Abstract
Nowadays, obtaining high-quality magnetic resonance (MR) images is a complex problem due to several acquisition factors, but is crucial in order to perform good diagnostics. The enhancement of the resolution is a typical procedure applied after the image generation. State-of-the-art works gather a large variety of methods for super-resolution (SR), among which deep learning has become very popular during the last years. Most of the SR deep-learning methods are based on the minimization of the residuals by the use of Euclidean loss layers. In this paper, we propose an SR model based on the use of a p-norm loss layer to improve the learning process and obtain a better high-resolution (HR) image. This method was implemented using a three-dimensional convolutional neural network (CNN), and tested for several norms in order to determine the most robust fit. The proposed methodology was trained and tested with sets of MR structural T1-weighted images and showed better outcomes quantitatively, in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and the restored and the calculated residual images showed better CNN outputs.
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Acknowledgements
This work is partially supported by the Ministry of Economy and Competitiveness of Spain under grant TIN2014-53465-R, project name Video surveillance by active search of anomalous events. It is also partially supported by the Autonomous Government of Andalusia (Spain) under project P12-TIC-657, project name Self-organizing systems and robust estimators for video surveillance. Both of them include funds from the European Regional Development Fund (ERDF). The authors thankfully the grant of the University of Málaga and acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga. They also gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan X GPUs. Karl Thurnhofer-Hemsi (FPU15/06512) is funded by a PhD scholarship from the Spanish Ministry of Education, Culture and Sport under the FPU program. The authors acknowledge the funding from the following grants, which was used to develop the OASIS database by its creators: P50 AG05681, P01 AG03991, R01 AG021910, P50 MH071616, U24 RR021382, R01 MH56584.
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Thurnhofer-Hemsi, K., López-Rubio, E., Roé-Vellvé, N., Molina-Cabello, M.A. (2019). Deep Learning Networks with p-norm Loss Layers for Spatial Resolution Enhancement of 3D Medical Images. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) From Bioinspired Systems and Biomedical Applications to Machine Learning. IWINAC 2019. Lecture Notes in Computer Science(), vol 11487. Springer, Cham. https://doi.org/10.1007/978-3-030-19651-6_28
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