1,230 research outputs found
High-efficient Bloch simulation of magnetic resonance imaging sequences based on deep learning
Objective: Bloch simulation constitutes an essential part of magnetic
resonance imaging (MRI) development. However, even with the graphics processing
unit (GPU) acceleration, the heavy computational load remains a major
challenge, especially in large-scale, high-accuracy simulation scenarios. This
work aims to develop a deep learning-based simulator to accelerate Bloch
simulation. Approach: The simulator model, called Simu-Net, is based on an
end-to-end convolutional neural network and is trained with synthetic data
generated by traditional Bloch simulation. It uses dynamic convolution to fuse
spatial and physical information with different dimensions and introduces
position encoding templates to achieve position-specific labeling and overcome
the receptive field limitation of the convolutional network. Main Results:
Compared with mainstream GPU-based MRI simulation software, Simu-Net
successfully accelerates simulations by hundreds of times in both traditional
and advanced MRI pulse sequences. The accuracy and robustness of the proposed
framework were verified qualitatively and quantitatively. Besides, the trained
Simu-Net was applied to generate sufficient customized training samples for
deep learning-based T2 mapping and comparable results to conventional methods
were obtained in the human brain. Significance: As a proof-of-concept work,
Simu-Net shows the potential to apply deep learning for rapidly approximating
the forward physical process of MRI and may increase the efficiency of Bloch
simulation for optimization of MRI pulse sequences and deep learning-based
methods.Comment: 18 pages, 8 figure
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
High-efficient deep learning-based DTI reconstruction with flexible diffusion gradient encoding scheme
Purpose: To develop and evaluate a novel dynamic-convolution-based method
called FlexDTI for high-efficient diffusion tensor reconstruction with flexible
diffusion encoding gradient schemes. Methods: FlexDTI was developed to achieve
high-quality DTI parametric mapping with flexible number and directions of
diffusion encoding gradients. The proposed method used dynamic convolution
kernels to embed diffusion gradient direction information into feature maps of
the corresponding diffusion signal. Besides, our method realized the
generalization of a flexible number of diffusion gradient directions by setting
the maximum number of input channels of the network. The network was trained
and tested using data sets from the Human Connectome Project and a local
hospital. Results from FlexDTI and other advanced tensor parameter estimation
methods were compared. Results: Compared to other methods, FlexDTI successfully
achieves high-quality diffusion tensor-derived variables even if the number and
directions of diffusion encoding gradients are variable. It increases peak
signal-to-noise ratio (PSNR) by about 10 dB on Fractional Anisotropy (FA) and
Mean Diffusivity (MD), compared with the state-of-the-art deep learning method
with flexible diffusion encoding gradient schemes. Conclusion: FlexDTI can well
learn diffusion gradient direction information to achieve generalized DTI
reconstruction with flexible diffusion gradient schemes. Both flexibility and
reconstruction quality can be taken into account in this network.Comment: 11 pages,6 figures,3 table
Motion robust acquisition and reconstruction of quantitative T2* maps in the developing brain
The goal of the research presented in this thesis was to develop methods for quantitative T2* mapping of the developing brain. Brain maturation in the early period of life involves complex structural and physiological changes caused by synaptogenesis, myelination and growth of cells. Molecular structures and biological processes give rise to varying levels of T2* relaxation time, which is an inherent contrast mechanism in magnetic resonance imaging. The knowledge of T2* relaxation times in the brain can thus help with evaluation of pathology by establishing its normative values in the key areas of the brain. T2* relaxation values are a valuable biomarker for myelin microstructure and iron concentration, as well as an important guide towards achievement of optimal fMRI contrast. However, fetal MR imaging is a significant step up from neonatal or adult MR imaging due to the complexity of the acquisition and reconstruction techniques that are required to provide high quality artifact-free images in the presence of maternal respiration and unpredictable fetal motion. The first contribution of