78 research outputs found
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
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
Matrix Completion-Informed Deep Unfolded Equilibrium Models for Self-Supervised k-Space Interpolation in MRI
Recently, regularization model-driven deep learning (DL) has gained
significant attention due to its ability to leverage the potent
representational capabilities of DL while retaining the theoretical guarantees
of regularization models. However, most of these methods are tailored for
supervised learning scenarios that necessitate fully sampled labels, which can
pose challenges in practical MRI applications. To tackle this challenge, we
propose a self-supervised DL approach for accelerated MRI that is theoretically
guaranteed and does not rely on fully sampled labels. Specifically, we achieve
neural network structure regularization by exploiting the inherent structural
low-rankness of the -space data. Simultaneously, we constrain the network
structure to resemble a nonexpansive mapping, ensuring the network's
convergence to a fixed point. Thanks to this well-defined network structure,
this fixed point can completely reconstruct the missing -space data based on
matrix completion theory, even in situations where full-sampled labels are
unavailable. Experiments validate the effectiveness of our proposed method and
demonstrate its superiority over existing self-supervised approaches and
traditional regularization methods, achieving performance comparable to that of
supervised learning methods in certain scenarios
The Fifteenth Marcel Grossmann Meeting
The three volumes of the proceedings of MG15 give a broad view of all aspects of gravitational physics and astrophysics, from mathematical issues to recent observations and experiments. The scientific program of the meeting included 40 morning plenary talks over 6 days, 5 evening popular talks and nearly 100 parallel sessions on 71 topics spread over 4 afternoons. These proceedings are a representative sample of the very many oral and poster presentations made at the meeting.Part A contains plenary and review articles and the contributions from some parallel sessions, while Parts B and C consist of those from the remaining parallel sessions. The contents range from the mathematical foundations of classical and quantum gravitational theories including recent developments in string theory, to precision tests of general relativity including progress towards the detection of gravitational waves, and from supernova cosmology to relativistic astrophysics, including topics such as gamma ray bursts, black hole physics both in our galaxy and in active galactic nuclei in other galaxies, and neutron star, pulsar and white dwarf astrophysics. Parallel sessions touch on dark matter, neutrinos, X-ray sources, astrophysical black holes, neutron stars, white dwarfs, binary systems, radiative transfer, accretion disks, quasars, gamma ray bursts, supernovas, alternative gravitational theories, perturbations of collapsed objects, analog models, black hole thermodynamics, numerical relativity, gravitational lensing, large scale structure, observational cosmology, early universe models and cosmic microwave background anisotropies, inhomogeneous cosmology, inflation, global structure, singularities, chaos, Einstein-Maxwell systems, wormholes, exact solutions of Einstein's equations, gravitational waves, gravitational wave detectors and data analysis, precision gravitational measurements, quantum gravity and loop quantum gravity, quantum cosmology, strings and branes, self-gravitating systems, gamma ray astronomy, cosmic rays and the history of general relativity
SCAN-MUSIC: An Efficient Super-resolution Algorithm for Single Snapshot Wide-band Line Spectral Estimation
We propose an efficient algorithm for reconstructing one-dimensional
wide-band line spectra from their Fourier data in a bounded interval
. While traditional subspace methods such as MUSIC achieve
super-resolution for closely separated line spectra, their computational cost
is high, particularly for wide-band line spectra. To address this issue, we
proposed a scalable algorithm termed SCAN-MUSIC that scans the spectral domain
using a fixed Gaussian window and then reconstructs the line spectra falling
into the window at each time. For line spectra with cluster structure, we
further refine the proposed algorithm using the annihilating filter technique.
Both algorithms can significantly reduce the computational complexity of the
standard MUSIC algorithm with a moderate loss of resolution. Moreover, in terms
of speed, their performance is comparable to the state-of-the-art algorithms,
while being more reliable for reconstructing line spectra with cluster
structure. The algorithms are supplemented with theoretical analyses of error
estimates, sampling complexity, computational complexity, and computational
limit
Bayesian Reconstruction of Magnetic Resonance Images using Gaussian Processes
A central goal of modern magnetic resonance imaging (MRI) is to reduce the
time required to produce high-quality images. Efforts have included hardware
and software innovations such as parallel imaging, compressed sensing, and deep
learning-based reconstruction. Here, we propose and demonstrate a Bayesian
method to build statistical libraries of magnetic resonance (MR) images in
k-space and use these libraries to identify optimal subsampling paths and
reconstruction processes. Specifically, we compute a multivariate normal
distribution based upon Gaussian processes using a publicly available library
of T1-weighted images of healthy brains. We combine this library with
physics-informed envelope functions to only retain meaningful correlations in
k-space. This covariance function is then used to select a series of
ring-shaped subsampling paths using Bayesian optimization such that they
optimally explore space while remaining practically realizable in commercial
MRI systems. Combining optimized subsampling paths found for a range of images,
we compute a generalized sampling path that, when used for novel images,
produces superlative structural similarity and error in comparison to
previously reported reconstruction processes (i.e. 96.3% structural similarity
and <0.003 normalized mean squared error from sampling only 12.5% of the
k-space data). Finally, we use this reconstruction process on pathological data
without retraining to show that reconstructed images are clinically useful for
stroke identification
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
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
- …