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Pseudo-SPR Map Generation from MRI Using U-Net Architecture for Ion Beam Therapy Application

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Medical Image Understanding and Analysis (MIUA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14122))

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Abstract

Stopping power ratio (SPR) maps are needed for dose deposition calculations and are typically estimated from single energy CT (SECT) in clinical routine. SECT-based SPR conversion leads to large variability due to the one-to-one relationship assumed by the conversion method. Dual-energy CT (DECT) involving the acquisition of two energy spectra captures both material-specific information and tissue characterization which is essential for an accurate SPR map conversion. The goal of this study is to train a U-Net architecture to generate pseudo-SPR map from MRI (Dixon) using a DECT-converted SPR map. The model performance was validated using Head & Neck cohort of 16 patients with paired MRI and SPR maps. The proposed solution achieved a mean absolute error (MAE) and peak-signal-to-noise-ratio (PSNR) of 19.41 ± 8.67 HU and 58.76 ± 2.17 dB respectively for all test cases. From observation, the sequential incorporation of different Dixon MRI images such as fat-suppressed and water-suppressed yielded an accurate pseudo-SPR map which is comparable to its corresponding target SPR map. Furthermore, bone delineation integrated as additional channel to Dixon MRI sequence demonstrated an enhanced bone identification on predicted pseudo-SPR map. As future direction, we would like to extend this approach to a clinical SPR map which will enable dosimetric analysis of clinical target volume (CTV) to be possible in treatment planning application for ion beam therapy.

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References

  1. Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49

    Chapter  Google Scholar 

  2. Dietterich, T.: Overfitting and undercomputing in machine learning. ACM Comput. Surv. (CSUR) 27(3), 326–327 (1995)

    Article  Google Scholar 

  3. Dowling, J.A., et al.: An atlas-based electron density mapping method for magnetic resonance imaging (mri)-alone treatment planning and adaptive mri-based prostate radiation therapy. Int. J. Radiation Oncol. Biol. Phys. 83(1), e5–e11 (2012)

    Google Scholar 

  4. Edmund, J.M., Kjer, H.M., Van Leemput, K., Hansen, R.H., Andersen, J.A., Andreasen, D.: A voxel-based investigation for mri-only radiotherapy of the brain using ultra short echo times. Phys. Med. Biol. 59(23), 7501 (2014)

    Article  Google Scholar 

  5. Edmund, J.M., Nyholm, T.: A review of substitute ct generation for mri-only radiation therapy. Radiat. Oncol. 12, 1–15 (2017)

    Article  Google Scholar 

  6. Forghani, R., De Man, B., Gupta, R.: Dual-energy computed tomography: physical principles, approaches to scanning, usage, and implementation: part 1. Neuroimaging Clin. 27(3), 371–384 (2017)

    Article  Google Scholar 

  7. Fu, Y., et al.: Pelvic multi-organ segmentation on cone-beam ct for prostate adaptive radiotherapy. Med. Phys. 47(8), 3415–3422 (2020)

    Article  Google Scholar 

  8. Hoffmann, A., et al.: Mr-guided proton therapy: a review and a preview. Radiat. Oncol. 15(1), 1–13 (2020)

    Article  Google Scholar 

  9. Hore, A., Ziou, D.: Image quality metrics: Psnr vs. ssim. In: 2010 20th International Conference on Pattern Recognition, pp. 2366–2369. IEEE (2010)

    Google Scholar 

  10. Hsu, S.H., Cao, Y., Huang, K., Feng, M., Balter, J.M.: Investigation of a method for generating synthetic CT models from MRI scans of the head and neck for radiation therapy. Phys. Med. Biol. 58(23), 8419 (2013)

    Article  Google Scholar 

  11. Hudobivnik, N., et al.: Comparison of proton therapy treatment planning for head tumors with a pencil beam algorithm on dual and single energy CT images. Med. Phys. 43(1), 495–504 (2016)

