506 research outputs found
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
Iterative Reweighted Algorithms for Sparse Signal Recovery with Temporally Correlated Source Vectors
Iterative reweighted algorithms, as a class of algorithms for sparse signal
recovery, have been found to have better performance than their non-reweighted
counterparts. However, for solving the problem of multiple measurement vectors
(MMVs), all the existing reweighted algorithms do not account for temporal
correlation among source vectors and thus their performance degrades
significantly in the presence of correlation. In this work we propose an
iterative reweighted sparse Bayesian learning (SBL) algorithm exploiting the
temporal correlation, and motivated by it, we propose a strategy to improve
existing reweighted algorithms for the MMV problem, i.e. replacing
their row norms with Mahalanobis distance measure. Simulations show that the
proposed reweighted SBL algorithm has superior performance, and the proposed
improvement strategy is effective for existing reweighted algorithms.Comment: Accepted by ICASSP 201
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Non-Equispaced Fast Fourier Transforms in Turbulence Simulation
Fourier pseudo-spectral method on equispaced grid is one of the approaches in turbulence simulation, to compute derivative of discrete data, using fast Fourier Transform (FFT) and gives low dispersion and dissipation errors. In many turbulent flows the dynamically important scales of motion are concentrated in certain regions which requires a coarser grid for higher accuracy. A coarser grid in other regions minimizes the memory requirement. This requires the use of Non-equispaced Fast Fourier Transform (NFFT) to compute the Fourier transform, by solving a system of linear equations.
To achieve similar accuracy, the NFFT needs to return more Fourier coefficients than the number of non-equispaced gridpoints, making it an under-determined system. The minimum L2 norm solution of the system is refined using an iterative reconstruction algorithm, FOCUSS.
The NFFT and FOCUSS algorithms yield accurate results with smaller test case of a Direct Numerical Simulation on a grid of 64 gridpoints in each dimension, using Taylor green initial condition. The computational speed for this case was found to be unacceptably slow and few methods to improve the performance have been discussed.
The approach of NFFT and FOCUSS was tested on a line extracted from 3-dimensional turbulent flow field. Fourier transform of the extracted line, sampled on 1024 non-equispaced gridpoints, computed for 2048 coefficients and the corresponding numerical derivative are found to be inaccurate. It can be observed that the NFFT and FOCUSS approach works for sparse Fourier transform, but not for turbulent fields having a wideband Fourier transform
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