Employing Multiple Low-Dose PET Images (at Different Dose Levels) as Prior Knowledge to Predict Standard-Dose PET Images

Sanaei, Behnoush; Faghihi, Reza; Arabi, Hossein

doi:10.1007/s10278-023-00815-y

J Digit Imaging

2023

DOI: 10.1007/s10278-023-00815-y

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Employing Multiple Low-Dose PET Images (at Different Dose Levels) as Prior Knowledge to Predict Standard-Dose PET Images

Behnoush Sanaei

Reza Faghihi

Hossein Arabi

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Cited by 3 publications

(2 citation statements)

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“…25 Even when PET images are collected from the same patient and reconstructed using the same algorithm, their noise levels can vary greatly due to the use of different scanners, scanning times and so on. 10,26 Besides, PET image noise levels may differ across individuals and medical devices. 20 For example, the injection doses of PET scans for children and the elderly are typically lower than those for adolescents.…”

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Adaptive 3D noise level‐guided restoration network for low‐dose positron emission tomography imaging

Huang

Zhou

et al. 2023

Interdisciplinary Medicine

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Many deep learning methods have been proposed to improve the quality of low‐dose PET images (LPET), which usually construct end‐to‐end networks with certain radiation dose inputs. However, these approaches have omitted the noise disparity in PET images, which may differ among manufacturers or populations. Therefore, we tend to exploit these noise differences among PET images to achieve adaptive restoration. We proposed a 3D noise level‐guided PET restoration network for LPET including (1) adaptive noise level‐aware subnetwork and (2) LPET restoration subnetwork. The first subnetwork aims to predict the noise level of the given LPET, while the second subnetwork treats the estimated noise level as a priori information to guide the restoration process from LPET to standard‐dose PET images. Experiments were performed on real human head and neck datasets while the peak signal‐to‐noise ratio and structural similarity index measure were used to evaluate LPET recovery performance. Moreover, we also compared the proposed network with several deep‐learning approaches. Experimental results demonstrate that our network with dual‐stage design can perform adaptive restoration for LPET, yielding better visual and quantitative results. In future work, we attempt to apply our method to other imaging tasks and adapt it for clinical practice.

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confidence: 99%

“…9 Although iterative reconstruction can sometimes achieve good performance, the reconstructed images still suffer from the loss of image details and blurred edges. 10 In addition, the computational cost of iterative reconstruction tends to be high, which has difficulties in practical applications.…”

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confidence: 99%

Adaptive 3D noise level‐guided restoration network for low‐dose positron emission tomography imaging

Huang

Zhou

et al. 2023

Interdisciplinary Medicine

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Transformer-Integrated Hybrid Convolutional Neural Network for Dose Prediction in Nasopharyngeal Carcinoma Radiotherapy

Li,

Liu,

Zhao

et al. 2024

J Digit Imaging. Inform. med.

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Deep learning-based PET image denoising and reconstruction: a review

Hashimoto,

Onishi,

Ote

et al. 2024

Radiol Phys Technol

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This review focuses on positron emission tomography (PET) imaging algorithms and traces the evolution of PET image reconstruction methods. First, we provide an overview of conventional PET image reconstruction methods from filtered backprojection through to recent iterative PET image reconstruction algorithms, and then review deep learning methods for PET data up to the latest innovations within three main categories. The first category involves post-processing methods for PET image denoising. The second category comprises direct image reconstruction methods that learn mappings from sinograms to the reconstructed images in an end-to-end manner. The third category comprises iterative reconstruction methods that combine conventional iterative image reconstruction with neural-network enhancement. We discuss future perspectives on PET imaging and deep learning technology.

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Employing Multiple Low-Dose PET Images (at Different Dose Levels) as Prior Knowledge to Predict Standard-Dose PET Images

Cited by 3 publications

References 26 publications

Adaptive 3D noise level‐guided restoration network for low‐dose positron emission tomography imaging

Adaptive 3D noise level‐guided restoration network for low‐dose positron emission tomography imaging

Transformer-Integrated Hybrid Convolutional Neural Network for Dose Prediction in Nasopharyngeal Carcinoma Radiotherapy

Deep learning-based PET image denoising and reconstruction: a review

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