7,185 research outputs found
Real-time Assessment of Right and Left Ventricular Volumes and Function in Children Using High Spatiotemporal Resolution Spiral bSSFP with Compressed Sensing
Background: Real-time (RT) assessment of ventricular volumes and function
enables data acquisition during free-breathing. However, in children the
requirement for high spatiotemporal resolution requires accelerated imaging
techniques. In this study, we implemented a novel RT bSSFP spiral sequence
reconstructed using Compressed Sensing (CS) and validated it against the
breath-hold (BH) reference standard for assessment of ventricular volumes in
children with heart disease.
Methods: Data was acquired in 60 children. Qualitative image scoring and
evaluation of ventricular volumes was performed by 3 clinical cardiac MR
specialists. 30 cases were reassessed for intra-observer variability, and the
other 30 cases for inter-observer variability.
Results: Spiral RT images were of good quality, however qualitative scores
reflected more residual artefact than standard BH images and slightly lower
edge definition. Quantification of Left Ventricular (LV) and Right Ventricular
(RV) metrics showed excellent correlation between the techniques with narrow
limits of agreement. However, we observed small but statistically significant
overestimation of LV end-diastolic volume, underestimation of LV end-systolic
volume, as well as a small overestimation of RV stroke volume and ejection
fraction using the RT imaging technique. No difference in inter-observer or
intra-observer variability were observed between the BH and RT sequences.
Conclusions: Real-time bSSFP imaging using spiral trajectories combined with
a compressed sensing reconstruction is feasible. The main benefit is that it
can be acquired during free breathing. However, another important secondary
benefit is that a whole ventricular stack can be acquired in ~20 seconds, as
opposed to ~6 minutes for standard BH imaging. Thus, this technique holds the
potential to significantly shorten MR scan times in children
Frequency-splitting Dynamic MRI Reconstruction using Multi-scale 3D Convolutional Sparse Coding and Automatic Parameter Selection
Department of Computer Science and EngineeringIn this thesis, we propose a novel image reconstruction algorithm using multi-scale 3D con- volutional sparse coding and a spectral decomposition technique for highly undersampled dy- namic Magnetic Resonance Imaging (MRI) data. The proposed method recovers high-frequency information using a shared 3D convolution-based dictionary built progressively during the re- construction process in an unsupervised manner, while low-frequency information is recovered using a total variation-based energy minimization method that leverages temporal coherence in dynamic MRI. Additionally, the proposed 3D dictionary is built across three different scales to more efficiently adapt to various feature sizes, and elastic net regularization is employed to promote a better approximation to the sparse input data. Furthermore, the computational com- plexity of each component in our iterative method is analyzed. We also propose an automatic parameter selection technique based on a genetic algorithm to find optimal parameters for our numerical solver which is a variant of the alternating direction method of multipliers (ADMM). We demonstrate the performance of our method by comparing it with state-of-the-art methods on 15 single-coil cardiac, 7 single-coil DCE, and a multi-coil brain MRI datasets at different sampling rates (12.5%, 25% and 50%). The results show that our method significantly outper- forms the other state-of-the-art methods in reconstruction quality with a comparable running time and is resilient to noise.ope
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
Accelerated High-Resolution Photoacoustic Tomography via Compressed Sensing
Current 3D photoacoustic tomography (PAT) systems offer either high image
quality or high frame rates but are not able to deliver high spatial and
temporal resolution simultaneously, which limits their ability to image dynamic
processes in living tissue. A particular example is the planar Fabry-Perot (FP)
scanner, which yields high-resolution images but takes several minutes to
sequentially map the photoacoustic field on the sensor plane, point-by-point.
However, as the spatio-temporal complexity of many absorbing tissue structures
is rather low, the data recorded in such a conventional, regularly sampled
fashion is often highly redundant. We demonstrate that combining variational
image reconstruction methods using spatial sparsity constraints with the
development of novel PAT acquisition systems capable of sub-sampling the
acoustic wave field can dramatically increase the acquisition speed while
maintaining a good spatial resolution: First, we describe and model two general
spatial sub-sampling schemes. Then, we discuss how to implement them using the
FP scanner and demonstrate the potential of these novel compressed sensing PAT
devices through simulated data from a realistic numerical phantom and through
measured data from a dynamic experimental phantom as well as from in-vivo
experiments. Our results show that images with good spatial resolution and
contrast can be obtained from highly sub-sampled PAT data if variational image
reconstruction methods that describe the tissues structures with suitable
sparsity-constraints are used. In particular, we examine the use of total
variation regularization enhanced by Bregman iterations. These novel
reconstruction strategies offer new opportunities to dramatically increase the
acquisition speed of PAT scanners that employ point-by-point sequential
scanning as well as reducing the channel count of parallelized schemes that use
detector arrays.Comment: submitted to "Physics in Medicine and Biology
Doctor of Philosophy
dissertationMagnetic resonance imaging (MRI) is a popular imaging modality that allows for noninvasive detection of diseases without the use of ionizing radiation. Every imaging modality comes with its set of challenges and limitations. Since the rate at which data can be collected using an MRI scanner is relatively slow, data undersampling is often employed in order to accelerate acquisitions. This undersampling leads to artifacts in the reconstructed image due to the lack of sufficient data. In this dissertation, novel reconstruction techniques are developed to reconstruct high-quality and artifact-free images using a compressed sensing (CS) formulation. The reconstruction techniques are specifically designed and developed to handle the various challenges that occur in acquiring high-resolution myocardial perfusion images. Since the reconstruction of these images is often time consuming, techniques are developed to allow rapid reconstruction of images. Rapid reconstruction aids in the transition of CS techniques for myocardial perfusion MRI into a tool that is routinely used by clinicians
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