Voxel-wise uncertainty in CT substitute derived from MRI.
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| Title: | Voxel-wise uncertainty in CT substitute derived from MRI. |
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| Authors: | Johansson, Adam1, Karlsson, Mikael1, Yu, Jun2, Asklund, Thomas1, Nyholm, Tufve1 |
| Source: | Medical Physics. Jun2012, Vol. 39 Issue 6, p3283-3290. 8p. |
| Subjects: | Tomography, Magnetic resonance imaging, Finite Gaussian mixture models (Statistics), Medical imaging systems, Uncertainty (Information theory), Parameter estimation, Mathematical models |
| Abstract: | Purpose: In an earlier work, we demonstrated that substitutes for CT images can be derived from MR images using ultrashort echo time (UTE) sequences, conventional T2 weighted sequences, and Gaussian mixture regression (GMR). In this study, we extend this work by analyzing the uncertainties associated with the GMR model and the information contributions from the individual imaging sequences. Methods: An analytical expression for the voxel-wise conditional expected absolute deviation (EAD) in substitute CT (s-CT) images was derived. The expression depends only on MR images and can thus be calculated along with each s-CT image. The uncertainty measure was evaluated by comparing the EAD to the true mean absolute prediction deviation (MAPD) between the s-CT and CT images for 14 patients. Further, the influence of the different MR images included in the GMR model on the generated s-CTs was investigated by removing one or more images and evaluating the MAPD for a spectrum of predicted radiological densities. Results: The largest EAD was predicted at air-soft tissue and bone-soft tissue interfaces. The EAD agreed with the MAPD in both these regions and in regions with lower EADs, such as the brain. Two of the MR images included in the GMR model were found to be mutually redundant for the purpose of s-CT generation. Conclusions: The presented uncertainty estimation method accurately predicts the voxel-wise MAPD in s-CT images. Also, the non-UTE sequence previously used in the model was found to be redundant. [ABSTRACT FROM AUTHOR] |
| Copyright of Medical Physics 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: Voxel-wise uncertainty in CT substitute derived from MRI. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Johansson%2C+Adam%22">Johansson, Adam</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Karlsson%2C+Mikael%22">Karlsson, Mikael</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Yu%2C+Jun%22">Yu, Jun</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Asklund%2C+Thomas%22">Asklund, Thomas</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Nyholm%2C+Tufve%22">Nyholm, Tufve</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Jun2012, Vol. 39 Issue 6, p3283-3290. 8p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Tomography%22">Tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging%22">Magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Finite+Gaussian+mixture+models+%28Statistics%29%22">Finite Gaussian mixture models (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+imaging+systems%22">Medical imaging systems</searchLink><br /><searchLink fieldCode="DE" term="%22Uncertainty+%28Information+theory%29%22">Uncertainty (Information theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Purpose: In an earlier work, we demonstrated that substitutes for CT images can be derived from MR images using ultrashort echo time (UTE) sequences, conventional T2 weighted sequences, and Gaussian mixture regression (GMR). In this study, we extend this work by analyzing the uncertainties associated with the GMR model and the information contributions from the individual imaging sequences. Methods: An analytical expression for the voxel-wise conditional expected absolute deviation (EAD) in substitute CT (s-CT) images was derived. The expression depends only on MR images and can thus be calculated along with each s-CT image. The uncertainty measure was evaluated by comparing the EAD to the true mean absolute prediction deviation (MAPD) between the s-CT and CT images for 14 patients. Further, the influence of the different MR images included in the GMR model on the generated s-CTs was investigated by removing one or more images and evaluating the MAPD for a spectrum of predicted radiological densities. Results: The largest EAD was predicted at air-soft tissue and bone-soft tissue interfaces. The EAD agreed with the MAPD in both these regions and in regions with lower EADs, such as the brain. Two of the MR images included in the GMR model were found to be mutually redundant for the purpose of s-CT generation. Conclusions: The presented uncertainty estimation method accurately predicts the voxel-wise MAPD in s-CT images. Also, the non-UTE sequence previously used in the model was found to be redundant. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Medical Physics 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: 10.1118/1.4711807 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 3283 Subjects: – SubjectFull: Tomography Type: general – SubjectFull: Magnetic resonance imaging Type: general – SubjectFull: Finite Gaussian mixture models (Statistics) Type: general – SubjectFull: Medical imaging systems Type: general – SubjectFull: Uncertainty (Information theory) Type: general – SubjectFull: Parameter estimation Type: general – SubjectFull: Mathematical models Type: general Titles: – TitleFull: Voxel-wise uncertainty in CT substitute derived from MRI. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Johansson, Adam – PersonEntity: Name: NameFull: Karlsson, Mikael – PersonEntity: Name: NameFull: Yu, Jun – PersonEntity: Name: NameFull: Asklund, Thomas – PersonEntity: Name: NameFull: Nyholm, Tufve IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2012 Type: published Y: 2012 Identifiers: – Type: issn-print Value: 00942405 Numbering: – Type: volume Value: 39 – Type: issue Value: 6 Titles: – TitleFull: Medical Physics Type: main |
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