ORCID Profile
0000-0002-4999-3563
Current Organisation
Trinity Western University
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Publisher: Hindawi Limited
Date: 2017
DOI: 10.1155/2017/4816024
Abstract: The k - t principal component analysis ( k - t PCA) is an effective approach for high spatiotemporal resolution dynamic magnetic resonance (MR) imaging. However, it suffers from larger residual aliasing artifacts and noise lification when the reduction factor goes higher. To further enhance the performance of this technique, we propose a new method called sparse k - t PCA that combines the k - t PCA algorithm with an artificial sparsity constraint. It is a self-calibrated procedure that is based on the traditional k - t PCA method by further eliminating the reconstruction error derived from complex subtraction of the s led k - t space from the original reconstructed k - t space. The proposed method is tested through both simulations and in vivo datasets with different reduction factors. Compared to the standard k - t PCA algorithm, the sparse k - t PCA can improve the normalized root-mean-square error performance and the accuracy of temporal resolution. It is thus useful for rapid dynamic MR imaging.
Publisher: Hindawi Limited
Date: 2017
DOI: 10.1155/2017/7685208
Abstract: Receiver arrays with a large number of coil elements are becoming progressively available because of their increased signal-to-noise ratio (SNR) and enhanced parallel imaging performance. However, longer reconstruction time and intensive computational cost have become significant concerns as the number of channels increases, especially in some iterative reconstructions. Coil compression can effectively solve this problem by linearly combining the raw data from multiple coils into fewer virtual coils. In this work, geometric-decomposition coil compression (GCC) is applied to radial s ling (both linear-angle and golden-angle patterns are discussed) for better compression. GCC, which is different from directly compressing in k -space, is performed separately in each spatial location along the fully s led directions, then followed by an additional alignment step to guarantee the smoothness of the virtual coil sensitivities. Both numerical simulation data and in vivo data were tested. Experimental results demonstrated that the GCC algorithm can achieve higher SNR and lower normalized root mean squared error values than the conventional principal component analysis approach in radial acquisitions.
Publisher: Cambridge University Press (CUP)
Date: 09-2020
DOI: 10.1017/INP.2020.25
Publisher: MDPI AG
Date: 17-06-2019
Abstract: Sweet potato (Ipomoea batatas (L.) Lam.) is a logical candidate crop to suppress invasive plants, but additional information is needed to support its potential application as a suppressive ground cover. The current study utilized a de Wit replacement series incorporating five ratios of sweet potato grown in the field in combination with one of three invasive plants (Ageratum conyzoides L., Bidens pilosa L., and Galinsoga parviflora Cav.) in replicated 9 m2 plots. Stem length, total biomass, and leaf area were higher for monoculture-grown sweet potato than these parameters for any of the invasive plants grown in monoculture. In mixed culture, the plant height, branch, leaf, inflorescence, seed, and biomass of all invasive plants were suppressed by sweet potato. The relative yield parameter indicated that intraspecific competition was greater than interspecific competition for sweet potato, while the reverse was true for invasive species. The net photosynthetic rate was higher for sweet potato than for B. pilosa and G. parviflora but not A. conyzoides. Superoxide dismutase and peroxidase activities of each of the three invasive plants were reduced in mixture with sweet potato. Our results demonstrated that these three invasive plants were significantly suppressed by sweet potato competition due to the rapid growth and phenotypic plasticity of sweet potato.
Publisher: Wiley
Date: 19-03-2019
DOI: 10.1111/WRE.12359
No related grants have been discovered for David Clements.