Data‐driven optimal binning for respiratory motion management in PET.
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| Title: | Data‐driven optimal binning for respiratory motion management in PET. |
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| Authors: | Kesner, Adam L.1 kesnera@mskcc.org, Meier, Joseph G.2, Burckhardt, Darrell D.3, Schwartz, Jazmin1, Lynch, David A.4 |
| Source: | Medical Physics. Jan2018, Vol. 45 Issue 1, p277-286. 10p. |
| Subjects: | Respiratory organ physiology, Positron emission tomography, Parameters (Statistics), Medical protocols, Fluorodeoxyglucose F18 |
| Abstract: | Purpose: Respiratory gating has been used in PET imaging to reduce the amount of image blurring caused by patient motion. Optimal binning is an approach for using the motion‐characterized data by binning it into a single, easy to understand/use, optimal bin. To date, optimal binning protocols have utilized externally driven motion characterization strategies that have been tuned with population‐derived assumptions and parameters. In this work, we are proposing a new strategy with which to characterize motion directly from a patient's gated scan, and use that signal to create a patient/instance‐specific optimal bin image. Methods: Two hundred and nineteen phase‐gated FDG PET scans, acquired using data‐driven gating as described previously, were used as the input for this study. For each scan, a phase‐amplitude motion characterization was generated and normalized using principle component analysis. A patient‐specific “optimal bin” window was derived using this characterization, via methods that mirror traditional optimal window binning strategies. The resulting optimal bin images were validated by correlating quantitative and qualitative measurements in the population of PET scans. Results: In 53% (n = 115) of the image population, the optimal bin was determined to include 100% of the image statistics. In the remaining images, the optimal binning windows averaged 60% of the statistics and ranged between 20% and 90%. Tuning the algorithm, through a single acceptance window parameter, allowed for adjustments of the algorithm's performance in the population toward conservation of motion or reduced noise—enabling users to incorporate their definition of optimal. In the population of images that were deemed appropriate for segregation, average lesion SUV max were 7.9, 8.5, and 9.0 for nongated images, optimal bin, and gated images, respectively. The Pearson correlation of FWHM measurements between optimal bin images and gated images were better than with nongated images, 0.89 and 0.85, respectively. Generally, optimal bin images had better resolution than the nongated images and better noise characteristics than the gated images. Discussion: We extended the concept of optimal binning to a data‐driven form, updating a traditionally one‐size‐fits‐all approach to a conformal one that supports adaptive imaging. This automated strategy was implemented easily within a large population and encapsulated motion information in an easy to use 3D image. Its simplicity and practicality may make this, or similar approaches ideal for use in clinical settings. [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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| Header | DbId: egs DbLabel: Engineering Source An: 127273249 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Data‐driven optimal binning for respiratory motion management in PET. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kesner%2C+Adam+L%2E%22">Kesner, Adam L.</searchLink><relatesTo>1</relatesTo><i> kesnera@mskcc.org</i><br /><searchLink fieldCode="AR" term="%22Meier%2C+Joseph+G%2E%22">Meier, Joseph G.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Burckhardt%2C+Darrell+D%2E%22">Burckhardt, Darrell D.</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Schwartz%2C+Jazmin%22">Schwartz, Jazmin</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Lynch%2C+David+A%2E%22">Lynch, David A.</searchLink><relatesTo>4</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Jan2018, Vol. 45 Issue 1, p277-286. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Respiratory+organ+physiology%22">Respiratory organ physiology</searchLink><br /><searchLink fieldCode="DE" term="%22Positron+emission+tomography%22">Positron emission tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Parameters+%28Statistics%29%22">Parameters (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+protocols%22">Medical protocols</searchLink><br /><searchLink fieldCode="DE" term="%22Fluorodeoxyglucose+F18%22">Fluorodeoxyglucose F18</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Purpose: Respiratory gating has been used in PET imaging to reduce the amount of image blurring caused by patient motion. Optimal binning is an approach for using the motion‐characterized data by binning it into a single, easy to understand/use, optimal bin. To date, optimal binning protocols have utilized externally driven motion characterization strategies that have been tuned with population‐derived assumptions and parameters. In this work, we are proposing a new strategy with which to characterize motion directly from a patient's gated scan, and use that signal to create a patient/instance‐specific optimal bin image. Methods: Two hundred and nineteen phase‐gated FDG PET scans, acquired using data‐driven gating as described previously, were used as the input for this study. For each scan, a phase‐amplitude motion characterization was generated and normalized using principle component analysis. A patient‐specific “optimal bin” window was derived using this characterization, via methods that mirror traditional optimal window binning strategies. The resulting optimal bin images were validated by correlating quantitative and qualitative measurements in the population of PET scans. Results: In 53% (n = 115) of the image population, the optimal bin was determined to include 100% of the image statistics. In the remaining images, the optimal binning windows averaged 60% of the statistics and ranged between 20% and 90%. Tuning the algorithm, through a single acceptance window parameter, allowed for adjustments of the algorithm's performance in the population toward conservation of motion or reduced noise—enabling users to incorporate their definition of optimal. In the population of images that were deemed appropriate for segregation, average lesion SUV max were 7.9, 8.5, and 9.0 for nongated images, optimal bin, and gated images, respectively. The Pearson correlation of FWHM measurements between optimal bin images and gated images were better than with nongated images, 0.89 and 0.85, respectively. Generally, optimal bin images had better resolution than the nongated images and better noise characteristics than the gated images. Discussion: We extended the concept of optimal binning to a data‐driven form, updating a traditionally one‐size‐fits‐all approach to a conformal one that supports adaptive imaging. This automated strategy was implemented easily within a large population and encapsulated motion information in an easy to use 3D image. Its simplicity and practicality may make this, or similar approaches ideal for use in clinical settings. [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.12651 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 277 Subjects: – SubjectFull: Respiratory organ physiology Type: general – SubjectFull: Positron emission tomography Type: general – SubjectFull: Parameters (Statistics) Type: general – SubjectFull: Medical protocols Type: general – SubjectFull: Fluorodeoxyglucose F18 Type: general Titles: – TitleFull: Data‐driven optimal binning for respiratory motion management in PET. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kesner, Adam L. – PersonEntity: Name: NameFull: Meier, Joseph G. – PersonEntity: Name: NameFull: Burckhardt, Darrell D. – PersonEntity: Name: NameFull: Schwartz, Jazmin – PersonEntity: Name: NameFull: Lynch, David A. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 00942405 Numbering: – Type: volume Value: 45 – Type: issue Value: 1 Titles: – TitleFull: Medical Physics Type: main |
| ResultId | 1 |
