Feasibility of PET-enabled dual-energy CT imaging: First physical phantom and initial patient study results
Purpose Dual-energy (DE) CT enables material decomposition by using two different x-ray energies and may be combined with PET for improved multimodality imaging. However, this increases radiation dose and may require a hardware upgrade due to the added second x-ray CT scan. The recently proposed PET...
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Published in | European journal of nuclear medicine and molecular imaging Vol. 52; no. 5; pp. 1912 - 1923 |
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Main Authors | , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.04.2025
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
ISSN | 1619-7070 1619-7089 1619-7089 |
DOI | 10.1007/s00259-024-06975-5 |
Cover
Abstract | Purpose
Dual-energy (DE) CT enables material decomposition by using two different x-ray energies and may be combined with PET for improved multimodality imaging. However, this increases radiation dose and may require a hardware upgrade due to the added second x-ray CT scan. The recently proposed PET-enabled DECT method allows dual-energy imaging using a conventional PET/CT scanner without the need to change scanner hardware or increase radiation exposure. Here we demonstrate the first-time physical phantom and patient data evaluation of this method.
Methods
The PET-enabled DECT method reconstructs a gamma-ray CT (gCT) image at 511 keV from the time-of-flight PET data with the maximum-likelihood attenuation and activity (MLAA) approach and then combines this image with the low-energy x-ray CT images to form a dual-energy image pair for material decomposition. To improve the image quality of gCT, a kernel MLAA method was developed using the x-ray CT as
a priori
information. Here we developed a general open-source implementation for gCT reconstruction and used this implementation for the first real data validation using both physical phantom study and human-subject study. Results from PET-enabled DECT were compared using x-ray DECT as the reference. Further, we applied the PET-enabled DECT method in another patient study to evaluate bone lesions.
Results
Compared to the standard MLAA, results from the kernel MLAA showed significantly improved image quality. PET-enabled DECT with the kernel MLAA was able to generate fractional images that were comparable to the x-ray DECT, with high correlation coefficients for both the phantom study and human subject study (
R
> 0.99). The application study also indicates that PET-enabled DECT has potential to characterize bone lesions.
Conclusion
Results from this study have demonstrated the feasibility of this PET-enabled method for CT imaging and material decomposition. PET-enabled DECT shows promise to provide comparable results to x-ray DECT. |
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AbstractList | Purpose
Dual-energy (DE) CT enables material decomposition by using two different x-ray energies and may be combined with PET for improved multimodality imaging. However, this increases radiation dose and may require a hardware upgrade due to the added second x-ray CT scan. The recently proposed PET-enabled DECT method allows dual-energy imaging using a conventional PET/CT scanner without the need to change scanner hardware or increase radiation exposure. Here we demonstrate the first-time physical phantom and patient data evaluation of this method.
Methods
The PET-enabled DECT method reconstructs a gamma-ray CT (gCT) image at 511 keV from the time-of-flight PET data with the maximum-likelihood attenuation and activity (MLAA) approach and then combines this image with the low-energy x-ray CT images to form a dual-energy image pair for material decomposition. To improve the image quality of gCT, a kernel MLAA method was developed using the x-ray CT as
a priori
information. Here we developed a general open-source implementation for gCT reconstruction and used this implementation for the first real data validation using both physical phantom study and human-subject study. Results from PET-enabled DECT were compared using x-ray DECT as the reference. Further, we applied the PET-enabled DECT method in another patient study to evaluate bone lesions.
Results
Compared to the standard MLAA, results from the kernel MLAA showed significantly improved image quality. PET-enabled DECT with the kernel MLAA was able to generate fractional images that were comparable to the x-ray DECT, with high correlation coefficients for both the phantom study and human subject study (
R
> 0.99). The application study also indicates that PET-enabled DECT has potential to characterize bone lesions.
