12 research outputs found
K-space Cold Diffusion: Learning to Reconstruct Accelerated MRI without Noise
Deep learning-based MRI reconstruction models have achieved superior
performance these days. Most recently, diffusion models have shown remarkable
performance in image generation, in-painting, super-resolution, image editing
and more. As a generalized diffusion model, cold diffusion further broadens the
scope and considers models built around arbitrary image transformations such as
blurring, down-sampling, etc. In this paper, we propose a k-space cold
diffusion model that performs image degradation and restoration in k-space
without the need for Gaussian noise. We provide comparisons with multiple deep
learning-based MRI reconstruction models and perform tests on a well-known
large open-source MRI dataset. Our results show that this novel way of
performing degradation can generate high-quality reconstruction images for
accelerated MRI.Comment: 22 pages, 5 figures, 3 table
MRI Field-transfer Reconstruction with Limited Data: Regularization by Neural Style Transfer
Recent works have demonstrated success in MRI reconstruction using deep
learning-based models. However, most reported approaches require training on a
task-specific, large-scale dataset. Regularization by denoising (RED) is a
general pipeline which embeds a denoiser as a prior for image reconstruction.
The potential of RED has been demonstrated for multiple image-related tasks
such as denoising, deblurring and super-resolution. In this work, we propose a
regularization by neural style transfer (RNST) method to further leverage the
priors from the neural transfer and denoising engine. This enables RNST to
reconstruct a high-quality image from a noisy low-quality image with different
image styles and limited data. We validate RNST with clinical MRI scans from
1.5T and 3T and show that RNST can significantly boost image quality. Our
results highlight the capability of the RNST framework for MRI reconstruction
and the potential for reconstruction tasks with limited data.Comment: 30 pages, 8 figures, 2 tables, 1 algorithm char
Complexities of deep learning-based undersampled MR image reconstruction
Artificial intelligence has opened a new path of innovation in magnetic resonance (MR) image reconstruction of undersampled k-space acquisitions. This review offers readers an analysis of the current deep learning-based MR image reconstruction methods. The literature in this field shows exponential growth, both in volume and complexity, as the capabilities of machine learning in solving inverse problems such as image reconstruction are explored. We review the latest developments, aiming to assist researchers and radiologists who are developing new methods or seeking to provide valuable feedback. We shed light on key concepts by exploring the technical intricacies of MR image reconstruction, highlighting the importance of raw datasets and the difficulty of evaluating diagnostic value using standard metrics.Relevance statementIncreasingly complex algorithms output reconstructed images that are difficult to assess for robustness and diagnostic quality, necessitating high-quality datasets and collaboration with radiologists.Key points• Deep learning-based image reconstruction algorithms are increasing both in complexity and performance.• The evaluation of reconstructed images may mistake perceived image quality for diagnostic value.• Collaboration with radiologists is crucial for advancing deep learning technology.</p
Multi-Coil MRI Reconstruction Challenge -- Assessing Brain MRI Reconstruction Models and their Generalizability to Varying Coil Configurations
Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction
methods have the potential to accelerate the MRI acquisition process.
Nevertheless, the scientific community lacks appropriate benchmarks to assess
MRI reconstruction quality of high-resolution brain images, and evaluate how
these proposed algorithms will behave in the presence of small, but expected
data distribution shifts. The Multi-Coil Magnetic Resonance Image (MC-MRI)
Reconstruction Challenge provides a benchmark that aims at addressing these
issues, using a large dataset of high-resolution, three-dimensional,
T1-weighted MRI scans. The challenge has two primary goals: 1) to compare
different MRI reconstruction models on this dataset and 2) to assess the
generalizability of these models to data acquired with a different number of
receiver coils. In this paper, we describe the challenge experimental design,
and summarize the results of a set of baseline and state of the art brain MRI
reconstruction models. We provide relevant comparative information on the
current MRI reconstruction state-of-the-art and highlight the challenges of
obtaining generalizable models that are required prior to broader clinical
adoption. The MC-MRI benchmark data, evaluation code and current challenge
leaderboard are publicly available. They provide an objective performance
assessment for future developments in the field of brain MRI reconstruction
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