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Restoration of MRI images based on multi-task learning
- Authors: Favorskaya M.N.1, Nishchhal N.1
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Affiliations:
- Reshetnev Siberian State University of Science and Technology named after Academician M. F. Reshetnev
- Issue: Vol 31, No 2 (2025)
- Pages: 101-111
- Section: Information technologies in biomedical systems
- Published: 15.02.2025
- URL: https://journals.eco-vector.com/1684-6400/article/view/702201
- DOI: https://doi.org/10.17587/it.31.101-111
- ID: 702201
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Abstract
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.
About the authors
M. N. Favorskaya
Reshetnev Siberian State University of Science and Technology named after Academician M. F. Reshetnev
Author for correspondence.
Email: favorskaya@sibsau.ru
Dr. Sc., Professor
Russian Federation, Krasnoyarsk, 660000N. Nishchhal
Reshetnev Siberian State University of Science and Technology named after Academician M. F. Reshetnev
Email: nik.321g@yandex.ru
PhD Student
Russian Federation, Krasnoyarsk, 660000References
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