Validation of low-dose lung cancer PET-CT protocol and PET image improvement using machine learning

Nai, Ying-Hwey; Schaefferkoetter, Josh; Fakhry-Darian, Daniel; O'Doherty, Sophie; Totman, John J.; Conti, Maurizio; Townsend, David W.; Sinha, Arvind; Tan, