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Multi-level pooling encoder-decoder convolution neural network for MRI reconstruction.

Sarattha Karnjanapreechakorn1, Worapan Kusakunniran1, Thanongchai Siriapisith2

  • 1Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom, Thailand.

Peerj. Computer Science
|May 2, 2022
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Summary

This study introduces a lightweight deep learning network, the Multi-Level Pooling Encoder-Decoder Net (MLPED Net), for faster Magnetic Resonance Imaging (MRI) reconstruction. The MLPED Net achieves high-quality MR image reconstruction with high acceleration factors.

Keywords:
Encoder–decoder CNNFast MRIMRI reconstructionMulti-level pooling

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Magnetic Resonance Imaging (MRI) reconstruction is crucial for image quality and speed.
  • Traditional parallel imaging and reconstruction techniques accelerate MRI by undersampling data.
  • Existing methods face limitations in balancing speed and image fidelity.

Purpose of the Study:

  • To develop a novel, lightweight deep learning network for accelerated MRI reconstruction.
  • To improve the quality and acceleration factor of MR image reconstruction.
  • To reduce the computational burden and training time for MRI reconstruction.

Main Methods:

  • A novel lightweight deep neural network, the Multi-Level Pooling Encoder-Decoder Net (MLPED Net), was proposed.
  • The MLPED Net was trained end-to-end using undersampled data from the fastMRI knee dataset.
  • The network reconstructs high-quality MR images from undersampled k-space data.

Main Results:

  • The MLPED Net achieved high-quality MR image reconstruction with a high peak signal-to-noise ratio (PSNR).
  • The network demonstrated superior performance compared to traditional encoder-decoder networks at 4-fold acceleration.
  • Significant improvements were observed across all evaluation metrics.

Conclusions:

  • The proposed MLPED Net offers a promising solution for accelerating MRI acquisition and reconstruction.
  • Its lightweight architecture significantly reduces training time and computational cost.
  • This deep learning approach enables higher acceleration factors without compromising image quality.