Introducing a novel sub‐millimeter lung CT image registration error quantitation tool.
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| Title: | Introducing a novel sub‐millimeter lung CT image registration error quantitation tool. |
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| Authors: | Boyle, Peter1 (AUTHOR) pboyle@mednet.ucla.edu, Naumann, Louise1 (AUTHOR), Lauria, Michael1 (AUTHOR), Miller, Claudia1 (AUTHOR), Andosca, Ryan1 (AUTHOR), Savjani, Ricky1 (AUTHOR), O'Connell, Dylan1 (AUTHOR), Moghanaki, Drew1 (AUTHOR), Barjaktarevic, Igor2 (AUTHOR), Goldin, Jonathan3 (AUTHOR), Low, Daniel A.1 (AUTHOR) |
| Source: | Medical Physics. Mar2025, Vol. 52 Issue 3, p1601-1614. 14p. |
| Subjects: | Image registration, Computed tomography, Measuring instruments, Measurement, Spatial resolution |
| Abstract: | Background: Lung computed tomography (CT) scan image registration is being used for lung function analysis such as ventilation. Given the high sensitivity of functional analyses to image registration errors, an image registration error scoring tool that can measure submillimeter image registration errors is needed. Purpose: To propose an image registration error scoring tool, termed λ, whose spatial sensitivity can be used to quantify image registration errors in steep image gradient regions under realistic noise conditions. Methods: λ compares two images, termed reference and evaluated. The HU and distance scales of both images are normalized by user‐selected scaling criteria. For each voxel in the reference image, the 4D Euclidian distances between the reference voxel and the nearby evaluated voxels are calculated, and the minimum of these distances is λ$\lambda $. We tested λ$\lambda $ in simulated individual blood vessels comprised of 1, 3, and 5 mm diameter cylinders in 1 × 1 × 1 mm3 voxel images, which were blurred to simulate CT scanner intrinsic resolution and volume averaging. We placed the simulated vessels in a homogeneous background simulating parenchymal tissue density and injected 20, 40, and 60 HU standard deviation Gaussian noise. We used isotropic Gaussian filters with 0.5, 1.0, and 1.5 mm standard deviation kernels to smooth the simulated images. We assessed λ$\lambda $ using reference‐evaluated vessel shifts of −1.0 to 1.0 mm in 0.05 mm steps via rigid translational and rotational deformations. We examined whether λ$\lambda $ tracked the translation vector via its internal spatial component. We restricted λ$\lambda $ to voxels using the angle, termed θ$\theta $, between the λ$\lambda $ vector and the normalized spatial‐distance axes, terming the results the restricted‐λ$\lambda $, λR${{\lambda }_R}$, where θ$\theta $ was hypothesized to be a proxy for image gradient. We determined whether θ$\theta $ was coincident with the image gradient by examining if the voxels with |θ|≤30∘$| \theta | \le {{30}^ \circ }$ tracked the evaluated vessels. We used the 95th percentile of λR${{\lambda }_R}$, λR95$\lambda _R^{95}$, to determine spatial sensitivity, which we took as a conservative estimate of registration error, by fitting λR95$\lambda _R^{95}$ to a modified absolute‐value function for each tested rigid translation, noise level, smoothing kernel, and vessel radius combination. We demonstrated the use of λ$\lambda $ on a clinical example consisting of a set of 25 deformably registered free‐breathing thoracic CT scans. We visually compared the λ$\lambda $ and λR${{\lambda }_R}$ results against the HU differences between each clinical image pair. Results: We found θ to be coincident with the image gradient. We found that λ$\lambda $'s spatial component tracked the vessel shifts. We determined the spatial sensitivity limit of λR95$\lambda _R^{95}$ to be < 0.2 mm. The noise level and smoothing kernel influenced λR95$\lambda _R^{95}$ sensitivity, worsening with increasing noise, and improving with increasing smoothing. For the clinical images, we observed λ$\lambda $ to qualitatively match the absolute difference of intensity in the image pairs and λR${{\lambda }_R}$ to restrict itself to high gradient regions or regions of visually apparent errors. Conclusion: λR95$\lambda _R^{95}$ detected sub‐millimeter positioning errors between simulated vessels in the presence of typical CT noise. The noise magnitude and choice of noise smoothing kernel were inversely related to λR95$\lambda _R^{95}$ sensitivity, implying that study‐specific tuning of the pre‐smoothing kernel may be required. The demonstrated ability in geometric tests of λR95$\lambda _R^{95}$ to detect subvoxel DIR errors warrants further evaluation and testing. [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.) | |
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| Items | – Name: Title Label: Title Group: Ti Data: Introducing a novel sub‐millimeter lung CT image registration error quantitation tool. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Boyle%2C+Peter%22">Boyle, Peter</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> pboyle@mednet.ucla.edu</i><br /><searchLink fieldCode="AR" term="%22Naumann%2C+Louise%22">Naumann, Louise</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lauria%2C+Michael%22">Lauria, Michael</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Miller%2C+Claudia%22">Miller, Claudia</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Andosca%2C+Ryan%22">Andosca, Ryan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Savjani%2C+Ricky%22">Savjani, Ricky</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22O'Connell%2C+Dylan%22">O'Connell, Dylan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Moghanaki%2C+Drew%22">Moghanaki, Drew</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Barjaktarevic%2C+Igor%22">Barjaktarevic, Igor</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Goldin%2C+Jonathan%22">Goldin, Jonathan</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Low%2C+Daniel+A%2E%22">Low, Daniel A.