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ABSTRACTS OF ARTICLES OF THE JOURNAL "INFORMATION TECHNOLOGIES".
No. 2. Vol. 31. 2025

DOI: 10.17587/it.31.101-111

M. N. Favorskaya, Dr. Sc., Professor, N. Nishchhal, PhD Student,
Reshetnev Siberian State University of Science and Technology named after Academician M. F. Reshetnev, Krasnoyarsk, 660000, Russian Federation

Restoration of MRI Images Based on Multi-Task Learning

The restoration process of MRI images of a patient's abdominal region is considered based on multi-task learning, includ­ing the creation of super-resolution images and noise reduction using deep learning methods. An improved RIRGAN model is proposed by adding a noise reduction module that compensates for additive noise and nonlinear noise. The proposed multi-task model called MT-RIRGAN is trained using a complex loss function consisting of pixel loss, perceptual loss, adversarial loss, and total variation loss. Experiments demonstrate good recovery results of MRI images while preserving the original visual structures important from the point of view of medical diagnostics. Keywords: medical images, multi-tasking, image restoration, super-resolution, noise reduction, deep learning

P. 101-111

 

References

  1. Plenge E., Poot D. H., Bernsen M., Kotek G., Houston G., Wielopolski P., van der Weerd L., Niessen W. J., Meijering E. Super-resolution methods in MRI: Can they improve the trade-off between resolution, signal-to-noise ratio, and acquisition time?, Magnetic Resonance in Medicine, 2012, vol. 68, no. 6, pp. 1983—1993.
  2. Liu S., Johns E., Davison A. J. End-to-end multi-task learning with attention, The IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 1871—1880.
  3. 3. Feng C.-M., Yan Y., Fu H., Chen L., Xu Y. Task transformer network for joint MRI reconstruction and super-resolution, International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 2021, pp. 307—317.
  4. 4. Umirzakova S., Ahmad S., Khan L. U., Whangbo T. Medical image super-resolution for smart healthcare applications: A comprehensive survey, Information Fusion, 2024, vol. 103, pp. 102075.1—102075.32.
  5. Li J., Chen J., Tang Y., Wang C., Landman B. A., Zhou S. K. Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives, Medical Image Analysis, 2023, vol. 85, pp. 102762.1—102762.38.
  6. Azad R., Kazerouni A., Heidari M., Aghdam E. K., Molaei A., Jia Y., Jose A., Roy R., Merho D. Advances in medical image analysis with vision Transformers: A comprehensive review, Medical Image Analysis, 2024, vol. 91, pp. 103000.1—103000.66.
  7. Corona V., Aviles-Rivero A., Debroux N., Guyader C. L., Sch nlieb C.-B. Variational multi-task MRI reconstruction: Joint reconstruction, registration and super-resolution, Medical Image Analysis, 2021, vol. 68, pp. 101941.1—101941.16.
  8. Lim B., Son S., Kim H., Nah S., Lee K. M. Enhanced deep residual networks for single image super-resolution, The IEEE conference on computer vision and pattern recognition workshops, 2017, pp. 136—144.
  9. Wang W., Shen H., Chen J., Xing F. MHAN: Multi-stage hybrid attention network for MRI reconstruction and superresolution, Computers in Biology and Medicine, 2023, vol. 163, pp. 107181.1—107181.12.
  10. Yang G., Zhang L., Liu A., Fu X., Chen X., Wang R. MGDUN: An interpretable network for multi-contrast MRI image super-resolution reconstruction, Computers in Biology and Medicine, 2023, vol. 167, pp. 107605.1—107605.11.
  11. Yu M., Guo M., Zhang S., Zhan Y., Zhao M., Lukasiewicz T., Xu Z. RIRGAN: An end-to-end lightweight multi-task learning method for brain MRI super-resolution and denoising, Computers in Biology and Medicine, 2023, vol. 167, pp. 107632.1—107632.17.
  12. Jolicoeur-Martineau A. The relativistic discriminator: A key element missing from standard GAN, The Seventh International Conference on Learning Representations (ICLR 2019), New Orleans, Louisiana, US, 2019, pp. 1—26.
  13. Zhao Y., Wang X., Che T., Bao G., Li S. Multi-task deep learning for medical image computing and analysis: A review, Computers in Biology and Medicine, 2023, vol. 153, pp. 106496.1— 106496.15.
  14. Ledig C., Theis L., Husz r F., Caballero J., Cunningham A., Acosta A., Aitken A., Tejani A., Totz J., Wang Z., Shi W. Photorealistic single image super-resolution using a generative adversarial network, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 105—114.
  15. Pan H., Wen Y. W., Zhu H. M. A regularization parameter selection model for total variation based image noise removal. Applied Mathematical Modelling, 2019, vol. 68, pp. 353—367.
  16. Clark K., Vendt B., Smith K., Freymann J., Kirby J., Koppel P., Moore S., Phillips S., Maffitt D., Pringle M., Tarbox L., Prior F. The cancer imaging archive (TCIA): Maintaining and operating a public information repository. Journal of Digital Imaging, 2013, vol. 26, no. 6, pp. 1045—1057.
  17. Ji Y., Bai H., Ge C., Yang J., Zhu Y., Zhang R., Li Z., Zhang L., Ma W., Wan X., Luo P. AMOS: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation. In Koyejo S., Mohamed S., Agarwal A., Belgrave D., Cho K., Oh A. (eds.) Advances in Neural Information Processing Systems, 2022, vol. 35, pp. 36722—36732.

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