Directionality guided non linear diffusion compressed sensing MR image reconstruction.
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| Title: | Directionality guided non linear diffusion compressed sensing MR image reconstruction. |
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| Authors: | Joy, Ajin1 (AUTHOR), Jacob, Mathews2 (AUTHOR), Paul, Joseph Suresh1 (AUTHOR) j.paul@iiitmk.ac.in |
| Source: | Magnetic Resonance in Medicine. Dec2019, Vol. 82 Issue 6, p2326-2342. 17p. |
| Subjects: | Image reconstruction, Magnetic resonance imaging, Diffusion, Nature reserves, Signal-to-noise ratio |
| Abstract: | Purpose: Address the shortcomings of edge‐preserving filters to preserve the complex nature of edges, by adapting the direction of diffusion to the local variations in intensity function on a subpixel level, thereby achieving a reconstruction accuracy superior to that of data‐driven learning‐based approaches. Theory and Methods: Rate of diffusion for edges is found to vary in accordance with their gradient direction. Therefore, the edge preservation is strongly dependent on the direction in which the gradient is computed. Since the directionality of edges varies at different regions of the image, the proposed technique computes the gradients in all possible angular directions and uses a spatial‐frequency‐based deviation measure to choose the most reliable edges from the images diffused along different directions. Results: The proposed method is compared with the state‐of‐the‐art data‐driven learning‐based techniques of block matching and 3D filtering (BM3D), patch‐based nonlocal operator (PANO), and dictionary learning MRI (DLMRI). Best results are obtained when directionality of edges is estimated from a prior optimized k‐space and shows an improvement in peak signal‐to‐noise ratio (PSNR) measures by a factor of 2.36 dB, 1.92 dB, and 1.59 dB over BM3D, PANO, and dictionary learning MRI, respectively. Conclusion: The proposed technique prevents the emphasis of false edges and better captures the structural details by a locally varying directionality‐guided diffusion to make the error lower than that of the state‐of‐the‐art reconstruction techniques. In addition, a highly parallelizable form of the proposed model promises a significant gain in the reconstruction speed for practical implementations. [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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| Header | DbId: egs DbLabel: Engineering Source An: 138372918 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Directionality guided non linear diffusion compressed sensing MR image reconstruction. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Joy%2C+Ajin%22">Joy, Ajin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jacob%2C+Mathews%22">Jacob, Mathews</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Paul%2C+Joseph+Suresh%22">Paul, Joseph Suresh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> j.paul@iiitmk.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Magnetic+Resonance+in+Medicine%22">Magnetic Resonance in Medicine</searchLink>. Dec2019, Vol. 82 Issue 6, p2326-2342. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Image+reconstruction%22">Image reconstruction</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging%22">Magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Diffusion%22">Diffusion</searchLink><br /><searchLink fieldCode="DE" term="%22Nature+reserves%22">Nature reserves</searchLink><br /><searchLink fieldCode="DE" term="%22Signal-to-noise+ratio%22">Signal-to-noise ratio</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Purpose: Address the shortcomings of edge‐preserving filters to preserve the complex nature of edges, by adapting the direction of diffusion to the local variations in intensity function on a subpixel level, thereby achieving a reconstruction accuracy superior to that of data‐driven learning‐based approaches. Theory and Methods: Rate of diffusion for edges is found to vary in accordance with their gradient direction. Therefore, the edge preservation is strongly dependent on the direction in which the gradient is computed. Since the directionality of edges varies at different regions of the image, the proposed technique computes the gradients in all possible angular directions and uses a spatial‐frequency‐based deviation measure to choose the most reliable edges from the images diffused along different directions. Results: The proposed method is compared with the state‐of‐the‐art data‐driven learning‐based techniques of block matching and 3D filtering (BM3D), patch‐based nonlocal operator (PANO), and dictionary learning MRI (DLMRI). Best results are obtained when directionality of edges is estimated from a prior optimized k‐space and shows an improvement in peak signal‐to‐noise ratio (PSNR) measures by a factor of 2.36 dB, 1.92 dB, and 1.59 dB over BM3D, PANO, and dictionary learning MRI, respectively. Conclusion: The proposed technique prevents the emphasis of false edges and better captures the structural details by a locally varying directionality‐guided diffusion to make the error lower than that of the state‐of‐the‐art reconstruction techniques. In addition, a highly parallelizable form of the proposed model promises a significant gain in the reconstruction speed for practical implementations. [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.27895 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 2326 Subjects: – SubjectFull: Image reconstruction Type: general – SubjectFull: Magnetic resonance imaging Type: general – SubjectFull: Diffusion Type: general – SubjectFull: Nature reserves Type: general – SubjectFull: Signal-to-noise ratio Type: general Titles: – TitleFull: Directionality guided non linear diffusion compressed sensing MR image reconstruction. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Joy, Ajin – PersonEntity: Name: NameFull: Jacob, Mathews – PersonEntity: Name: NameFull: Paul, Joseph Suresh IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 07403194 Numbering: – Type: volume Value: 82 – Type: issue Value: 6 Titles: – TitleFull: Magnetic Resonance in Medicine Type: main |
| ResultId | 1 |
