11 research outputs found
AliasNet: Alias Artefact Suppression Network for Accelerated Phase-Encode MRI
Sparse reconstruction is an important aspect of MRI, helping to reduce
acquisition time and improve spatial-temporal resolution. Popular methods are
based mostly on compressed sensing (CS), which relies on the random sampling of
k-space to produce incoherent (noise-like) artefacts. Due to hardware
constraints, 1D Cartesian phase-encode under-sampling schemes are popular for
2D CS-MRI. However, 1D under-sampling limits 2D incoherence between
measurements, yielding structured aliasing artefacts (ghosts) that may be
difficult to remove assuming a 2D sparsity model. Reconstruction algorithms
typically deploy direction-insensitive 2D regularisation for these
direction-associated artefacts. Recognising that phase-encode artefacts can be
separated into contiguous 1D signals, we develop two decoupling techniques that
enable explicit 1D regularisation and leverage the excellent 1D incoherence
characteristics. We also derive a combined 1D + 2D reconstruction technique
that takes advantage of spatial relationships within the image. Experiments
conducted on retrospectively under-sampled brain and knee data demonstrate that
combination of the proposed 1D AliasNet modules with existing 2D deep learned
(DL) recovery techniques leads to an improvement in image quality. We also find
AliasNet enables a superior scaling of performance compared to increasing the
size of the original 2D network layers. AliasNet therefore improves the
regularisation of aliasing artefacts arising from phase-encode under-sampling,
by tailoring the network architecture to account for their expected appearance.
The proposed 1D + 2D approach is compatible with any existing 2D DL recovery
technique deployed for this application
Learning Apparent Diffusion Coefficient Maps from Accelerated Radial k-Space Diffusion-Weighted MRI in Mice using a Deep CNN-Transformer Model
Purpose: To accelerate radially sampled diffusion weighted spin-echo
(Rad-DW-SE) acquisition method for generating high quality apparent diffusion
coefficient (ADC) maps. Methods: A deep learning method was developed to
generate accurate ADC maps from accelerated DWI data acquired with the
Rad-DW-SE method. The deep learning method integrates convolutional neural
networks (CNNs) with vision transformers to generate high quality ADC maps from
accelerated DWI data, regularized by a monoexponential ADC model fitting term.
A model was trained on DWI data of 147 mice and evaluated on DWI data of 36
mice, with acceleration factors of 4x and 8x compared to the original
acquisition parameters. We have made our code publicly available at GitHub:
https://github.com/ymli39/DeepADC-Net-Learning-Apparent-Diffusion-Coefficient-Maps,
and our dataset can be downloaded at
https://pennpancreaticcancerimagingresource.github.io/data.html. Results:
Ablation studies and experimental results have demonstrated that the proposed
deep learning model generates higher quality ADC maps from accelerated DWI data
than alternative deep learning methods under comparison when their performance
is quantified in whole images as well as in regions of interest, including
tumors, kidneys, and muscles. Conclusions: The deep learning method with
integrated CNNs and transformers provides an effective means to accurately
compute ADC maps from accelerated DWI data acquired with the Rad-DW-SE method.Comment: Accepted by Magnetic Resonance in Medicin
Machine learning in Magnetic Resonance Imaging: Image reconstruction.
Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis, management and monitoring of many diseases. However, it is an inherently slow imaging technique. Over the last 20Â years, parallel imaging, temporal encoding and compressed sensing have enabled substantial speed-ups in the acquisition of MRI data, by accurately recovering missing lines of k-space data. However, clinical uptake of vastly accelerated acquisitions has been limited, in particular in compressed sensing, due to the time-consuming nature of the reconstructions and unnatural looking images. Following the success of machine learning in a wide range of imaging tasks, there has been a recent explosion in the use of machine learning in the field of MRI image reconstruction. A wide range of approaches have been proposed, which can be applied in k-space and/or image-space. Promising results have been demonstrated from a range of methods, enabling natural looking images and rapid computation. In this review article we summarize the current machine learning approaches used in MRI reconstruction, discuss their drawbacks, clinical applications, and current trends
Knowledge-driven deep learning for fast MR imaging: undersampled MR image reconstruction from supervised to un-supervised learning
Deep learning (DL) has emerged as a leading approach in accelerating MR
imaging. It employs deep neural networks to extract knowledge from available
datasets and then applies the trained networks to reconstruct accurate images
from limited measurements. Unlike natural image restoration problems, MR
imaging involves physics-based imaging processes, unique data properties, and
diverse imaging tasks. This domain knowledge needs to be integrated with
data-driven approaches. Our review will introduce the significant challenges
faced by such knowledge-driven DL approaches in the context of fast MR imaging
along with several notable solutions, which include learning neural networks
and addressing different imaging application scenarios. The traits and trends
of these techniques have also been given which have shifted from supervised
learning to semi-supervised learning, and finally, to unsupervised learning
methods. In addition, MR vendors' choices of DL reconstruction have been
provided along with some discussions on open questions and future directions,
which are critical for the reliable imaging systems.Comment: 46 pages, 5figures, 1 tabl