898 research outputs found
Self-Learned Kernel Low Rank Approach TO Accelerated High Resolution 3D Diffusion MRI
Diffusion Magnetic Resonance Imaging (dMRI) is a promising method to analyze the subtle changes in the tissue structure. However, the lengthy acquisition time is a major limitation in the clinical application of dMRI. Different image acquisition techniques such as parallel imaging, compressed sensing, has shortened the prolonged acquisition time but creating high-resolution 3D dMRI slices still requires a significant amount of time. In this study, we have shown that high resolution 3D dMRI can be reconstructed from the highly undersampled k-space and q-space data using a Kernel Low Rank method. Our proposed method has outperformed the conventional CS methods in terms of both image quality and diffusion maps constructed from the diffusion-weighted images
Self-Learned Kernel Low Rank Approach TO Accelerated High Resolution 3D Diffusion MRI
Diffusion Magnetic Resonance Imaging (dMRI) is a promising method to analyze the subtle changes in the tissue structure. However, the lengthy acquisition time is a major limitation in the clinical application of dMRI. Different image acquisition techniques such as parallel imaging, compressed sensing, has shortened the prolonged acquisition time but creating high-resolution 3D dMRI slices still requires a significant amount of time. In this study, we have shown that high resolution 3D dMRI can be reconstructed from the highly undersampled k-space and q-space data using a Kernel Low Rank method. Our proposed method has outperformed the conventional CS methods in terms of both image quality and diffusion maps constructed from the diffusion-weighted images
Structured low-rank methods for robust 3D multi-shot EPI
Magnetic resonance imaging (MRI) has inherently slow acquisition speed, and Echo-Planar Imaging (EPI), as an efficient acquisition scheme, has been widely used in functional magnetic resonance imaging (fMRI) where an image series with high temporal resolution is needed to measure neuronal activity. Recently, 3D multi-shot EPI which samples data from an entire 3D volume with repeated shots has been drawing growing interest for fMRI with its high isotropic spatial resolution, particularly at ultra-high fields. However, compared to single-shot EPI, multi-shot EPI is sensitive to any inter-shot instabilities, e.g., subject movement and even physiologically induced field fluctuations. These inter-shot inconsistencies can greatly negate the theoretical benefits of 3D multi-shot EPI over conventional 2D multi-slice acquisitions.
Structured low-rank image reconstruction which regularises under-sampled image reconstruction by exploiting the linear dependencies in MRI data has been successfully demonstrated in a variety of applications. In this thesis, a structured low-rank reconstruction method is optimised for 3D multi-shot EPI imaging together with a dedicated sampling pattern termed seg-CAIPI, in order to enhance the robustness to physiological fluctuations and improve the temporal stability of 3D multi-shot EPI for fMRI at 7T. Moreover, a motion compensated structured low-rank reconstruction framework is also presented for robust 3D multi-shot EPI which further takes into account inter-shot instabilities due to bulk motion. Lastly, this thesis also investigates into the improvement of structured low-rank reconstruction from an algorithmic perspective and presents the locally structured low-rank reconstruction scheme
Self-navigated 3D diffusion MRI using an optimized CAIPI sampling and structured low-rank reconstruction
3D multi-slab acquisitions are an appealing approach for diffusion MRI
because they are compatible with the imaging regime delivering optimal SNR
efficiency. In conventional 3D multi-slab imaging, shot-to-shot phase
variations caused by motion pose challenges due to the use of multi-shot
k-space acquisition. Navigator acquisition after each imaging echo is typically
employed to correct phase variations, which prolongs scan time and increases
the specific absorption rate (SAR). The aim of this study is to develop a
highly efficient, self-navigated method to correct for phase variations in 3D
multi-slab diffusion MRI without explicitly acquiring navigators. The sampling
of each shot is carefully designed to intersect with the central kz plane of
each slab, and the multi-shot sampling is optimized for self-navigation
performance while retaining decent reconstruction quality. The central kz
intersections from all shots are jointly used to reconstruct a 2D phase map for
each shot using a structured low-rank constrained reconstruction that leverages
the redundancy in shot and coil dimensions. The phase maps are used to
eliminate the shot-to-shot phase inconsistency in the final 3D multi-shot
reconstruction. We demonstrate the method's efficacy using retrospective
simulations and prospectively acquired in-vivo experiments at 1.22 mm and 1.09
mm isotropic resolutions. Compared to conventional navigated 3D multi-slab
imaging, the proposed self-navigated method achieves comparable image quality
while shortening the scan time by 31.7% and improving the SNR efficiency by
15.5%. The proposed method produces comparable quality of DTI and white matter
tractography to conventional navigated 3D multi-slab acquisition with a much
shorter scan time.Comment: 10 pages, 11 figures, 2 tables. This work has been submitted to the
IEEE for possible publication. Copyright may be transferred without notice,
after which this version may no longer be accessibl
Low-rank Tensor Assisted K-space Generative Model for Parallel Imaging Reconstruction
Although recent deep learning methods, especially generative models, have
shown good performance in fast magnetic resonance imaging, there is still much
room for improvement in high-dimensional generation. Considering that internal
dimensions in score-based generative models have a critical impact on
estimating the gradient of the data distribution, we present a new idea,
low-rank tensor assisted k-space generative model (LR-KGM), for parallel
imaging reconstruction. This means that we transform original prior information
into high-dimensional prior information for learning. More specifically, the
multi-channel data is constructed into a large Hankel matrix and the matrix is
subsequently folded into tensor for prior learning. In the testing phase, the
low-rank rotation strategy is utilized to impose low-rank constraints on tensor
output of the generative network. Furthermore, we alternately use traditional
generative iterations and low-rank high-dimensional tensor iterations for
reconstruction. Experimental comparisons with the state-of-the-arts
demonstrated that the proposed LR-KGM method achieved better performance
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