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GAN for synthesizing CT from T2-weighted MRI data towards MR-guided radiation treatment

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Magnetic Resonance Materials in Physics, Biology and Medicine Aims and scope Submit manuscript

Abstract

Objective

In medical domain, cross-modality image synthesis suffers from multiple issues , such as context-misalignment, image distortion, image blurriness, and loss of details. The fundamental objective behind this study is to address these issues in estimating synthetic Computed tomography (sCT) scans from T2-weighted Magnetic Resonance Imaging (MRI) scans to achieve MRI-guided Radiation Treatment (RT).

Materials and methods

We proposed a conditional generative adversarial network (cGAN) with multiple residual blocks to estimate sCT from T2-weighted MRI scans using 367 paired brain MR-CT images dataset. Few state-of-the-art deep learning models were implemented to generate sCT including Pix2Pix model, U-Net model, autoencoder model and their results were compared, respectively.

Results

Results with paired MR-CT image dataset demonstrate that the proposed model with nine residual blocks in generator architecture results in the smallest mean absolute error (MAE) value of \(0.030 \pm 0.017\), and mean squared error (MSE) value of \(0.010 \pm 0.011\), and produces the largest Pearson correlation coefficient (PCC) value of \(0.954 \pm 0.041\), SSIM value of \(0.823 \pm 0.063\) and peak signal-to-noise ratio (PSNR) value of \(21.422 \pm 3.964\), respectively. We qualitatively evaluated our result by visual comparisons of generated sCT to original CT of respective MRI input.

Discussion

The quantitative and qualitative comparison of this work demonstrates that deep learning-based cGAN model can be used to estimate sCT scan from a reference T2 weighted MRI scan. The overall accuracy of our proposed model outperforms different state-of-the-art deep learning-based models.

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Data and Code Availability

https://github.com/Amitranjan71/GAN-for-Synthesizing-CT-from-T2-Weighted-MRI-data-towards-MR-guided-Radiation-Treatment.git.

References

  1. Maspero M, Savenije MH, Dinkla AM, Seevinck PR, Intven MP, Jurgenliemk-Schulz IM, van den Berg CA (2018) Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy. Phys Med Biol 63(18):185001

  2. Maspero M, Bentvelzen LG, Savenije MH, Guerreiro F, Seravalli E, Janssens GO, Philippens ME (2020) Deep learning-based synthetic CT generation for paediatric brain MR-only photon and proton radiotherapy. Radiother Oncol 153:197–204

    Article  CAS  Google Scholar 

  3. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Bengio Y (2014) Generative adversarial nets. Adv Neural Inf Process Syst 27:2672–2680

    Google Scholar 

  4. Isola P, Zhu JY, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks, pp 1125–1134

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

    Article  Google Scholar 

  6. Arabi H, Koutsouvelis N, Rouzaud M, Miralbell R, Zaidi H (2016) Atlas-guided generation of pseudo-CT images for MRI-only and hybrid PET-MRI-guided radiotherapy treatment planning. Phys Med Biol 61(17):6531

    Article  CAS  Google Scholar 

  7. Edmund JM, Kjer HM, Van Leemput K, Hansen RH, Andersen JA, Andreasen D (2014) A voxel-based investigation for MRI-only radiotherapy of the brain using ultra short echo times. Phys Med Biol 59(23):7501

    Article  Google Scholar 

  8. Berker Y, Franke J, Salomon A, Palmowski M, Donker HC, Temur Y, Schulz V (2012) MRI-based attenuation correction for hybrid PET/MRI systems: a 4-class tissue segmentation technique using a combined ultrashort-echo-time/Dixon MRI sequence. J Nucl Med 53(5):796–804

    Article  Google Scholar 

  9. Arabi H, Dowling JA, Burgos N, Han X, Greer PB, Koutsouvelis N, Zaidi H (2018) Comparative study of algorithms for synthetic CT generation from MRI: consequences for MRI-guided radiation planning in the pelvic region. Med Phys 45(11):5218–5233

    Article  Google Scholar 

  10. Huynh T, Gao Y, Kang J, Wang L, Zhang P, Lian J, Shen D (2015) Estimating CT image from MRI data using structured random forest and auto-context model. IEEE Trans Med Imaging 35(1):174–183

    Article  Google Scholar 

  11. Xiang L, Wang Q, Nie D, Zhang L, Jin X, Qiao Y, Shen D (2018) Deep embedding convolutional neural network for synthesizing CT image from T1-Weighted MR image. Med Image Anal 47:31–44

