2,883 research outputs found
Accelerated Cardiac Diffusion Tensor Imaging Using Joint Low-Rank and Sparsity Constraints
Objective: The purpose of this manuscript is to accelerate cardiac diffusion
tensor imaging (CDTI) by integrating low-rankness and compressed sensing.
Methods: Diffusion-weighted images exhibit both transform sparsity and
low-rankness. These properties can jointly be exploited to accelerate CDTI,
especially when a phase map is applied to correct for the phase inconsistency
across diffusion directions, thereby enhancing low-rankness. The proposed
method is evaluated both ex vivo and in vivo, and is compared to methods using
either a low-rank or sparsity constraint alone. Results: Compared to using a
low-rank or sparsity constraint alone, the proposed method preserves more
accurate helix angle features, the transmural continuum across the myocardium
wall, and mean diffusivity at higher acceleration, while yielding significantly
lower bias and higher intraclass correlation coefficient. Conclusion:
Low-rankness and compressed sensing together facilitate acceleration for both
ex vivo and in vivo CDTI, improving reconstruction accuracy compared to
employing either constraint alone. Significance: Compared to previous methods
for accelerating CDTI, the proposed method has the potential to reach higher
acceleration while preserving myofiber architecture features which may allow
more spatial coverage, higher spatial resolution and shorter temporal footprint
in the future.Comment: 11 pages, 16 figures, published on IEEE Transactions on Biomedical
Engineerin
Free-Breathing Myocardial T1 Mapping using Inversion-Recovery Radial FLASH and Motion-Resolved Model-Based Reconstruction
Purpose: To develop a free-breathing myocardial T1 mapping technique using
inversion-recovery (IR) radial fast low-angle shot (FLASH) and calibrationless
motion-resolved model-based reconstruction. Methods: Free-running
(free-breathing, retrospective cardiac gating) IR radial FLASH is used for data
acquisition at 3T. First, to reduce the waiting time between inversions, an
analytical formula is derived that takes the incomplete T1 recovery into
account for an accurate T1 calculation. Second, the respiratory motion signal
is estimated from the k-space center of the contrast varying acquisition using
an adapted singular spectrum analysis (SSA-FARY) technique. Third, a
motion-resolved model-based reconstruction is used to estimate both parameter
and coil sensitivity maps directly from the sorted k-space data. Thus,
spatio-temporal total variation, in addition to the spatial sparsity
constraints, can be directly applied to the parameter maps. Validations are
performed on an experimental phantom, eleven human subjects, and a young
landrace pig with myocardial infarction. Results: In comparison to an IR
spin-echo reference, phantom results confirm good T1 accuracy, when reducing
the waiting time from five seconds to one second using the new correction. The
motion-resolved model-based reconstruction further improves T1 precision
compared to the spatial regularization-only reconstruction. Aside from showing
that a reliable respiratory motion signal can be estimated using modified
SSA-FARY, in vivo studies demonstrate that dynamic myocardial T1 maps can be
obtained within two minutes with good precision and repeatability. Conclusion:
Motion-resolved myocardial T1 mapping during free-breathing with good accuracy,
precision and repeatability can be achieved by combining inversion-recovery
radial FLASH, self-gating and a calibrationless motion-resolved model-based
reconstruction.Comment: Part of this work has been presented at the ISMRM Annual Conference
2021 (Virtual), submitted to Magnetic Resonance in Medicin
Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI
Parallel MRI is a fast imaging technique that enables the acquisition of
highly resolved images in space or/and in time. The performance of parallel
imaging strongly depends on the reconstruction algorithm, which can proceed
either in the original k-space (GRAPPA, SMASH) or in the image domain
(SENSE-like methods). To improve the performance of the widely used SENSE
algorithm, 2D- or slice-specific regularization in the wavelet domain has been
deeply investigated. In this paper, we extend this approach using 3D-wavelet
representations in order to handle all slices together and address
reconstruction artifacts which propagate across adjacent slices. The gain
induced by such extension (3D-Unconstrained Wavelet Regularized -SENSE:
3D-UWR-SENSE) is validated on anatomical image reconstruction where no temporal
acquisition is considered. Another important extension accounts for temporal
correlations that exist between successive scans in functional MRI (fMRI). In
addition to the case of 2D+t acquisition schemes addressed by some other
methods like kt-FOCUSS, our approach allows us to deal with 3D+t acquisition
schemes which are widely used in neuroimaging. The resulting 3D-UWR-SENSE and
4D-UWR-SENSE reconstruction schemes are fully unsupervised in the sense that
all regularization parameters are estimated in the maximum likelihood sense on
a reference scan. The gain induced by such extensions is illustrated on both
anatomical and functional image reconstruction, and also measured in terms of
statistical sensitivity for the 4D-UWR-SENSE approach during a fast
event-related fMRI protocol. Our 4D-UWR-SENSE algorithm outperforms the SENSE
reconstruction at the subject and group levels (15 subjects) for different
contrasts of interest (eg, motor or computation tasks) and using different
parallel acceleration factors (R=2 and R=4) on 2x2x3mm3 EPI images.Comment: arXiv admin note: substantial text overlap with arXiv:1103.353
Advanced methods for mapping the radiofrequency magnetic fields in MRI
As MRI systems have increased in static magnetic field strength, the radiofrequency
(RF) fields that are used for magnetisation excitation and signal reception have become
significantly less uniform. This can lead to image artifacts and errors when performing
quantitative MRI. A further complication arises if the RF fields vary substantially in time.
