Deep Convolutional Encoder-Decoder algorithm for MRI brain reconstruction.
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 ar...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 59; no. 1; pp. 85 - 107 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jan2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=148139433&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148139433 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jan2021 vid: 59 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 148139433 147210635 148139433 NLM33231848 10.1007/s11517-020-02285-8 NLM33231848 148139433 ppf: 85 ppct: 22 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Convolutional Encoder-Decoder algorithm for MRI brain reconstruction. aug: au: Njeh, Ines Mzoughi, Hiba Ben Slima, Mohamed Ben Hamida, Ahmed Mhiri, Chokri Ben Mahfoudh, Kheireddine affil: Advanced Technology for Medicine & Signals, National School of Engineering of Sfax (ENIS), Sfax University, Sfax, Tunisia sug: subj: Magnetic Resonance Imaging Image Processing, Computer Assisted Algorithms Brain Clinical Assessment Tools Scales Ferrans and Powers Quality of Life Index ab: 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. Graphical abstract. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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