35 research outputs found
WKGM: Weight-K-space Generative Model for Parallel Imaging Reconstruction
Deep learning based parallel imaging (PI) has made great progresses in recent
years to accelerate magnetic resonance imaging (MRI). Nevertheless, it still
has some limitations, such as the robustness and flexibility of existing
methods have great deficiency. In this work, we propose a method to explore the
k-space domain learning via robust generative modeling for flexible
calibration-less PI reconstruction, coined weight-k-space generative model
(WKGM). Specifically, WKGM is a generalized k-space domain model, where the
k-space weighting technology and high-dimensional space augmentation design are
efficiently incorporated for score-based generative model training, resulting
in good and robust reconstructions. In addition, WKGM is flexible and thus can
be synergistically combined with various traditional k-space PI models, which
can make full use of the correlation between multi-coil data and
realizecalibration-less PI. Even though our model was trained on only 500
images, experimental results with varying sampling patterns and acceleration
factors demonstrate that WKGM can attain state-of-the-art reconstruction
results with the well-learned k-space generative prior.Comment: 11pages, 12 figure
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
Low-rank and sparse reconstruction in dynamic magnetic resonance imaging via proximal splitting methods
Dynamic magnetic resonance imaging (MRI) consists of collecting multiple MR images in time, resulting in a spatio-temporal signal. However, MRI intrinsically suffers from long acquisition times due to various constraints. This limits the full potential of dynamic MR imaging, such as obtaining high spatial and temporal resolutions which are crucial to observe dynamic phenomena. This dissertation addresses the problem of the reconstruction of dynamic MR images from a limited amount of samples arising from a nuclear magnetic resonance experiment. The term limited can be explained by the approach taken in this thesis to speed up scan time, which is based on violating the Nyquist criterion by skipping measurements that would be normally acquired in a standard MRI procedure. The resulting problem can be classified in the general framework of linear ill-posed inverse problems. This thesis shows how low-dimensional signal models, specifically lowrank and sparsity, can help in the reconstruction of dynamic images from partial measurements. The use of these models are justified by significant developments in signal recovery techniques from partial data that have emerged in recent years in signal processing. The major contributions of this thesis are the development and characterisation of fast and efficient computational tools using convex low-rank and sparse constraints via proximal gradient methods, the development and characterisation of a novel joint reconstruction–separation method via the low-rank plus sparse matrix decomposition technique, and the development and characterisation of low-rank based recovery methods in the context of dynamic parallel MRI. Finally, an additional contribution of this thesis is to formulate the various MR image reconstruction problems in the context of convex optimisation to develop algorithms based on proximal splitting methods
Global k-Space Interpolation for Dynamic MRI Reconstruction using Masked Image Modeling
In dynamic Magnetic Resonance Imaging (MRI), k-space is typically
undersampled due to limited scan time, resulting in aliasing artifacts in the
image domain. Hence, dynamic MR reconstruction requires not only modeling
spatial frequency components in the x and y directions of k-space but also
considering temporal redundancy. Most previous works rely on image-domain
regularizers (priors) to conduct MR reconstruction. In contrast, we focus on
interpolating the undersampled k-space before obtaining images with Fourier
transform. In this work, we connect masked image modeling with k-space
interpolation and propose a novel Transformer-based k-space Global
Interpolation Network, termed k-GIN. Our k-GIN learns global dependencies among
low- and high-frequency components of 2D+t k-space and uses it to interpolate
unsampled data. Further, we propose a novel k-space Iterative Refinement Module
(k-IRM) to enhance the high-frequency components learning. We evaluate our
approach on 92 in-house 2D+t cardiac MR subjects and compare it to MR
reconstruction methods with image-domain regularizers. Experiments show that
our proposed k-space interpolation method quantitatively and qualitatively
outperforms baseline methods. Importantly, the proposed approach achieves
substantially higher robustness and generalizability in cases of
highly-undersampled MR data
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