Conclusion
Results from this study have demonstrated the feasibility of this PET-enabled method for CT imaging and material decomposition. PET-enabled DECT shows promise to provide comparable results to x-ray DECT. Dual-energy (DE) CT enables material decomposition by using two different x-ray energies and may be combined with PET for improved multimodality imaging. However, this increases radiation dose and may require a hardware upgrade due to the added second x-ray CT scan. The recently proposed PET-enabled DECT method allows dual-energy imaging using a conventional PET/CT scanner without the need to change scanner hardware or increase radiation exposure. Here we demonstrate the first-time physical phantom and patient data evaluation of this method. The PET-enabled DECT method reconstructs a gamma-ray CT (gCT) image at 511 keV from the time-of-flight PET data with the maximum-likelihood attenuation and activity (MLAA) approach and then combines this image with the low-energy x-ray CT images to form a dual-energy image pair for material decomposition. To improve the image quality of gCT, a kernel MLAA method was developed using the x-ray CT as a priori information. Here we developed a general open-source implementation for gCT reconstruction and used this implementation for the first real data validation using both physical phantom study and human-subject study. Results from PET-enabled DECT were compared using x-ray DECT as the reference. Further, we applied the PET-enabled DECT method in another patient study to evaluate bone lesions. Compared to the standard MLAA, results from the kernel MLAA showed significantly improved image quality. PET-enabled DECT with the kernel MLAA was able to generate fractional images that were comparable to the x-ray DECT, with high correlation coefficients for both the phantom study and human subject study (R > 0.99). The application study also indicates that PET-enabled DECT has potential to characterize bone lesions. Results from this study have demonstrated the feasibility of this PET-enabled method for CT imaging and material decomposition. PET-enabled DECT shows promise to provide comparable results to x-ray DECT. Dual-energy (DE) CT enables material decomposition by using two different x-ray energies and may be combined with PET for improved multimodality imaging. However, this increases radiation dose and may require a hardware upgrade due to the added second x-ray CT scan. The recently proposed PET-enabled DECT method allows dual-energy imaging using a conventional PET/CT scanner without the need to change scanner hardware or increase radiation exposure. Here we demonstrate the first-time physical phantom and patient data evaluation of this method.PURPOSEDual-energy (DE) CT enables material decomposition by using two different x-ray energies and may be combined with PET for improved multimodality imaging. However, this increases radiation dose and may require a hardware upgrade due to the added second x-ray CT scan. The recently proposed PET-enabled DECT method allows dual-energy imaging using a conventional PET/CT scanner without the need to change scanner hardware or increase radiation exposure. Here we demonstrate the first-time physical phantom and patient data evaluation of this method.The PET-enabled DECT method reconstructs a gamma-ray CT (gCT) image at 511 keV from the time-of-flight PET data with the maximum-likelihood attenuation and activity (MLAA) approach and then combines this image with the low-energy x-ray CT images to form a dual-energy image pair for material decomposition. To improve the image quality of gCT, a kernel MLAA method was developed using the x-ray CT as a priori information. Here we developed a general open-source implementation for gCT reconstruction and used this implementation for the first real data validation using both physical phantom study and human-subject study. Results from PET-enabled DECT were compared using x-ray DECT as the reference. Further, we applied the PET-enabled DECT method in another patient study to evaluate bone lesions.METHODSThe PET-enabled DECT method reconstructs a gamma-ray CT (gCT) image at 511 keV from the time-of-flight PET data with the maximum-likelihood attenuation and activity (MLAA) approach and then combines this image with the low-energy x-ray CT images to form a dual-energy image pair for material decomposition. To improve the image quality of gCT, a kernel MLAA method was developed using the x-ray CT as a priori information. Here we developed a general open-source implementation for gCT reconstruction and used this implementation for the first real data validation using both physical phantom study and human-subject study. Results from PET-enabled DECT were compared using x-ray DECT as the reference. Further, we applied the PET-enabled DECT method in another patient study to evaluate bone lesions.Compared to the standard MLAA, results from the kernel MLAA showed significantly improved image quality. PET-enabled DECT with the kernel MLAA was able to generate fractional images that were comparable to the x-ray DECT, with high correlation coefficients for both the phantom study and human subject study (R > 0.99). The application study also indicates that PET-enabled DECT has potential to characterize bone lesions.RESULTSCompared to the standard MLAA, results from the kernel MLAA showed significantly improved image quality. PET-enabled DECT with the kernel MLAA was able to generate fractional images that were comparable to the x-ray DECT, with high correlation coefficients for both the phantom study and human subject study (R > 0.99). The application study also indicates that PET-enabled DECT has potential to characterize bone lesions.Results from this study have demonstrated the feasibility of this PET-enabled method for CT imaging and material decomposition. PET-enabled DECT shows promise to provide comparable results to x-ray DECT.CONCLUSIONResults from this study have demonstrated the feasibility of this PET-enabled method for CT imaging and material decomposition. PET-enabled DECT shows promise to provide comparable results to x-ray DECT. PurposeDual-energy (DE) CT enables material decomposition by using two different x-ray energies and may be combined with PET for improved multimodality imaging. However, this increases radiation dose and may require a hardware upgrade due to the added second x-ray CT scan. The recently proposed PET-enabled DECT method allows dual-energy imaging using a conventional PET/CT scanner without the need to change scanner