortion in the heavily distorted image-sets from 2.072 to 1.059 mm and the control points with > 0.5-mm distortion fell from 50.2% to 4.0%. Before and especially after correction, the eCDFs of the four repeats were visually similar. ICC was 0.812 (excellent–good agreement). The algorithm increased the DSCs for all patients and image-sets.

Conclusion

The Brainlab algorithm significantly and reproducibly ameliorated MRI distortion, even with heavily distorted images. Thus, it increases the accuracy of cranial SRT lesion delineation. After further testing, this tool may be suitable for SRT of small lesions.

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References

  1. Gilbo P, Zhang I, Knisely J (2017) Stereotactic radiosurgery of the brain: a review of common indications. Chin Clin Oncol 6:S14

    Article  PubMed  Google Scholar 

  2. Sahgal A, Ruschin M, Ma L, Verbakel W, Larson D, Brown PD (2017) Stereotactic radiosurgery alone for multiple brain metastases? A review of clinical and technical issues. Neuro Oncol 19:ii2–ii15

    Article  PubMed  PubMed Central  Google Scholar 

  3. Hartgerink D, Swinnen A, Roberge D, Nichol A, Zygmanski P, Yin FF et al (2019) LINAC based stereotactic radiosurgery for multiple brain metastases: guidance for clinical implementation. Acta Oncol 58:1275–1282

    Article  PubMed  Google Scholar 

  4. Nakano H, Tanabe S, Utsunomiya S, Yamada T, Sasamoto R, Nakano T et al (2020) Effect of setup error in the single-isocenter technique on stereotactic radiosurgery for multiple brain metastases. J Appl Clin Med Phys 21:155–165

    Article  PubMed  PubMed Central  Google Scholar 

  5. Putz F, Mengling V, Perrin R, Masitho S, Weissmann T, Rosch J et al (2020) Magnetic resonance imaging for brain stereotactic radiotherapy : a review of requirements and pitfalls. Strahlenther Onkol 196:444–456

    Article  PubMed  PubMed Central  Google Scholar 

  6. Schmitt D, Blanck O, Gauer T, Fix MK, Brunner TB, Fleckenstein J et al (2020) Technological quality requirements for stereotactic radiotherapy : expert review group consensus from the DGMP working group for physics and technology in Stereotactic radiotherapy. Strahlenther Onkol 196:421–443

    Article  PubMed  PubMed Central  Google Scholar 

  7. Dellios D, Pappas EP, Seimenis I, Paraskevopoulou C, Lampropoulos KI, Lymperopoulou G et al (2021) Evaluation of patient-specific MR distortion correction schemes for improved target localization accuracy in SRS. Med Phys 48:1661–1672

    Article  CAS  PubMed  Google Scholar 

  8. Weygand J, Fuller CD, Ibbott GS, Mohamed AS, Ding Y, Yang J et al (2016) Spatial precision in magnetic resonance imaging-guided radiation therapy: the role of geometric distortion. Int J Radiat Oncol Biol Phys 95:1304–1316

    Article  PubMed  Google Scholar 

  9. Lightstone AW, Benedict SH, Bova FJ, Solberg TD, Stern RL, American Association of Physicists in Medicine Radiation Therapy C (2005) Intracranial stereotactic positioning systems: report of the American Association of Physicists in Medicine Radiation Therapy Committee Task Group no. 68. Med Phys 32:2380–2398

    Article  PubMed  Google Scholar 

  10. Seibert TM, White NS, Kim GY, Moiseenko V, McDonald CR, Farid N et al (2016) Distortion inherent to magnetic resonance imaging can lead to geometric miss in radiosurgery planning. Pract Radiat Oncol 6:e319–e328

    Article  PubMed  PubMed Central  Google Scholar 

  11. Owrangi AM, Greer PB, Glide-Hurst CK (2018) MRI-only treatment planning: benefits and challenges. Phys Med Biol 63:05TR1

