62,060 research outputs found
Four-dimensional tomographic reconstruction by time domain decomposition
Since the beginnings of tomography, the requirement that the sample does not
change during the acquisition of one tomographic rotation is unchanged. We
derived and successfully implemented a tomographic reconstruction method which
relaxes this decades-old requirement of static samples. In the presented
method, dynamic tomographic data sets are decomposed in the temporal domain
using basis functions and deploying an L1 regularization technique where the
penalty factor is taken for spatial and temporal derivatives. We implemented
the iterative algorithm for solving the regularization problem on modern GPU
systems to demonstrate its practical use
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
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