Evidence map›Paper›PMID 39549045›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2025

Feasibility of PET-enabled dual-energy CT imaging: First physical phantom and initial patient study results.

Yansong Zhu, Siqi Li, Zhaoheng Xie, Edwin K Leung, Reimund Bayerlein, Negar Omidvari, Yasser G Abdelhafez, Simon R Cherry, Jinyi Qi, Ramsey D Badawi and 2 more

Erratum issuedAbstract read
In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Yansong ZhuDepartment of Radiology, UC Davis Health, 95817, Sacramento, CA, USA. yszhu@ucdavis.edu.ORCID 0000-0002-3877-9369
Siqi LiDepartment of Radiology, UC Davis Health, 95817, Sacramento, CA, USA.
Zhaoheng XieDepartment of Biomedical Engineering, University of California at Davis, 95616, Davis, CA, USA.
Edwin K LeungDepartment of Radiology, UC Davis Health, 95817, Sacramento, CA, USA.
Reimund BayerleinDepartment of Radiology, UC Davis Health, 95817, Sacramento, CA, USA.
Negar OmidvariDepartment of Biomedical Engineering, University of California at Davis, 95616, Davis, CA, USA.
Yasser G AbdelhafezDepartment of Radiology, UC Davis Health, 95817, Sacramento, CA, USA.
Simon R CherryDepartment of Radiology, UC Davis Health, 95817, Sacramento, CA, USA.
Jinyi QiDepartment of Biomedical Engineering, University of California at Davis, 95616, Davis, CA, USA.
Ramsey D BadawiDepartment of Radiology, UC Davis Health, 95817, Sacramento, CA, USA.
Benjamin A SpencerDepartment of Radiology, UC Davis Health, 95817, Sacramento, CA, USA.
Guobao WangDepartment of Radiology, UC Davis Health, 95817, Sacramento, CA, USA.

Funding

Staff InvestigatorsP30CA093373 · NCI · UNIVERSITY OF CALIFORNIA DAVIS · PI Gina M Dayton · 2002 to 2026
$84.9M
PET-enabled Dual-energy CTR21EB027346 · NIBIB · UNIVERSITY OF CALIFORNIA AT DAVIS · PI WANG, GUOBAO · 2019 to 2021
$628k
NCI NIH HHS P30 CA093373NIBIB NIH HHS R21 EB027346
6 · The paper itself

Abstract

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.

Indexed as

Phantoms, ImagingPositron Emission Tomography Computed TomographyTomography, X-Ray ComputedFeasibility StudiesHumansImage Processing, Computer-AssistedMaleDual-energy CTKernel methodPET-enabled dual-energy CTReal data validation

Identifiers

PMID39549045
PMCPMC11928277

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.