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Exploiting Generative Adversarial Networks in Joint Sensitivity Encoding for Enhanced MRI Reconstruction

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Advances in Visual Computing (ISVC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14362))

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Abstract

In this paper, we propose a novel approach for improving the quality of Parallel Magnetic Resonance Imaging (pMRI) reconstructions by incorporating the power of Generative Adversarial Networks (GANs). We integrate the Joint Sensitivity Encoding (JSENSE) technique with a GAN for parallel magnetic resonance imaging (MRI). The innovation lies in refining the JSENSE iterative reconstruction process using a GAN which effectively addresses the persistent challenge of low signal-to-noise ratio (SNR) and artifact degradation. While JSENSE offers improved reconstruction in rapid scanning or under-sampled acquisitions, images often exhibit noise and aliasing artifacts when the reduction factor is high. To resolve this problem, we deployed a GAN within the JSENSE framework for image-to-image translation and transforming noisy and artifact-ridden images into high-quality ones. Our GAN model, trained on paired sets of clean and noisy MRI images, performs noise and artifact removal after each JSENSE reconstruction iteration. Comparative evaluations with standard JSENSE and other contemporary techniques, such as CG-SENSE indicate significant improvement in the quality of the proposed method. Our approach achieved superior Structural Similarity Index Measure (SSIM) and lower Normalized Mean Squared Error (NMSE) with increased reduction factors and demonstrated its effectiveness in high-quality MRI reconstruction.

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References

  1. Larkman, D.J., Nunes, R.G.: Parallel magnetic resonance imaging. Phys. Med. Biol. 52(7), R15 (2007)

    Article  Google Scholar 

  2. Ying, L., Sheng, J.: Joint image reconstruction and sensitivity estimation in SENSE (JSENSE). Magn. Reson. Med. Official J. Int. Soc. Magn. Reson. Med. 57(6), 1196–1202 (2007)

    Article  Google Scholar 

  3. Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020)

    Article  MathSciNet  Google Scholar 

  4. Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)

    Google Scholar 

  5. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  6. Pruessmann, K.P., Weiger, M., Scheidegger, M.B., Boesiger, P.: SENSE: sensitivity encoding for fast MRI. Magn. Reson. Med. Official J. Int. Soc. Magn. Reson. Med. 42(5), 952–962 (1999)

    Article  Google Scholar 

  7. Laino, M.E., Cancian, P., Politi, L.S., Della Porta, M.G., Saba, L., Savevski, V.: Generative adversarial networks in brain imaging: a narrative review. J. Imaging 8(4), 83 (2022)

    Article  Google Scholar 

  8. Pal, A., Rathi, Y.: A review and experimental evaluation of deep learning methods for MRI reconstruction. J. Mach. Learn. Biomed. Imaging, 1 (2022)

    Google Scholar 

  9. Saju, G., Li, Z., Abiri, R., Liu, T., Chang, Y.: Incorporating untrained neural network prior in PROPELLER imaging. In: ISMRM Scientific Meeting & Exhibition (Vol. 4038) (2023)

    Google Scholar 

  10. Shitrit, O., Riklin Raviv, T.: Accelerated magnetic resonance imaging by adversarial neural network. In: Cardoso, M.J., (eds.) et al. DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 30–38. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_4

    Chapter  Google Scholar 

  11. Yang, G., et al.: DAGAN: Deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction. IEEE Trans. Med. Imaging, 37(6), 1310–1321 (2017)

    Google Scholar 

  12. Mardani, M., et al.: Deep generative adversarial neural networks for compressive sensing MRI. IEEE Trans. Med. Imaging 38(1), 167–179 (2018)

    Article  MathSciNet  Google Scholar 

  13. Shaul, R., David, I., Shitrit, O., Raviv, T.R.: Subsampled brain MRI reconstruction by generative adversarial neural networks. Med. Image Anal. 65, 101747 (2020)

    Article  Google Scholar 

  14. Li, Z., Zhang, T., Wan, P., Zhang, D.: SEGAN: structure-enhanced generative adversarial network for compressed sensing MRI reconstruction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 1012–1019 (2019)

