11 research outputs found
APIR-Net: Autocalibrated Parallel Imaging Reconstruction using a Neural Network
Deep learning has been successfully demonstrated in MRI reconstruction of
accelerated acquisitions. However, its dependence on representative training
data limits the application across different contrasts, anatomies, or image
sizes. To address this limitation, we propose an unsupervised, auto-calibrated
k-space completion method, based on a uniquely designed neural network that
reconstructs the full k-space from an undersampled k-space, exploiting the
redundancy among the multiple channels in the receive coil in a parallel
imaging acquisition. To achieve this, contrary to common convolutional network
approaches, the proposed network has a decreasing number of feature maps of
constant size. In contrast to conventional parallel imaging methods such as
GRAPPA that estimate the prediction kernel from the fully sampled
autocalibration signals in a linear way, our method is able to learn nonlinear
relations between sampled and unsampled positions in k-space. The proposed
method was compared to the start-of-the-art ESPIRiT and RAKI methods in terms
of noise amplification and visual image quality in both phantom and in-vivo
experiments. The experiments indicate that APIR-Net provides a promising
alternative to the conventional parallel imaging methods, and results in
improved image quality especially for low SNR acquisitions.Comment: To appear in the proceedings of MICCAI 2019 Workshop Machine Learning
for Medical Image Reconstructio
Active MR k-space Sampling with Reinforcement Learning
Deep learning approaches have recently shown great promise in accelerating
magnetic resonance image (MRI) acquisition. The majority of existing work have
focused on designing better reconstruction models given a pre-determined
acquisition trajectory, ignoring the question of trajectory optimization. In
this paper, we focus on learning acquisition trajectories given a fixed image
reconstruction model. We formulate the problem as a sequential decision process
and propose the use of reinforcement learning to solve it. Experiments on a
large scale public MRI dataset of knees show that our proposed models
significantly outperform the state-of-the-art in active MRI acquisition, over a
large range of acceleration factors.Comment: Presented at the 23rd International Conference on Medical Image
Computing and Computer Assisted Intervention, MICCAI 202
Uncertainty Estimation using the Local Lipschitz for Deep Learning Image Reconstruction Models
The use of supervised deep neural network approaches has been investigated to
solve inverse problems in all domains, especially radiology where imaging
technologies are at the heart of diagnostics. However, in deployment, these
models are exposed to input distributions that are widely shifted from training
data, due in part to data biases or drifts. It becomes crucial to know whether
a given input lies outside the training data distribution before relying on the
reconstruction for diagnosis. The goal of this work is three-fold: (i)
demonstrate use of the local Lipshitz value as an uncertainty estimation
threshold for determining suitable performance, (ii) provide method for
identifying out-of-distribution (OOD) images where the model may not have
generalized, and (iii) use the local Lipschitz values to guide proper data
augmentation through identifying false positives and decrease epistemic
uncertainty. We provide results for both MRI reconstruction and CT sparse view
to full view reconstruction using AUTOMAP and UNET architectures due to it
being pertinent in the medical domain that reconstructed images remain
diagnostically accurate
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 20 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
Recurrent inference machines for reconstructing heterogeneous MRI data
Deep learning allows for accelerated magnetic resonance image (MRI) reconstruction, thereby shortening measurement times. Rather than using sparsifying transforms, a prerequisite in Compressed Sensing (CS), suitable MRI prior distributions are learned from data. In clinical practice, both the underlying anatomy as well as image acquisition settings vary. For this reason, deep neural networks must be able to reapply what they learn across different measurement conditions. We propose to use Recurrent Inference Machines (RIM) as a framework for accelerated MRI reconstruction. RIMs solve inverse problems in an iterative and recurrent inference procedure by repeatedly reassessing the state of their reconstruction, and subsequently making incremental adjustments to it in accordance with the forward model of accelerated MRI. RIMs learn the inferential process of reconstructing a given signal, which, in combination with the use of internal states as part of their recurrent architecture, makes them less dependent on learning the features pertaining to the source of the signal itself. This gives RIMs a low tendency to overfit, and a high capacity to generalize to unseen types of data. We demonstrate this ability with respect to anatomy by reconstructing brain and knee scans, as well as other MRI acquisition settings, by reconstructing scans of different contrast and resolution, at different field strength, subjected to varying acceleration levels. We show that RIMs outperform CS not only with respect to quality metrics, but also according to a rating given by an experienced neuroradiologist in a double blinded experiment. Finally, we show with qualitative results that our model can be applied to prospectively under-sampled raw data, as acquired by pre-installed acquisition protocols
Improved 3D MR Image Acquisition and Processing in Congenital Heart Disease
Congenital heart disease (CHD) is the most common type of birth defect, affecting about 1% of the population. MRI is an essential tool in the assessment of CHD, including diagnosis, intervention planning and follow-up. Three-dimensional MRI can provide particularly rich visualization and information. However, it is often complicated by long scan times, cardiorespiratory motion, injection of contrast agents, and complex and time-consuming postprocessing. This thesis comprises four pieces of work that attempt to respond to some of these challenges.
The first piece of work aims to enable fast acquisition of 3D time-resolved cardiac imaging during free breathing. Rapid imaging was achieved using an efficient spiral sequence and a sparse parallel imaging reconstruction. The feasibility of this approach was demonstrated on a population of 10 patients with CHD, and areas of improvement were identified.
The second piece of work is an integrated software tool designed to simplify and accelerate the development of machine learning (ML) applications in MRI research. It also exploits the strengths of recently developed ML libraries for efficient MR image reconstruction and processing.
The third piece of work aims to reduce contrast dose in contrast-enhanced MR angiography (MRA). This would reduce risks and costs associated with contrast agents. A deep learning-based contrast enhancement technique was developed and shown to improve image quality in real low-dose MRA in a population of 40 children and adults with CHD.
The fourth and final piece of work aims to simplify the creation of computational models for hemodynamic assessment of the great arteries. A deep learning technique for 3D segmentation of the aorta and the pulmonary arteries was developed and shown to enable accurate calculation of clinically relevant biomarkers in a population of 10 patients with CHD