911 research outputs found
Graphics processing unit accelerating compressed sensing photoacoustic computed tomography with total variation
Photoacoustic computed tomography with compressed sensing (CS-PACT) is a commonly used imaging strategy for sparse-sampling PACT. However, it is very time-consuming because of the iterative process involved in the image reconstruction. In this paper, we present a graphics processing unit (GPU)-based parallel computation framework for total-variation-based CS-PACT and adapted into a custom-made PACT system. Specifically, five compute-intensive operators are extracted from the iteration algorithm and are redesigned for parallel performance on a GPU. We achieved an image reconstruction speed 24–31 times faster than the CPU performance. We performed in vivo experiments on human hands to verify the feasibility of our developed method
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 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
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
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