ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning

02/25/2021
by   Soumick Chatterjee, et al.
0

Diffusion-weighted magnetic resonance imaging (DW-MRI) can be used to characterise the microstructure of the nervous tissue, e.g. to delineate brain white matter connections in a non-invasive manner via fibre tracking. Magnetic Resonance Imaging (MRI) in high spatial resolution would play an important role in visualising such fibre tracts in a superior manner. However, obtaining an image of such resolution comes at the expense of longer scan time. Longer scan time can be associated with the increase of motion artefacts, due to the patient's psychological and physical conditions. Single Image Super-Resolution (SISR), a technique aimed to obtain high-resolution (HR) details from one single low-resolution (LR) input image, achieved with Deep Learning, is the focus of this study. Compared to interpolation techniques or sparse-coding algorithms, deep learning extracts prior knowledge from big datasets and produces superior MRI images from the low-resolution counterparts. In this research, a deep learning based super-resolution technique is proposed and has been applied for DW-MRI. Images from the IXI dataset have been used as the ground-truth and were artificially downsampled to simulate the low-resolution images. The proposed method has shown statistically significant improvement over the baselines and achieved an SSIM of 0.913±0.045.

READ FULL TEXT

page 1

page 3

page 4

01/08/2018

Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks

Magnetic resonance image (MRI) in high spatial resolution provides detai...
02/17/2021

SRDTI: Deep learning-based super-resolution for diffusion tensor MRI

High-resolution diffusion tensor imaging (DTI) is beneficial for probing...
02/10/2022

DDoS-UNet: Incorporating temporal information using Dynamic Dual-channel UNet for enhancing super-resolution of dynamic MRI

Magnetic resonance imaging (MRI) provides high spatial resolution and ex...
03/04/2018

Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network

High-resolution (HR) magnetic resonance images (MRI) provide detailed an...
07/12/2019

Coupled-Projection Residual Network for MRI Super-Resolution

Magnetic Resonance Imaging(MRI) has been widely used in clinical applica...
02/28/2017

Context-Sensitive Super-Resolution for Fast Fetal Magnetic Resonance Imaging

3D Magnetic Resonance Imaging (MRI) is often a trade-off between fast bu...
11/28/2021

3D High-Quality Magnetic Resonance Image Restoration in Clinics Using Deep Learning

Shortening acquisition time and reducing the motion-artifact are two of ...