hardware or increase radiation exposure. Here we demonstrate the first-time physical phantom and patient data evaluation of this method.MethodsThe PET-enabled DECT method reconstructs a gamma-ray CT (gCT) image at 511 keV from the time-of-flight PET data with the maximum-likelihood attenuation and activity (MLAA) approach and then combines this image with the low-energy x-ray CT images to form a dual-energy image pair for material decomposition. To improve the image quality of gCT, a kernel MLAA method was developed using the x-ray CT as a priori information. Here we developed a general open-source implementation for gCT reconstruction and used this implementation for the first real data validation using both physical phantom study and human-subject study. Results from PET-enabled DECT were compared using x-ray DECT as the reference. Further, we applied the PET-enabled DECT method in another patient study to evaluate bone lesions.ResultsCompared to the standard MLAA, results from the kernel MLAA showed significantly improved image quality. PET-enabled DECT with the kernel MLAA was able to generate fractional images that were comparable to the x-ray DECT, with high correlation coefficients for both the phantom study and human subject study (R > 0.99). The application study also indicates that PET-enabled DECT has potential to characterize bone lesions.ConclusionResults from this study have demonstrated the feasibility of this PET-enabled method for CT imaging and material decomposition. PET-enabled DECT shows promise to provide comparable results to x-ray DECT. |
Author | Qi, Jinyi Wang, Guobao Omidvari, Negar Abdelhafez, Yasser G. Badawi, Ramsey D. Li, Siqi Leung, Edwin K. Xie, Zhaoheng Zhu, Yansong Cherry, Simon R. Bayerlein, Reimund Spencer, Benjamin A. |
AuthorAffiliation | 2 Department of Biomedical Engineering, University of California at Davis, Davis, CA 95616, U.S.A 1 Department of Radiology, UC Davis Health, Sacramento, CA 95817, U.S.A 3 UIH America, Inc., Houston, TX 77054, U.S.A |
AuthorAffiliation_xml | – name: 2 Department of Biomedical Engineering, University of California at Davis, Davis, CA 95616, U.S.A – name: 3 UIH America, Inc., Houston, TX 77054, U.S.A – name: 1 Department of Radiology, UC Davis Health, Sacramento, CA 95817, U.S.A |
Author_xml | – sequence: 1 givenname: Yansong orcidid: 0000-0002-3877-9369 surname: Zhu fullname: Zhu, Yansong email: yszhu@ucdavis.edu organization: Department of Radiology, UC Davis Health – sequence: 2 givenname: Siqi surname: Li fullname: Li, Siqi organization: Department of Radiology, UC Davis Health – sequence: 3 givenname: Zhaoheng surname: Xie fullname: Xie, Zhaoheng organization: Department of Biomedical Engineering, University of California at Davis – sequence: 4 givenname: Edwin K. surname: Leung fullname: Leung, Edwin K. organization: Department of Radiology, UC Davis Health, Department of Biomedical Engineering, University of California at Davis, UIH America, Inc – sequence: 5 givenname: Reimund surname: Bayerlein fullname: Bayerlein, Reimund organization: Department of Radiology, UC Davis Health, Department of Biomedical Engineering, University of California at Davis – sequence: 6 givenname: Negar surname: Omidvari fullname: Omidvari, Negar organization: Department of Biomedical Engineering, University of California at Davis – sequence: 7 givenname: Yasser G. surname: Abdelhafez fullname: Abdelhafez, Yasser G. organization: Department of Radiology, UC Davis Health – sequence: 8 givenname: Simon R. surname: Cherry fullname: Cherry, Simon R. organization: Department of Radiology, UC Davis Health, Department of Biomedical Engineering, University of California at Davis – sequence: 9 givenname: Jinyi surname: Qi fullname: Qi, Jinyi organization: Department of Biomedical Engineering, University of California at Davis – sequence: 10 givenname: Ramsey D. surname: Badawi fullname: Badawi, Ramsey D. organization: Department of Radiology, UC Davis Health, Department of Biomedical Engineering, University of California at Davis – sequence: 11 givenname: Benjamin A. surname: Spencer fullname: Spencer, Benjamin A. organization: Department of Radiology, UC Davis Health, Department of Biomedical Engineering, University of California at Davis – sequence: 12 givenname: Guobao surname: Wang fullname: Wang, Guobao organization: Department of Radiology, UC Davis Health |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39549045$$D View this record in MEDLINE/PubMed |
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Keywords | Dual-energy CT Real data validation PET-enabled dual-energy CT Kernel method |
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Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 GW and YZ conceived the concept and designed the study. YZ developed the implementations, conducted the evaluations, and analyzed the results. BAS designed and performed the phantom scans. SL, ZX, EKL, RB, NO, YGA, SRC, JQ, and RDB contributed to the study methods and materials. YGA and RDB also contributed to data interpretation. The first draft of the manuscript was written by YZ and revised by GW, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Author contributions |
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Dual-energy (DE) CT enables material decomposition by using two different x-ray energies and may be combined with PET for improved multimodality... Dual-energy (DE) CT enables material decomposition by using two different x-ray energies and may be combined with PET for improved multimodality imaging.... PurposeDual-energy (DE) CT enables material decomposition by using two different x-ray energies and may be combined with PET for improved multimodality... |
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SubjectTerms | Bone lesions Cardiology Computed tomography Correlation coefficients Decomposition Feasibility Studies Gamma rays Hardware Humans Image Processing, Computer-Assisted Image quality Image reconstruction Imaging Lesions Male Medicine Medicine & Public Health Nuclear Medicine Oncology Original Article Orthopedics Phantoms, Imaging Positron emission Positron emission tomography Positron Emission Tomography Computed Tomography - instrumentation Positron Emission Tomography Computed Tomography - methods Radiation Radiation dosage Radiation effects Radiology Scanners Tomography, X-Ray Computed Upgrading X-rays |
Title | Feasibility of PET-enabled dual-energy CT imaging: First physical phantom and initial patient study results |
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