Sparse BLIP: BLind Iterative Parallel imaging reconstruction using compressed sensing.
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| Title: | Sparse BLIP: BLind Iterative Parallel imaging reconstruction using compressed sensing. |
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| Authors: | She, Huajun1, Chen, Rong‐Rong1, Liang, Dong2, DiBella, Edward V. R.3, Ying, Leslie4,5 |
| Source: | Magnetic Resonance in Medicine. Feb2014, Vol. 71 Issue 2, p645-660. 16p. |
| Abstract: | Purpose To develop a sensitivity-based parallel imaging reconstruction method to reconstruct iteratively both the coil sensitivities and MR image simultaneously based on their prior information. Methods Parallel magnetic resonance imaging reconstruction problem can be formulated as a multichannel sampling problem where solutions are sought analytically. However, the channel functions given by the coil sensitivities in parallel imaging are not known exactly and the estimation error usually leads to artifacts. In this study, we propose a new reconstruction algorithm, termed Sparse BLind Iterative Parallel, for blind iterative parallel imaging reconstruction using compressed sensing. The proposed algorithm reconstructs both the sensitivity functions and the image simultaneously from undersampled data. It enforces the sparseness constraint in the image as done in compressed sensing, but is different from compressed sensing in that the sensing matrix is unknown and additional constraint is enforced on the sensitivities as well. Both phantom and in vivo imaging experiments were carried out with retrospective undersampling to evaluate the performance of the proposed method. Results Experiments show improvement in Sparse BLind Iterative Parallel reconstruction when compared with Sparse SENSE, JSENSE, IRGN-TV, and L1-SPIRiT reconstructions with the same number of measurements. Conclusion The proposed Sparse BLind Iterative Parallel algorithm reduces the reconstruction errors when compared to the state-of-the-art parallel imaging methods. Magn Reson Med 71:645-660, 2014. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR] |
| Copyright of Magnetic Resonance in Medicine is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: Sparse BLIP: BLind Iterative Parallel imaging reconstruction using compressed sensing. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22She%2C+Huajun%22">She, Huajun</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Chen%2C+Rong‐Rong%22">Chen, Rong‐Rong</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Liang%2C+Dong%22">Liang, Dong</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22DiBella%2C+Edward+V%2E+R%2E%22">DiBella, Edward V. R.</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Ying%2C+Leslie%22">Ying, Leslie</searchLink><relatesTo>4,5</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Magnetic+Resonance+in+Medicine%22">Magnetic Resonance in Medicine</searchLink>. Feb2014, Vol. 71 Issue 2, p645-660. 16p. – Name: Abstract Label: Abstract Group: Ab Data: Purpose To develop a sensitivity-based parallel imaging reconstruction method to reconstruct iteratively both the coil sensitivities and MR image simultaneously based on their prior information. Methods Parallel magnetic resonance imaging reconstruction problem can be formulated as a multichannel sampling problem where solutions are sought analytically. However, the channel functions given by the coil sensitivities in parallel imaging are not known exactly and the estimation error usually leads to artifacts. In this study, we propose a new reconstruction algorithm, termed Sparse BLind Iterative Parallel, for blind iterative parallel imaging reconstruction using compressed sensing. The proposed algorithm reconstructs both the sensitivity functions and the image simultaneously from undersampled data. It enforces the sparseness constraint in the image as done in compressed sensing, but is different from compressed sensing in that the sensing matrix is unknown and additional constraint is enforced on the sensitivities as well. Both phantom and in vivo imaging experiments were carried out with retrospective undersampling to evaluate the performance of the proposed method. Results Experiments show improvement in Sparse BLind Iterative Parallel reconstruction when compared with Sparse SENSE, JSENSE, IRGN-TV, and L1-SPIRiT reconstructions with the same number of measurements. Conclusion The proposed Sparse BLind Iterative Parallel algorithm reduces the reconstruction errors when compared to the state-of-the-art parallel imaging methods. Magn Reson Med 71:645-660, 2014. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Magnetic Resonance in Medicine is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/mrm.24716 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 645 Titles: – TitleFull: Sparse BLIP: BLind Iterative Parallel imaging reconstruction using compressed sensing. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: She, Huajun – PersonEntity: Name: NameFull: Chen, Rong‐Rong – PersonEntity: Name: NameFull: Liang, Dong – PersonEntity: Name: NameFull: DiBella, Edward V. R. – PersonEntity: Name: NameFull: Ying, Leslie IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2014 Type: published Y: 2014 Identifiers: – Type: issn-print Value: 07403194 Numbering: – Type: volume Value: 71 – Type: issue Value: 2 Titles: – TitleFull: Magnetic Resonance in Medicine Type: main |
| ResultId | 1 |