    Article  Google Scholar 

  12. Crijns SP, Raaymakers BW, Lagendijk JJ (2011) Real-time correction of magnetic field inhomogeneity-induced image distortions for MRI-guided conventional and proton radiotherapy. Phys Med Biol 56:289–297

    Article  CAS  PubMed  Google Scholar 

  13. Wang D, Doddrell DM, Cowin G (2004) A novel phantom and method for comprehensive 3‑dimensional measurement and correction of geometric distortion in magnetic resonance imaging. Magn Reson Imaging 22:529–542

    Article  PubMed  Google Scholar 

  14. Mizowaki T, Nagata Y, Okajima K, Kokubo M, Negoro Y, Araki N et al (2000) Reproducibility of geometric distortion in magnetic resonance imaging based on phantom studies. Radiother Oncol 57:237–242

    Article  CAS  PubMed  Google Scholar 

  15. Doran SJ, Charles-Edwards L, Reinsberg SA, Leach MO (2005) A complete distortion correction for MR images: I. Gradient warp correction. Phys Med Biol 50:1343–1361

    Article  PubMed  Google Scholar 

  16. Shan S, Li M, Li M, Tang F, Crozier S, Liu F (2021) ReUINet: A fast GNL distortion correction approach on a 1.0 T MRI-Linac scanner. Med Phys 48:2991–3002

    Article  PubMed  Google Scholar 

  17. Janke A, Zhao H, Cowin GJ, Galloway GJ, Doddrell DM (2004) Use of spherical harmonic deconvolution methods to compensate for nonlinear gradient effects on MRI images. Magn Reson Med 52:115–122

    Article  PubMed  Google Scholar 

  18. Wang D, Strugnell W, Cowin G, Doddrell DM, Slaughter R (2004) Geometric distortion in clinical MRI systems Part II: correction using a 3D phantom. Magn Reson Imaging 22:1223–1232

    Article  PubMed  Google Scholar 

  19. Wang H, Balter J, Cao Y (2013) Patient-induced susceptibility effect on geometric distortion of clinical brain MRI for radiation treatment planning on a 3T scanner. Phys Med Biol 58:465–477

    Article  CAS  PubMed  Google Scholar 

  20. Pappas EP, Alshanqity M, Moutsatsos A, Lababidi H, Alsafi K, Georgiou K et al (2017) MRI-related geometric distortions in stereotactic radiotherapy treatment planning: evaluation and dosimetric impact. Technol Cancer Res Treat 16:1120–1129

    Article  PubMed  PubMed Central  Google Scholar 

  21. Walker A, Metcalfe P, Liney G, Batumalai V, Dundas K, Glide-Hurst C et al (2016) MRI geometric distortion: Impact on tangential whole-breast IMRT. J Appl Clin Med Phys 17:7–19

    Article  PubMed  PubMed Central  Google Scholar 

  22. Winter RM, Schmidt H, Leibfarth S, Zwirner K, Welz S, Schwenzer NF et al (2017) Distortion correction of diffusion-weighted magnetic resonance imaging of the head and neck in radiotherapy position. Acta Oncol 56:1659–1663

    Article  PubMed  Google Scholar 

  23. Calvo-Ortega JF, Mateos J, Alberich A, Moragues S, Acebes JJ, Jose SS et al (2019) Evaluation of a novel software application for magnetic resonance distortion correction in cranial stereotactic radiosurgery. Med Dosim 44:136–143

    Article  PubMed  Google Scholar 

  24. Gerhardt J, Sollmann N, Hiepe P, Kirschke JS, Meyer B, Krieg SM et al (2019) Retrospective distortion correction of diffusion tensor imaging data by semi-elastic image fusion—Evaluation by means of anatomical landmarks. Clin Neurol Neurosurg 183:105387

    Article  PubMed  Google Scholar 

  25. Hiepe P (2017) Cranial distortion correction technical background. Brainlab

    Google Scholar 

  26. Koo TK, Li MY (2016) A guideline of selecting and reporting Intraclass correlation coefficients for reliability research. J Chiropr Med 15:155–163

