Deep Convolutional Encoder-Decoder algorithm for MRI brain reconstruction
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ORIGINAL ARTICLE
Deep Convolutional Encoder-Decoder algorithm for MRI brain reconstruction Ines Njeh 1,2 & Hiba Mzoughi 1 & Mohamed Ben Slima 1,3 & Ahmed Ben Hamida 1 & Chokri Mhiri 4 & Kheireddine Ben Mahfoudh 5 Received: 17 April 2020 / Accepted: 31 October 2020 # International Federation for Medical and Biological Engineering 2020
Abstract Compressed Sensing Magnetic Resonance Imaging (CS-MRI) could be considered a challenged task since it could be designed as an efficient technique for fast MRI acquisition which could be highly beneficial for several clinical routines. In fact, it could grant better scan quality by reducing motion artifacts amount as well as the contrast washout effect. It offers also the possibility to reduce the exploration cost and the patient’s anxiety. Recently, Deep Learning Neuronal Network (DL) has been suggested in order to reconstruct MRI scans with conserving the structural details and improving parallel imaging-based fast MRI. In this paper, we propose Deep Convolutional Encoder-Decoder architecture for CS-MRI reconstruction. Such architecture bridges the gap between the non-learning techniques, using data from only one image, and approaches using large training data. The proposed approach is based on autoencoder architecture divided into two parts: an encoder and a decoder. The encoder as well as the decoder has essentially three convolutional blocks. The proposed architecture has been evaluated through two databases: Hammersmith dataset (for the normal scans) and MICCAI 2018 (for pathological MRI). Moreover, we extend our model to cope with noisy pathological MRI scans. The normalized mean square error (NMSE), the peak-to-noise ratio (PSNR), and the structural similarity index (SSIM) have been adopted as evaluation metrics in order to evaluate the proposed architecture performance and to make a comparative study with the state-of-the-art reconstruction algorithms. The higher PSNR and SSIM values as well as the lowest NMSE values could attest that the proposed architecture offers better reconstruction and preserves textural image details. Furthermore, the running time is about 0.8 s, which is suitable for real-time processing. Such results could encourage the neurologist to adopt it in their clinical routines. Keywords Compressed Sensing Magnetic Resonance Imaging (CS-MRI) . Image reconstruction . Convolutional Encoder-Decoder architecture . Fast MRI . Deep Learning (DL)
1 Introduction * Ines Njeh [email protected] 1
Advanced Technology for Medicine & Signals, National School of Engineering of Sfax (ENIS), Sfax University, Sfax, Tunisia
2
Higher Institute of Computer Science and Multimedia of Gabes, Gabes University, Zrig Eddakhlania, Tunisia
3
National School of Electronics and Telecommunications of Sfax, Sfax University, Sfax, Tunisia
4
Department of Neurology, Habib Bourguiba Hospital, Sfax, Tunisia
5
Department of Radiology, Habib Bourguiba Hospital, Sfax, Tunisia
For numerous clinical explorations, MRI could be considered the best diagnostic modality. Such techni
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