this thesis, described in Chapter 4, presents a novel acquisition method for measurement of fetal brain T2* values. At the time of publication, this was the first study of fetal brain T2* values. Single shot multi-echo gradient echo EPI was proposed as a rapid method for measuring fetal T2* values by effectively freezing intra-slice motion. The study concluded that fetal T2* values are higher than those previously reported for pre-term neonates and decline with a consistent trend across gestational age. The data also suggested that longer than usual echo times or direct T2* measurement should be considered when performing fetal fMRI in order to reach optimal BOLD sensitivity. For the second contribution, described in Chapter 5, measurements were extended to a higher field strength of 3T and reported, for the first time, both for fetal and neonatal subjects at this field strength. The technical contribution of this work is a fully automatic segmentation framework that propagates brain tissue labels onto the acquired T2* maps without the need for manual intervention. The third contribution, described in Chapter 6, proposed a new method for performing 3D fetal brain reconstruction where the available data is sparse and is therefore limited in the use of current state of the art techniques for 3D brain reconstruction in the presence of motion. To enable a high resolution reconstruction, a generative adversarial network was trained to perform image to image translation between T2 weighted and T2* weighted data. Translated images could then be served as a prior for slice alignment and super resolution reconstruction of 3D brain image.Open Acces
Physics-based Reconstruction Methods for Magnetic Resonance Imaging
Conventional Magnetic Resonance Imaging (MRI) is hampered by long scan times
and only qualitative image contrasts that prohibit a direct comparison between
different systems. To address these limitations, model-based reconstructions
explicitly model the physical laws that govern the MRI signal generation. By
formulating image reconstruction as an inverse problem, quantitative maps of
the underlying physical parameters can then be extracted directly from
efficiently acquired k-space signals without intermediate image reconstruction
-- addressing both shortcomings of conventional MRI at the same time. This
review will discuss basic concepts of model-based reconstructions and report
about our experience in developing several model-based methods over the last
decade using selected examples that are provided complete with data and code.Comment: 8 figures, review accepted to Philos. Trans. R. Soc.
Blip-Up Blip-Down Circular EPI (BUDA-cEPI) for Distortion-Free dMRI with Rapid Unrolled Deep Learning Reconstruction
Purpose: We implemented the blip-up, blip-down circular echo planar imaging
(BUDA-cEPI) sequence with readout and phase partial Fourier to reduced
off-resonance effect and T2* blurring. BUDA-cEPI reconstruction with S-based
low-rank modeling of local k-space neighborhoods (S-LORAKS) is shown to be
effective at reconstructing the highly under-sampled BUDA-cEPI data, but it is
computationally intensive. Thus, we developed an ML-based reconstruction
technique termed "BUDA-cEPI RUN-UP" to enable fast reconstruction.
Methods: BUDA-cEPI RUN-UP - a model-based framework that incorporates
off-resonance and eddy current effects was unrolled through an artificial
neural network with only six gradient updates. The unrolled network alternates
between data consistency (i.e., forward BUDA-cEPI and its adjoint) and
regularization steps where U-Net plays a role as the regularizer. To handle the
partial Fourier effect, the virtual coil concept was also incorporated into the
reconstruction to effectively take advantage of the smooth phase prior, and
trained to predict the ground-truth images obtained by BUDA-cEPI with S-LORAKS.
Results: BUDA-cEPI with S-LORAKS reconstruction enabled the management of
off-resonance, partial Fourier, and residual aliasing artifacts. However, the
reconstruction time is approximately 225 seconds per slice, which may not be
practical in a clinical setting. In contrast, the proposed BUDA-cEPI RUN-UP
yielded similar results to BUDA-cEPI with S-LORAKS, with less than a 5%
normalized root mean square error detected, while the reconstruction time is
approximately 3 seconds.
Conclusion: BUDA-cEPI RUN-UP was shown to reduce the reconstruction time by
~88x when compared to the state-of-the-art technique, while preserving imaging
details as demonstrated through DTI application.Comment: Number: Figures: 8 Tables: 3 References: 7
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