Adaptive Volterra Filter for Parallel MRI Reconstruction
Abstract Parallel magnetic resonance imaging (MRI) technique is able to accelerate MRI speed for reducing costs and enhancing patient’s comfortability. Parallel MRI can be categorized into two types: image-based and k-space-based methods. For k-space-based parallel MRI, missing k-space data is recon...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2019-07-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13634-019-0633-5 |
| _version_ | 1818154780436987904 |
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| author | Haifeng Wang Yihang Zhou Shi Su Zhanqi Hu Jianxiang Liao Yuchou Chang |
| author_facet | Haifeng Wang Yihang Zhou Shi Su Zhanqi Hu Jianxiang Liao Yuchou Chang |
| author_sort | Haifeng Wang |
| collection | DOAJ |
| description | Abstract Parallel magnetic resonance imaging (MRI) technique is able to accelerate MRI speed for reducing costs and enhancing patient’s comfortability. Parallel MRI can be categorized into two types: image-based and k-space-based methods. For k-space-based parallel MRI, missing k-space data is reconstructed by interpolating existing acquired k-space data with appropriate coefficients, which is generally considered as a linear process. However, noise cannot be suppressed or removed during the linear reconstruction process and therefore reconstructed image often suffers serious noise, especially when the acceleration factor is high. Non-linear filters are known to remove non-linear noise better. Based on the Volterra series that discovers and removes the second-order non-linear noise, we proposed a non-linear reconstruction strategy called adaptive Volterra generalized autocalibrating partial parallel acquisition (AV-GRAPPA) to reconstruct the unacquired k-space signals. For the proposed AV-GRAPPA, optimal selection of the second-order Volterra series terms is adjusted and determined for optimizing reconstruction quality. Experimental results show that the proposed method is able to better remove the reconstruction noise and suppress aliasing artifacts. |
| first_indexed | 2024-12-11T14:31:57Z |
| format | Article |
| id | doaj.art-8fb895518cfb4132ad99f46480fc6b47 |
| institution | Directory Open Access Journal |
| issn | 1687-6180 |
| language | English |
| last_indexed | 2024-12-11T14:31:57Z |
| publishDate | 2019-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-8fb895518cfb4132ad99f46480fc6b472022-12-22T01:02:22ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802019-07-01201911810.1186/s13634-019-0633-5Adaptive Volterra Filter for Parallel MRI ReconstructionHaifeng Wang0Yihang Zhou1Shi Su2Zhanqi Hu3Jianxiang Liao4Yuchou Chang5Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesMedical Physics and Research Department, Hong Kong Sanatorium and HospitalPaul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesDepartment of Neurology, Shenzhen Children’s HospitalDepartment of Neurology, Shenzhen Children’s HospitalComputer Science and Engineering Technology Department, University of Houston-DowntownAbstract Parallel magnetic resonance imaging (MRI) technique is able to accelerate MRI speed for reducing costs and enhancing patient’s comfortability. Parallel MRI can be categorized into two types: image-based and k-space-based methods. For k-space-based parallel MRI, missing k-space data is reconstructed by interpolating existing acquired k-space data with appropriate coefficients, which is generally considered as a linear process. However, noise cannot be suppressed or removed during the linear reconstruction process and therefore reconstructed image often suffers serious noise, especially when the acceleration factor is high. Non-linear filters are known to remove non-linear noise better. Based on the Volterra series that discovers and removes the second-order non-linear noise, we proposed a non-linear reconstruction strategy called adaptive Volterra generalized autocalibrating partial parallel acquisition (AV-GRAPPA) to reconstruct the unacquired k-space signals. For the proposed AV-GRAPPA, optimal selection of the second-order Volterra series terms is adjusted and determined for optimizing reconstruction quality. Experimental results show that the proposed method is able to better remove the reconstruction noise and suppress aliasing artifacts.http://link.springer.com/article/10.1186/s13634-019-0633-5Parallel MRIGRAPPASecond-order non-linear noiseVolterra seriesNon-linear filter |
| spellingShingle | Haifeng Wang Yihang Zhou Shi Su Zhanqi Hu Jianxiang Liao Yuchou Chang Adaptive Volterra Filter for Parallel MRI Reconstruction EURASIP Journal on Advances in Signal Processing Parallel MRI GRAPPA Second-order non-linear noise Volterra series Non-linear filter |
| title | Adaptive Volterra Filter for Parallel MRI Reconstruction |
| title_full | Adaptive Volterra Filter for Parallel MRI Reconstruction |
| title_fullStr | Adaptive Volterra Filter for Parallel MRI Reconstruction |
| title_full_unstemmed | Adaptive Volterra Filter for Parallel MRI Reconstruction |
| title_short | Adaptive Volterra Filter for Parallel MRI Reconstruction |
| title_sort | adaptive volterra filter for parallel mri reconstruction |
| topic | Parallel MRI GRAPPA Second-order non-linear noise Volterra series Non-linear filter |
| url | http://link.springer.com/article/10.1186/s13634-019-0633-5 |
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