A new fast and fully automated software based algorithm for extracting respiratory signal from raw PET data and its comparison to other methods.
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| Title: | A new fast and fully automated software based algorithm for extracting respiratory signal from raw PET data and its comparison to other methods. |
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| Authors: | Kesner, Adam Leon1, Kuntner, Claudia2 |
| Source: | Medical Physics. Oct2010, Vol. 37 Issue 10, p5550-5559. 10p. |
| Subjects: | Algorithms, Computer software, Data extraction, Positron emission tomography, Respiration, Computers in medicine, Computer input-output equipment, Comparative studies |
| Abstract: | Purpose: Respiratory gating in PET is an approach used to minimize the negative effects of respiratory motion on spatial resolution. It is based on an initial determination of a patient's respiratory movements during a scan, typically using hardware based systems. In recent years, several fully automated databased algorithms have been presented for extracting a respiratory signal directly from PET data, providing a very practical strategy for implementing gating in the clinic. In this work, a new method is presented for extracting a respiratory signal from raw PET sinogram data and compared to previously presented automated techniques. Methods: The acquisition of respiratory signal from PET data in the newly proposed method is based on rebinning the sinogram data into smaller data structures and then analyzing the time activity behavior in the elements of these structures. From this analysis, a 1D respiratory trace is produced, analogous to a hardware derived respiratory trace. To assess the accuracy of this fully automated method, respiratory signal was extracted from a collection of 22 clinical FDG-PET scans using this method, and compared to signal derived from several other software based methods as well as a signal derived from a hardware system. Results: The method presented required approximately 9 min of processing time for each 10 min scan (using a single 2.67 GHz processor), which in theory can be accomplished while the scan is being acquired and therefore allowing a real-time respiratory signal acquisition. Using the mean correlation between the software based and hardware based respiratory traces, the optimal parameters were determined for the presented algorithm. The mean/median/range of correlations for the set of scans when using the optimal parameters was found to be 0.58/0.68/0.07-0.86. The speed of this method was within the range of real-time while the accuracy surpassed the most accurate of the previously presented algorithms. Conclusions: PET data inherently contains information about patient motion; information that is not currently being utilized. We have shown that a respiratory signal can be extracted from raw PET data in potentially real-time and in a fully automated manner. This signal correlates well with hardware based signal for a large percentage of scans, and avoids the efforts and complications associated with hardware. The proposed method to extract a respiratory signal can be implemented on existing scanners and, if properly integrated, can be applied without changes to routine clinical procedures. [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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 54006086 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A new fast and fully automated software based algorithm for extracting respiratory signal from raw PET data and its comparison to other methods. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kesner%2C+Adam+Leon%22">Kesner, Adam Leon</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Kuntner%2C+Claudia%22">Kuntner, Claudia</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Oct2010, Vol. 37 Issue 10, p5550-5559. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software%22">Computer software</searchLink><br /><searchLink fieldCode="DE" term="%22Data+extraction%22">Data extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Positron+emission+tomography%22">Positron emission tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Respiration%22">Respiration</searchLink><br /><searchLink fieldCode="DE" term="%22Computers+in+medicine%22">Computers in medicine</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+input-output+equipment%22">Computer input-output equipment</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+studies%22">Comparative studies</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Purpose: Respiratory gating in PET is an approach used to minimize the negative effects of respiratory motion on spatial resolution. It is based on an initial determination of a patient's respiratory movements during a scan, typically using hardware based systems. In recent years, several fully automated databased algorithms have been presented for extracting a respiratory signal directly from PET data, providing a very practical strategy for implementing gating in the clinic. In this work, a new method is presented for extracting a respiratory signal from raw PET sinogram data and compared to previously presented automated techniques. Methods: The acquisition of respiratory signal from PET data in the newly proposed method is based on rebinning the sinogram data into smaller data structures and then analyzing the time activity behavior in the elements of these structures. From this analysis, a 1D respiratory trace is produced, analogous to a hardware derived respiratory trace. To assess the accuracy of this fully automated method, respiratory signal was extracted from a collection of 22 clinical FDG-PET scans using this method, and compared to signal derived from several other software based methods as well as a signal derived from a hardware system. Results: The method presented required approximately 9 min of processing time for each 10 min scan (using a single 2.67 GHz processor), which in theory can be accomplished while the scan is being acquired and therefore allowing a real-time respiratory signal acquisition. Using the mean correlation between the software based and hardware based respiratory traces, the optimal parameters were determined for the presented algorithm. The mean/median/range of correlations for the set of scans when using the optimal parameters was found to be 0.58/0.68/0.07-0.86. The speed of this method was within the range of real-time while the accuracy surpassed the most accurate of the previously presented algorithms. Conclusions: PET data inherently contains information about patient motion; information that is not currently being utilized. We have shown that a respiratory signal can be extracted from raw PET data in potentially real-time and in a fully automated manner. This signal correlates well with hardware based signal for a large percentage of scans, and avoids the efforts and complications associated with hardware. The proposed method to extract a respiratory signal can be implemented on existing scanners and, if properly integrated, can be applied without changes to routine clinical procedures. [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: 10.1118/1.3483784 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 5550 Subjects: – SubjectFull: Algorithms Type: general – SubjectFull: Computer software Type: general – SubjectFull: Data extraction Type: general – SubjectFull: Positron emission tomography Type: general – SubjectFull: Respiration Type: general – SubjectFull: Computers in medicine Type: general – SubjectFull: Computer input-output equipment Type: general – SubjectFull: Comparative studies Type: general Titles: – TitleFull: A new fast and fully automated software based algorithm for extracting respiratory signal from raw PET data and its comparison to other methods. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kesner, Adam Leon – PersonEntity: Name: NameFull: Kuntner, Claudia IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2010 Type: published Y: 2010 Identifiers: – Type: issn-print Value: 00942405 Numbering: – Type: volume Value: 37 – Type: issue Value: 10 Titles: – TitleFull: Medical Physics Type: main |
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