Single-Subject Deep-Learning Image Reconstruction With a Neural Optimization Transfer Algorithm for PET-Enabled Dual-Energy CT Imaging

Combining dual-energy computed tomography (DECT) with positron emission tomography (PET) offers many potential clinical applications but typically requires expensive hardware upgrades or increases radiation doses on PET/CT scanners due to an extra X-ray CT scan. The recent PET-enabled DECT method al...

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Vydáno v:IEEE transactions on image processing Ročník 33; s. 4075 - 4089
Hlavní autoři: Li, Siqi, Zhu, Yansong, Spencer, Benjamin A., Wang, Guobao
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Artificial neural networks
Computed tomography
Computer simulation
convolutional neural-network
Deep Learning
Humans
Image Processing, Computer-Assisted - methods
Image quality
Image reconstruction
Imaging
Kernel
kernel methods
Machine learning
Medical imaging
Neural Networks, Computer
Optimization
PET-enabled dual-energy CT
PET/CT
Phantoms, Imaging
Positron emission
Positron emission tomography
Positron Emission Tomography Computed Tomography - methods
Radiation dosage
Tomography
Tomography, X-Ray Computed - methods
Upgrading
X-ray imaging
ISSN:1057-7149, 1941-0042, 1941-0042
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Popis
Shrnutí:Combining dual-energy computed tomography (DECT) with positron emission tomography (PET) offers many potential clinical applications but typically requires expensive hardware upgrades or increases radiation doses on PET/CT scanners due to an extra X-ray CT scan. The recent PET-enabled DECT method allows DECT imaging on PET/CT without requiring a second X-ray CT scan. It combines the already existing X-ray CT image with a 511 keV <inline-formula> <tex-math notation="LaTeX">\gamma </tex-math></inline-formula>-ray CT (gCT) image reconstructed from time-of-flight PET emission data. A kernelized framework has been developed for reconstructing gCT image but this method has not fully exploited the potential of prior knowledge. Use of deep neural networks may explore the power of deep learning in this application. However, common approaches require a large database for training, which is impractical for a new imaging method like PET-enabled DECT. Here, we propose a single-subject method by using neural-network representation as a deep coefficient prior to improving gCT image reconstruction without population-based pre-training. The resulting optimization problem becomes the tomographic estimation of nonlinear neural-network parameters from gCT projection data. This complicated problem can be efficiently solved by utilizing the optimization transfer strategy with quadratic surrogates. Each iteration of the proposed neural optimization transfer algorithm includes: PET activity image update; gCT image update; and least-square neural-network learning in the gCT image domain. This algorithm is guaranteed to monotonically increase the data likelihood. Results from computer simulation, real phantom data and real patient data have demonstrated that the proposed method can significantly improve gCT image quality and consequent multi-material decomposition as compared to other methods.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2024.3418347