    Article  PubMed  PubMed Central  Google Scholar 

  27. Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26:297–302

    Article  Google Scholar 

  28. Cicchetti DV (1994) Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychol Assess 6:284–290

    Article  Google Scholar 

  29. Rampton JW, Young PM, Fidler JL, Hartman RP, Herfkens RJ (2013) Putting the fat and water protons to work for you: a demonstration through clinical cases of how fat-water separation techniques can benefit your body MRI practice. AJR Am J Roentgenol 201:1303–1308

    Article  PubMed  Google Scholar 

  30. Schenck JF (1996) The role of magnetic susceptibility in magnetic resonance imaging: MRI magnetic compatibility of the first and second kinds. Med Phys 23:815–850

    Article  CAS  PubMed  Google Scholar 

  31. Bagherimofidi SM, Yang CC, Rey-Dios R, Kanakamedala MR, Fatemi A (2019) Evaluating the accuracy of geometrical distortion correction of magnetic resonance images for use in intracranial brain tumor radiotherapy. Rep Pract Oncol Radiother 24:606–613

    Article  PubMed  PubMed Central  Google Scholar 

  32. Morgan PS, Bowtell RW, McIntyre DJ, Worthington BS (2004) Correction of spatial distortion in EPI due to inhomogeneous static magnetic fields using the reversed gradient method. J Magn Reson Imaging 19:499–507

    Article  PubMed  Google Scholar 

  33. Karaiskos P, Moutsatsos A, Pappas E, Georgiou E, Roussakis A, Torrens M et al (2014) A simple and efficient methodology to improve geometric accuracy in gamma knife radiation surgery: implementation in multiple brain metastases. Int J Radiat Oncol Biol Phys 90:1234–1241

    Article  PubMed  Google Scholar 

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Acknowledgements

We thank Dr. Yinka Zevering of SciMeditor Medical Writing and Editing Services for assistance with the manuscript. We also thank Mircea Lazea from CIRS (CIRS, Norfolk, VA, USA) for sharing all the technical information about the Distortion Check software.

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Authors and Affiliations

  1. Medical Physics Unit, CHR Metz-Thionville, Metz, France

    Paul Retif, Abdourahamane Djibo Sidikou, Romain Letellier & Emilie Verrecchia-Ramos

  2. Université de Lorraine, CNRS, CRAN, 54000, Nancy, France

    Paul Retif

  3. Medical Imaging Department, CHR Metz-Thionville, Metz, France

    Christian Mathis & Rémi Dupres

  4. Radiation Therapy Department, CHR Metz-Thionville, Metz, France

    Xavier Michel

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  1. Paul Retif
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Contributions

All authors certify that they have been included in substantial contributions to the conception or design of the work (all authors); or the acquisition (Paul Retif, Abdourahamane Djibo Sidikou, and Christian Mathis), analysis (Paul Retif, Abdourahamane Djibo Sidikou, and Xavier Michel), or interpretation of data for the work (Paul Retif and Abdourahamane Djibo Sidikou); or drafting the work or revising it critically for important intellectual content (all authors). Final approval of the version to be published was given by all authors, as was agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Correspondence to Paul Retif.

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Conflict of interest

P. Retif, A. Djibo Sidikou, C. Mathis, R. Letellier, E. Verrecchia-Ramos, R. Dupres, and X. Michel declare that they have no competing interests. None of the authors has a professional or personal relationship with Brainlab (Munich, Germany), which produced the Brainlab Elements Cranial Distortion Correction algorithm.

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Retif, P., Djibo Sidikou, A., Mathis, C. et al. Evaluation of the ability of the Brainlab Elements Cranial Distortion Correction algorithm to correct clinically relevant MRI distortions for cranial SRT. Strahlenther Onkol 198, 907–918 (2022). https://doi.org/10.1007/s00066-022-01988-1

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  • DOI: https://doi.org/10.1007/s00066-022-01988-1

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