2D-MRI of the Central Nervous System: The effect of a deep learning-based reconstruction pipeline on the overall image quality

TL;DR

This study demonstrates that a deep learning-based reconstruction pipeline significantly improves 2D MRI image quality of the central nervous system by reducing artifacts and increasing SNR, enabling higher resolution or faster scans.

Contribution

The paper introduces a deep learning reconstruction pipeline that enhances MRI image quality by reducing Gibbs artifacts and boosting SNR compared to conventional methods.

Findings

DL reconstruction reduces Gibbs artifacts effectively.

SNR is significantly improved with DL pipeline.

Potential for higher resolution or shorter scan times.

Abstract

Purpose of this study was to evaluate the effect of a robust magnetic resonance reconstruction pipeline equipped with a deep convolutional neural network on the overall image quality, in terms of Gibbs artifact reduction, and SNR improvement. Sixteen (16) healthy volunteers enrolled in this study and were imaged at 3T. Representative images of each image series that were reconstructed through the pipeline that leverages a deep learning (DL) algorithm were retrospectively benchmarked against corresponding images reconstructed through a conventional pipeline. DL-reconstructed images showed significant SNR improvements compared to the corresponding conventionally reconstructed images. In addition to that, Gibbs artifacts were effectively eliminated, when the raw data were reconstructed through the DL pipeline. Gibbs artifact reduction was qualitatively assessed by two experienced medical physicists and two experienced radiologists. DL-based reconstruction can lead to an SNR surplus which can be further invested into either higher spatial resolution and thinner slices, or into shorter scan times.