    Article  Google Scholar 

  12. Jais, I.K.M., Ismail, A.R., Nisa, S.Q.: Adam optimization algorithm for wide and deep neural network. Knowl. Eng. Data Sci. 2(1), 41–46 (2019)

    Article  Google Scholar 

  13. Johansson, A., Karlsson, M., Nyholm, T.: Ct substitute derived from MRI sequences with ultrashort echo time. Med. Phys. 38(5), 2708–2714 (2011)

    Article  Google Scholar 

  14. Karotki, A., Mah, K., Meijer, G., Meltsner, M.: Comparison of bulk electron density and voxel-based electron density treatment planning. J. Appl. Clin. Med. Phys. 12(4), 97–104 (2011)

    Article  Google Scholar 

  15. Kristensen, B.H., Laursen, F.J., Løgager, V., Geertsen, P.F., Krarup-Hansen, A.: Dosimetric and geometric evaluation of an open low-field magnetic resonance simulator for radiotherapy treatment planning of brain tumours. Radiother. Oncol. 87(1), 100–109 (2008)

    Article  Google Scholar 

  16. Largent, A., et al.: Pseudo-CT generation for MRI-only radiation therapy treatment planning: comparison among patch-based, atlas-based, and bulk density methods. Int. J. Radiation Oncol. Biol. Phys. 103(2), 479–490 (2019)

    Google Scholar 

  17. Leibfarth, S., et al.: A strategy for multimodal deformable image registration to integrate pet/MR into radiotherapy treatment planning. Acta Oncol. 52(7), 1353–1359 (2013)

    Article  Google Scholar 

  18. Leu, S.C., Huang, Z., Lin, Z.: Generation of pseudo-CT using high-degree polynomial regression on dual-contrast pelvic MRI data. Sci. Rep. 10(1), 1–11 (2020)

    Article  Google Scholar 

  19. Liu, R., et al.: Synthetic dual-energy CT for MRI-only based proton therapy treatment planning using label-gan. Phys. Med. Biol. 66(6), 065014 (2021)

    Article  Google Scholar 

  20. Liu, Y., et al.: MRI-based treatment planning for liver stereotactic body radiotherapy: validation of a deep learning-based synthetic ct generation method. Br. J. Radiol. 92(1100), 20190067 (2019)

    Article  Google Scholar 

  21. Liu, Y., et al.: Evaluation of a deep learning-based pelvic synthetic CT generation technique for MRI-based prostate proton treatment planning. Phys. Med. Biol. 64(20), 205022 (2019)

    Article  Google Scholar 

  22. Ma, X., Chen, X., Li, J., Wang, Y., Men, K., Dai, J.: Mri-only radiotherapy planning for nasopharyngeal carcinoma using deep learning. Front. Oncol. 11, 713617 (2021)

    Article  Google Scholar 

  23. Minogue, S., Gillham, C., Kearney, M., Mullaney, L.: Intravenous contrast media in radiation therapy planning computed tomography scans-current practice in Ireland. Techn. Innov. Patient Support Radiation Oncol. 12, 3–15 (2019)

    Article  Google Scholar 

  24. Owrangi, A.M., Greer, P.B., Glide-Hurst, C.K.: Mri-only treatment planning: benefits and challenges. Phys. Med. Biol. 63(5), 05TR01 (2018)

    Google Scholar 

  25. Perona, P., Shiota, T., Malik, J.: Anisotropic diffusion. Geometry-driven diffusion in computer vision, pp. 73–92 (1994)

    Google Scholar 

  26. Sjölund, J., Forsberg, D., Andersson, M., Knutsson, H.: Generating patient specific pseudo-CT of the head from MR using atlas-based regression. Phys. Med. Biol. 60(2), 825 (2015)

    Article  Google Scholar 

  27. Spadea, M.F., et al.: Deep convolution neural network (DCNN) multiplane approach to synthetic CT generation from MR images–application in brain proton therapy. Int. J. Radiation Oncol. Biol. Phys. 105(3), 495–503 (2019)