In the first part of this investigation temporal variations caused by respiration were
explored on a 3T scanner. It was found that fractional changes in transmit field
amplitude between inhalation and expiration ranged from 1% to 14% in the region of
the liver in a small group of normal subjects. This observation motivated the
development of a pulse sequence and reconstruction method to allow dynamic
observation of the transmit field throughout the respiratory cycle. However, the
proposed method was unsuccessful due to the inherently time-consuming nature of
transmit field mapping sequences.
This prompted the development of a novel data reconstruction method to allow the
acceleration of transmit field mapping sequences. The proposed technique posed the RF
field reconstruction as a nonlinear least-squares optimisation problem, exploiting the
fact that the fields vary smoothly. It was shown that this approach was superior to
standard reconstruction approaches.
The final component of this thesis presents a unified approach to RF field calibration.
The proposed method uses all measured data to estimate both transmit and receive
sensitivities, whilst simultaneously insisting that they are smooth functions of space.
The resulting maps are robust to both noise and imperfections in regions of low signal
Doctor of Philosophy
dissertationDynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is a powerful tool to detect cardiac diseases and tumors, and both spatial resolution and temporal resolution are important for disease detection. Sampling less in each time frame and applying sophisticated reconstruction methods to overcome image degradations is a common strategy in the literature. In this thesis, temporal TV constrained reconstruction that was successfully applied to DCE myocardial perfusion imaging by our group was extended to three-dimensional (3D) DCE breast and 3D myocardial perfusion imaging, and the extension includes different forms of constraint terms and various sampling patterns. We also explored some other popular reconstruction algorithms from a theoretical level and showed that they can be included in a unified framework. Current 3D Cartesian DCE breast tumor imaging is limited in spatiotemporal resolution as high temporal resolution is desired to track the contrast enhancement curves, and high spatial resolution is desired to discern tumor morphology. Here temporal TV constrained reconstruction was extended and different forms of temporal TV constraints were compared on 3D Cartesian DCE breast tumor data with simulated undersampling. Kinetic parameters analysis was used to validate the methods
Doctor of Philosophy
dissertationDiffusion tensor MRI (DT-MRI or DTI) has been proven useful for characterizing biological tissue microstructure, with the majority of DTI studies having been performed previously in the brain. Other studies have shown that changes in DTI parameters are detectable in the presence of cardiac pathology, recovery, and development, and provide insight into the microstructural mechanisms of these processes. However, the technical challenges of implementing cardiac DTI in vivo, including prohibitive scan times inherent to DTI and measuring small-scale diffusion in the beating heart, have limited its widespread usage. This research aims to address these technical challenges by: (1) formulating a model-based reconstruction algorithm to accurately estimate DTI parameters directly from fewer MRI measurements and (2) designing novel diffusion encoding MRI pulse sequences that compensate for the higher-order motion of the beating heart. The model-based reconstruction method was tested on undersampled DTI data and its performance was compared against other state-of-the-art reconstruction algorithms. Model-based reconstruction was shown to produce DTI parameter maps with less blurring and noise and to estimate global DTI parameters more accurately than alternative methods. Through numerical simulations and experimental demonstrations in live rats, higher-order motion compensated diffusion-encoding was shown to successfully eliminate signal loss due to motion, which in turn produced data of sufficient quality to accurately estimate DTI parameters, such as fiber helix angle. Ultimately, the model-based reconstruction and higher-order motion compensation methods were combined to characterize changes in the cardiac microstructure in a rat model with inducible arterial hypertension in order to demonstrate the ability of cardiac DTI to detect pathological changes in living myocardium
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