</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Mar2025, Vol. 52 Issue 3, p1601-1614. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Image+registration%22">Image registration</searchLink><br /><searchLink fieldCode="DE" term="%22Computed+tomography%22">Computed tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Measuring+instruments%22">Measuring instruments</searchLink><br /><searchLink fieldCode="DE" term="%22Measurement%22">Measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Spatial+resolution%22">Spatial resolution</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background: Lung computed tomography (CT) scan image registration is being used for lung function analysis such as ventilation. Given the high sensitivity of functional analyses to image registration errors, an image registration error scoring tool that can measure submillimeter image registration errors is needed. Purpose: To propose an image registration error scoring tool, termed λ, whose spatial sensitivity can be used to quantify image registration errors in steep image gradient regions under realistic noise conditions. Methods: λ compares two images, termed reference and evaluated. The HU and distance scales of both images are normalized by user‐selected scaling criteria. For each voxel in the reference image, the 4D Euclidian distances between the reference voxel and the nearby evaluated voxels are calculated, and the minimum of these distances is λ$\lambda $. We tested λ$\lambda $ in simulated individual blood vessels comprised of 1, 3, and 5 mm diameter cylinders in 1 × 1 × 1 mm3 voxel images, which were blurred to simulate CT scanner intrinsic resolution and volume averaging. We placed the simulated vessels in a homogeneous background simulating parenchymal tissue density and injected 20, 40, and 60 HU standard deviation Gaussian noise. We used isotropic Gaussian filters with 0.5, 1.0, and 1.5 mm standard deviation kernels to smooth the simulated images. We assessed λ$\lambda $ using reference‐evaluated vessel shifts of −1.0 to 1.0 mm in 0.05 mm steps via rigid translational and rotational deformations. We examined whether λ$\lambda $ tracked the translation vector via its internal spatial component. We restricted λ$\lambda $ to voxels using the angle, termed θ$\theta $, between the λ$\lambda $ vector and the normalized spatial‐distance axes, terming the results the restricted‐λ$\lambda $, λR${{\lambda }_R}$, where θ$\theta $ was hypothesized to be a proxy for image gradient. We determined whether θ$\theta $ was coincident with the image gradient by examining if the voxels with |θ|≤30∘$| \theta | \le {{30}^ \circ }$ tracked the evaluated vessels. We used the 95th percentile of λR${{\lambda }_R}$, λR95$\lambda _R^{95}$, to determine spatial sensitivity, which we took as a conservative estimate of registration error, by fitting λR95$\lambda _R^{95}$ to a modified absolute‐value function for each tested rigid translation, noise level, smoothing kernel, and vessel radius combination. We demonstrated the use of λ$\lambda $ on a clinical example consisting of a set of 25 deformably registered free‐breathing thoracic CT scans. We visually compared the λ$\lambda $ and λR${{\lambda }_R}$ results against the HU differences between each clinical image pair. Results: We found θ to be coincident with the image gradient. We found that λ$\lambda $'s spatial component tracked the vessel shifts. We determined the spatial sensitivity limit of λR95$\lambda _R^{95}$ to be < 0.2 mm. The noise level and smoothing kernel influenced λR95$\lambda _R^{95}$ sensitivity, worsening with increasing noise, and improving with increasing smoothing. For the clinical images, we observed λ$\lambda $ to qualitatively match the absolute difference of intensity in the image pairs and λR${{\lambda }_R}$ to restrict itself to high gradient regions or regions of visually apparent errors. Conclusion: λR95$\lambda _R^{95}$ detected sub‐millimeter positioning errors between simulated vessels in the presence of typical CT noise. The noise magnitude and choice of noise smoothing kernel were inversely related to λR95$\lambda _R^{95}$ sensitivity, implying that study‐specific tuning of the pre‐smoothing kernel may be required. The demonstrated ability in geometric tests of λR95$\lambda _R^{95}$ to detect subvoxel DIR errors warrants further evaluation and testing. [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: 10.1002/mp.17552 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1601 Subjects: – SubjectFull: Image registration Type: general – SubjectFull: Computed tomography Type: general – SubjectFull: Measuring instruments Type: general – SubjectFull: Measurement Type: general – SubjectFull: Spatial resolution Type: general Titles: – TitleFull: Introducing a novel sub‐millimeter lung CT image registration error quantitation tool. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Boyle, Peter – PersonEntity: Name: NameFull: Naumann, Louise – PersonEntity: Name: NameFull: Lauria, Michael – PersonEntity: Name: NameFull: Miller, Claudia – PersonEntity: Name: NameFull: Andosca, Ryan – PersonEntity: Name: NameFull: Savjani, Ricky – PersonEntity: Name: NameFull: O'Connell, Dylan – PersonEntity: Name: NameFull: Moghanaki, Drew – PersonEntity: Name: NameFull: Barjaktarevic, Igor – PersonEntity: Name: NameFull: Goldin, Jonathan – PersonEntity: Name: NameFull: Low, Daniel A. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 00942405 Numbering: – Type: volume Value: 52 – Type: issue Value: 3 Titles: – TitleFull: Medical Physics Type: main |
| ResultId | 1 |
