Multi-level pooling encoder–decoder convolution neural network for MRI reconstruction
Issued Date
2022-01-01
Resource Type
eISSN
23765992
Scopus ID
2-s2.0-85129763414
Journal Title
PeerJ Computer Science
Volume
8
Rights Holder(s)
SCOPUS
Bibliographic Citation
PeerJ Computer Science Vol.8 (2022)
Suggested Citation
Karnjanapreechakorn S., Kusakunniran W., Siriapisith T., Saiviroonporn P. Multi-level pooling encoder–decoder convolution neural network for MRI reconstruction. PeerJ Computer Science Vol.8 (2022). doi:10.7717/PEERJ-CS.934 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/84395
Title
Multi-level pooling encoder–decoder convolution neural network for MRI reconstruction
Author's Affiliation
Other Contributor(s)
Abstract
MRI reconstruction is one of the critical processes of MRI machines, along with the acquisition. Due to a slow processing time of signal acquiring, parallel imaging and reconstruction techniques are applied for acceleration. To accelerate the acquisition process, fewer raw data are sampled simultaneously with all RF coils acquisition. Then, the reconstruction uses under-sampled data from all RF coils to restore the final MR image that resembles the fully sampled MR image. These processes have been a traditional procedure inside the MRI system since the invention of the multicoils MRI machine. This paper proposes the deep learning technique with a lightweight network. The deep neural network is capable of generating the highquality reconstructed MR image with a high peak signal-to-noise ratio (PSNR). This also opens a high acceleration factor for MR data acquisition. The lightweight network is called Multi-Level Pooling Encoder–Decoder Net (MLPED Net). The proposed network outperforms the traditional encoder–decoder networks on 4-fold acceleration with a significant margin on every evaluation metric. The network can be trained end-to-end, and it is a lightweight structure that can reduce training time significantly. Experimental results are based on a publicly available MRI Knee dataset from the fastMRI competition.