    Google Scholar 

  15. Murugesan, B., Vijaya Raghavan, S., Sarveswaran, K., Ram, K., Sivaprakasam, M.: Recon-GLGAN: a global-local context based generative adversarial network for MRI reconstruction. In: Knoll, F., Maier, A., Rueckert, D., Ye, J.C. (eds.) MLMIR 2019. LNCS, vol. 11905, pp. 3–15. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33843-5_1

    Chapter  Google Scholar 

  16. Deora, P., Vasudeva, B., Bhattacharya, S., Pradhan, P. M.: Structure preserving compressive sensing MRI reconstruction using generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 522–523 (2020)

    Google Scholar 

  17. Shamshad, F., et al.: Transformers in medical imaging: a survey. Med. Image Anal. 88, 102802 (2023)

    Article  Google Scholar 

  18. Le Bihan, D., et al.: Diffusion tensor imaging: concepts and applications. J. Magn. Reson. Imaging: Official J. Int. Soc. Magn. Reson. Med. 13(4), 534–546 (2001)

    Article  Google Scholar 

  19. DeYoe, E.A., Bandettini, P., Neitz, J., Miller, D., Winans, P.: Functional magnetic resonance imaging (FMRI) of the human brain. J. Neurosci. Methods 54(2), 171–187 (1994)

    Article  Google Scholar 

  20. The OpenfMRI dataset. OpenfMRI. https://www.openfmri.org/dataset/

  21. Saju, G., Li, Z., Abiri, R., Liu, T., Chang, Y.: Improving JSENSE using an initial reconstruction with an unrolled deep network prior. In: ISMRM Scientific Meeting & Exhibition (Vol. 4037) (2023)

    Google Scholar 

  22. Saju, G., Li, Z., Abiri, R., Liu, T., Chang, Y.: Joint estimation of coil sensitivity and image by using untrained neural network without external training data. In: ISMRM Scientific Meeting & Exhibition, vol. 3893 (2023)

    Google Scholar 

  23. Chang, Y., Saju, G., Yu, J., Abiri, R., Li, Z., Liu, T.: Suppressing MRI background noise via modeling phase variations. In: ISMRM Scientific Meeting & Exhibition, vol. 2031 (2023)

    Google Scholar 

  24. Pruessmann, K.P., Weiger, M., Börnert, P., Boesiger, P.: Advances in sensitivity encoding with arbitrary k-space trajectories. Magn. Reson. Med. Official J. Int. Soc. Magn. Reson. Med. 46(4), 638–651 (2001)

    Article  Google Scholar 

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Acknowledgment

This work was supported by the National Science Foundation under Grant No. 2050972.

Author information

Authors and Affiliations

  1. Computer and Information Science Department, University of Massachusetts Dartmouth, 285 Old Westport Road, Dartmouth, MA, 02747, USA

    Gulfam Saju & Yuchou Chang

  2. Physics Department, Ursinus College, 601 E Main St, Collegeville, PA, 19426, USA

    Alan Okinaka

Authors
  1. Gulfam Saju
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  2. Alan Okinaka
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  3. Yuchou Chang
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Corresponding author

Correspondence to Yuchou Chang .

Editor information

Editors and Affiliations

  1. University of Nevada Reno, Reno, NV, USA

    George Bebis

  2. Google Research, Mountain View, CA, USA

    Golnaz Ghiasi

  3. New York University, New York, USA

    Yi Fang

  4. Ben-Gurion University, Be'er Sheva, Israel

    Andrei Sharf

  5. Microsoft Research, Beijing, China

    Yue Dong

  6. The University of Oklahoma, Norman, OK, USA

    Chris Weaver

  7. University of Maryland, Collage Park, MD, USA

    Zhicheng Leo

  8. University of Central Florida, Orlando, FL, USA

    Joseph J. LaViola Jr.

  9. InnerOptic Technology, Hillsborough, NC, USA

    Luv Kohli

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Saju, G., Okinaka, A., Chang, Y. (2023). Exploiting Generative Adversarial Networks in Joint Sensitivity Encoding for Enhanced MRI Reconstruction. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2023. Lecture Notes in Computer Science, vol 14362. Springer, Cham. https://doi.org/10.1007/978-3-031-47966-3_35

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  • DOI: https://doi.org/10.1007/978-3-031-47966-3_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47965-6

  • Online ISBN: 978-3-031-47966-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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