    Article  Google Scholar 

  12. Sun C, Guo S, Zhang H, Li J, Ma S, Li X (2017) Liver lesion segmentation in CT images with MK-FCN. IEEE, pp 1794–1798

  13. Emami H, Dong M, Nejad-Davarani SP, Glide-Hurst CK (2018) Generating synthetic CTs from magnetic resonance images using generative adversarial networks. Med Phys 45(8):3627–3636

    Article  Google Scholar 

  14. Kazemifar S, McGuire S, Timmerman R, Wardak Z, Nguyen D, Park Y, Owrangi A (2019) MRI-only brain radiotherapy: Assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach. Radiother Oncol 136:56–63

    Article  Google Scholar 

  15. Yang Q, Yan P, Zhang Y, Hengyong Yu, Shi Y, Mou X, Kalra MK, Zhang Y, Sun L, Wang G (2018) Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE Trans Med Imaging 37(6):1348–1357

    Article  Google Scholar 

  16. Frid-Adar M, Diamant I, Klang E, Amitai M, Goldberger J, Greenspan H (2018) GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 321:321–331

    Article  Google Scholar 

  17. Shin HC, Tenenholtz NA, Rogers JK, Schwarz CG, Senjem ML, Gunter JL, Andriole KP, Michalski M (2018) Medical image synthesis for data augmentation and anonymization using generative adversarial networks. Springer, Cham, pp 1–11

    Google Scholar 

  18. Yang G, Yu S, Dong H, Slabaugh G, Dragotti PL, Ye X, Liu F et al (2017) DAGAN: Deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction. IEEE Trans Med imaging 37(6):1310–1321

    Article  Google Scholar 

  19. Armanious K, Jiang C, Fischer M, Küstner T, Hepp T, Nikolaou K, Gatidis S, Yang B (2020) MedGAN: Medical image translation using GANs. Comput Med Imaging Graph 79:101684

    Article  Google Scholar 

  20. Nie D, Trullo R, Lian J, Petitjean C, Ruan S, Wang Q, Shen D (2017) Medical image synthesis with context-aware generative adversarial networks. In: International conference on medical image computing and computer-assisted intervention. Springer, Cham, pp 417–425

  21. Dar SU, Yurt M, Karacan L, Erdem A, Erdem E, Çukur T (2019) Image synthesis in multi-contrast mri with conditional generative adversarial networks. IEEE Trans Med Imaging 38(10):2375–2388

    Article  Google Scholar 

  22. Dar SUH, Yurt M, Shahdloo M, Ildız ME, Tınaz B, Çukur T (2020) Prior-guided image reconstruction for accelerated multi-contrast MRI via generative adversarial networks. IEEE J Sel Topics Signal Process 14(6):1072–1087

    Article  Google Scholar 

  23. Johnson KA, Alex BJ (2020) Whole Brain Atlas. http://www.med.harvard.edu/AANLIB/home.html. Accessed 27 Nov

  24. Li C, Wand M (2016) Precomputed real-time texture synthesis with Markovian generative adversarial networks. Springer, Cham, pp 702–716

  25. Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29(6):1310–1320

    Article  Google Scholar 

  26. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66

    Article  Google Scholar 

  27. Cox IJ, Roy S, Hingorani SL (1995) Dynamic histogram warping of image pairs for constant image brightness, vol 2. IEEE, pp 366–369

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Patna, Bihta, 801103, India

    Amit Ranjan, Debanshu Lalwani & Rajiv Misra

Authors
  1. Amit Ranjan
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  2. Debanshu Lalwani
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  3. Rajiv Misra
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Contributions

The study’s inception and design were aided by all of the authors. Amit Ranjan and Debanshu Lalwani prepared the methods, collected the data, and analysed the results. The final manuscript was reviewed and approved by all authors. Rajiv Misra guided the development of this project.

Corresponding author

Correspondence to Amit Ranjan.

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Conflict of interest

Amit Ranjan, Debanshu Lalwani, and Rajiv Misra announce that they all have no conflict of interest.

Ethical approval

All techniques used in studies involving human subjects complied with the institutional and/or national research board’s ethical requirements, as well as the 1964 Helsinki manifesto and its subsequent revisions or similar ethical standards.

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Each individual involved in the research gave their informed consent.

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Ranjan, A., Lalwani, D. & Misra, R. GAN for synthesizing CT from T2-weighted MRI data towards MR-guided radiation treatment. Magn Reson Mater Phy 35, 449–457 (2022). https://doi.org/10.1007/s10334-021-00974-5

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  • DOI: https://doi.org/10.1007/s10334-021-00974-5

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