Evidence map›Paper›PMID 33120376›Full record

ArticlePhysics in medicine and biology2020

PET-enabled dual-energy CT: image reconstruction and a proof-of-concept computer simulation study.

Guobao Wang

Open access · greenAbstract read
In one paragraph

Article in Physics in medicine and biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.7field-weighted citation impact, top 33% of its field
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Systematic Review on Learning-based Spectral CT.IEEE transactions on radiation and plasma medical sciences · 2024
    Article
  5. Super-resolution reconstruction ofProceedings of SPIE--the International Society for Optical Engineering · 2024
    Article
  6. Synergistic tomographic image reconstruction: part 2.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021
    Article
  7. Modified kernel MLAA using autoencoder for PET-enabled dual-energy CT.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021
    Article
  8. Neural MLAA for PET-enabled Dual-Energy CT Imaging.Proceedings of SPIE--the International Society for Optical Engineering · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author at 1 institution in 1 country.

Guobao WangDepartment of Radiology, University of California, Davis, CA, United States of America.ORCID 0000-0001-6526-1533
University of California, Davis · US

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

Standard dual-energy computed tomography (CT) uses two different x-ray energies to obtain energy-dependent tissue attenuation information to allow quantitative material decomposition. The combined use of dual-energy CT and positron emission tomography (PET) may provide a more comprehensive characterization of disease states in cancer and other diseases. However, the integration of dual-energy CT with PET is not trivial, either requiring costly hardware upgrades or increasing radiation exposure. This paper proposes a different dual-energy CT imaging method that is enabled by PET. Instead of using a second x-ray CT scan with a different energy, this method exploits time-of-flight PET image reconstruction via the maximum likelihood attenuation and activity (MLAA) algorithm to obtain a 511 keV gamma-ray attenuation image from PET emission data. The high-energy gamma-ray attenuation image is then combined with the low-energy x-ray CT of PET/CT to provide a pair of dual-energy CT images. A major challenge with the standard MLAA reconstruction is the high noise present in the reconstructed 511 keV attenuation map, which would not compromise the PET activity reconstruction too much but may significantly affect the performance of the gamma-ray attenuation image for material decomposition. To overcome the problem, we further propose a kernel MLAA algorithm to exploit the prior information from the available x-ray CT image. We conducted a computer simulation to test the concept and algorithm for the task of material decomposition. The simulation results demonstrate that this PET-enabled dual-energy CT method is promising for quantitative material decomposition. The proposed method can be readily implemented on time-of-flight PET/CT scanners to enable simultaneous PET and dual-energy CT imaging.

Indexed as

Computer SimulationPositron Emission Tomography Computed TomographyAlgorithmsHumansImage Processing, Computer-AssistedPhantoms, Imaging

Identifiers

PMID33120376
PMCPMC8582600
OpenAlexW3080566337

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.