Correction of susceptibility artifacts in diffusion tensor data using non-linear registration
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| Title: | Correction of susceptibility artifacts in diffusion tensor data using non-linear registration |
|---|---|
| Authors: | Merhof, D.1,2 www9.informatik.uni-erlangen.de/people, Soza, G.3, Stadlbauer, A.2, Greiner, G.1, Nimsky, C.2 |
| Source: | Medical Image Analysis. Dec2007, Vol. 11 Issue 6, p588-603. 16p. |
| Subjects: | Medical imaging systems, Imaging systems, Image analysis, Medical equipment |
| Abstract: | Abstract: Diffusion tensor imaging can be used to localize major white matter tracts within the human brain. For surgery of tumors near eloquent brain areas such as the pyramidal tract this information is of importance to achieve an optimal resection while avoiding post-operative neurological deficits. However, due to the small bandwidth of echo planar imaging, diffusion tensor images suffer from susceptibility artifacts resulting in positional shifts and distortion. As a consequence, the fiber tracts computed from echo planar imaging data are spatially distorted. We present an approach based on non-linear registration using Bézier functions to efficiently correct distortions due to susceptibility artifacts. The approach makes extensive use of graphics hardware to accelerate the non-linear registration procedure. An improvement presented in this paper is a more robust and efficient optimization strategy based on simultaneous perturbation stochastic approximation (SPSA). Since the accuracy of non-linear registration is crucial for the value of the presented correction method, two techniques were applied in order to prove the quality of the proposed framework. First, the registration accuracy was evaluated by recovering a known transformation with non-linear registration. Second, landmark-based evaluation of the registration method for anatomical and diffusion tensor data was performed. The registration was then applied to patients with lesions adjacent to the pyramidal tract in order to compensate for susceptibility artifacts. The effect of the correction on the pyramidal tract was then quantified by measuring the position of the tract before and after registration. As a result, the distortions observed in phase encoding direction were most prominent at the cortex and the brainstem. The presented approach allows correcting fiber tract distortions which is an important prerequisite when tractography data are integrated into a stereotactic setup for intra-operative guidance. [Copyright &y& Elsevier] |
| Copyright of Medical Image Analysis is the property of Elsevier B.V. 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 27244631 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Correction of susceptibility artifacts in diffusion tensor data using non-linear registration – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Merhof%2C+D%2E%22">Merhof, D.</searchLink><relatesTo>1,2</relatesTo><i> www9.informatik.uni-erlangen.de/people</i><br /><searchLink fieldCode="AR" term="%22Soza%2C+G%2E%22">Soza, G.</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Stadlbauer%2C+A%2E%22">Stadlbauer, A.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Greiner%2C+G%2E%22">Greiner, G.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Nimsky%2C+C%2E%22">Nimsky, C.</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Image+Analysis%22">Medical Image Analysis</searchLink>. Dec2007, Vol. 11 Issue 6, p588-603. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Medical+imaging+systems%22">Medical imaging systems</searchLink><br /><searchLink fieldCode="DE" term="%22Imaging+systems%22">Imaging systems</searchLink><br /><searchLink fieldCode="DE" term="%22Image+analysis%22">Image analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+equipment%22">Medical equipment</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: Diffusion tensor imaging can be used to localize major white matter tracts within the human brain. For surgery of tumors near eloquent brain areas such as the pyramidal tract this information is of importance to achieve an optimal resection while avoiding post-operative neurological deficits. However, due to the small bandwidth of echo planar imaging, diffusion tensor images suffer from susceptibility artifacts resulting in positional shifts and distortion. As a consequence, the fiber tracts computed from echo planar imaging data are spatially distorted. We present an approach based on non-linear registration using Bézier functions to efficiently correct distortions due to susceptibility artifacts. The approach makes extensive use of graphics hardware to accelerate the non-linear registration procedure. An improvement presented in this paper is a more robust and efficient optimization strategy based on simultaneous perturbation stochastic approximation (SPSA). Since the accuracy of non-linear registration is crucial for the value of the presented correction method, two techniques were applied in order to prove the quality of the proposed framework. First, the registration accuracy was evaluated by recovering a known transformation with non-linear registration. Second, landmark-based evaluation of the registration method for anatomical and diffusion tensor data was performed. The registration was then applied to patients with lesions adjacent to the pyramidal tract in order to compensate for susceptibility artifacts. The effect of the correction on the pyramidal tract was then quantified by measuring the position of the tract before and after registration. As a result, the distortions observed in phase encoding direction were most prominent at the cortex and the brainstem. The presented approach allows correcting fiber tract distortions which is an important prerequisite when tractography data are integrated into a stereotactic setup for intra-operative guidance. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Medical Image Analysis is the property of Elsevier B.V. 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: 10.1016/j.media.2007.05.004 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 588 Subjects: – SubjectFull: Medical imaging systems Type: general – SubjectFull: Imaging systems Type: general – SubjectFull: Image analysis Type: general – SubjectFull: Medical equipment Type: general Titles: – TitleFull: Correction of susceptibility artifacts in diffusion tensor data using non-linear registration Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Merhof, D. – PersonEntity: Name: NameFull: Soza, G. – PersonEntity: Name: NameFull: Stadlbauer, A. – PersonEntity: Name: NameFull: Greiner, G. – PersonEntity: Name: NameFull: Nimsky, C. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2007 Type: published Y: 2007 Identifiers: – Type: issn-print Value: 13618415 Numbering: – Type: volume Value: 11 – Type: issue Value: 6 Titles: – TitleFull: Medical Image Analysis Type: main |
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