    Google Scholar 

  28. Tang, H., Cahill, L.: A new criterion for the evaluation of image restoration quality. In: TENCON’92-Technology Enabling Tomorrow, pp. 573–577. IEEE (1992)

    Google Scholar 

  29. Tustison, N.J., et al.: N4itk: improved n3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010). https://doi.org/10.1109/TMI.2010.2046908

    Article  Google Scholar 

  30. Uh, J., Merchant, T.E., Li, Y., Li, X., Hua, C.: MRI-based treatment planning with pseudo CT generated through atlas registration. Med. Phys. 41(5), 051711 (2014)

    Article  Google Scholar 

  31. Yang, M., et al.: Comprehensive analysis of proton range uncertainties related to patient stopping-power-ratio estimation using the stoichiometric calibration. Phys. Med. Biol. 57(13), 4095 (2012)

    Article  Google Scholar 

  32. Zhou, L., Pan, S., Wang, J., Vasilakos, A.V.: Machine learning on big data: opportunities and challenges. Neurocomputing 237, 350–361 (2017)

    Article  Google Scholar 

Download references

Funding

This study was mainly funded by German Federal Ministry of Education and Research within the track “Bildgeführte Diagnostik und Therapie - Neue Wege in der Intervention” in ARTEMIS project (13GW0436).

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Authors and Affiliations

  1. German Cancer Research Center (DKFZ), Division of Medical Physics in Radiation Oncology, Heidelberg, Germany

    Ama Katseena Yawson, Stefan Dorsch, Oliver Jäkel & Kristina Giske

  2. Heidelberg Institute for Radiation Oncology (HIRO), National Center for Radiation Research in Oncology (NCRO), Heidelberg, Germany

    Ama Katseena Yawson, Katharina Maria Paul, Cedric Beyer, Stefan Dorsch, Sebastian Klüter, Thomas Welzel, Katharina Seidensaal, Jürgen Debus, Oliver Jäkel & Kristina Giske

  3. Heidelberg University, Medical Faculty, Heidelberg, Germany

    Ama Katseena Yawson

  4. University Clinics Heidelberg, Department of Radiation Oncology, Heidelberg, Germany

    Katharina Maria Paul, Sebastian Klüter, Thomas Welzel, Katharina Seidensaal & Jürgen Debus

  5. Heidelberg Ion Therapy Centre (HIT), Heidelberg, Germany

    Cedric Beyer, Jürgen Debus & Oliver Jäkel

Authors
  1. Ama Katseena Yawson
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  2. Katharina Maria Paul
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  5. Sebastian Klüter
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  7. Katharina Seidensaal
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  8. Jürgen Debus
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  9. Oliver Jäkel
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  10. Kristina Giske
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Corresponding author

Correspondence to Ama Katseena Yawson .

Editor information

Editors and Affiliations

  1. University of Aberdeen, Aberdeen, UK

    Gordon Waiter

  2. University of Aberdeen, Aberdeen, UK

    Tryphon Lambrou

  3. University of Aberdeen, Aberdeen, UK

    Georgios Leontidis

  4. University of Aberdeen, Aberdeen, UK

    Nir Oren

  5. University of Aberdeen, Aberdeen, UK

    Teresa Morris

  6. University of Aberdeen, Aberdeen, UK

    Sharon Gordon

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

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Yawson, A.K. et al. (2024). Pseudo-SPR Map Generation from MRI Using U-Net Architecture for Ion Beam Therapy Application. In: Waiter, G., Lambrou, T., Leontidis, G., Oren, N., Morris, T., Gordon, S. (eds) Medical Image Understanding and Analysis. MIUA 2023. Lecture Notes in Computer Science, vol 14122. Springer, Cham. https://doi.org/10.1007/978-3-031-48593-0_19

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  • DOI: https://doi.org/10.1007/978-3-031-48593-0_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48592-3

  • Online ISBN: 978-3-031-48593-0

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