Deblurring for spiral real-time MRI using convolutional neural networks.

ABSTRACT:

Purpose

To develop and evaluate a fast and effective method for deblurring spiral real-time MRI (RT-MRI) using convolutional neural networks.

Methods

We demonstrate a 3-layer residual convolutional neural networks to correct image domain off-resonance artifacts in speech production spiral RT-MRI without the knowledge of field maps. The architecture is motivated by the traditional deblurring approaches. Spatially varying off-resonance blur is synthetically generated by using discrete object approximation and field maps with data augmentation from a large database of 2D human speech production RT-MRI. The effect of off-resonance range, shift-invariance of blur, and readout durations on deblurring performance are investigated. The proposed method is validated using synthetic an

SUBMITTER: Lim Y 

PROVIDER: S-EPMC7722023 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature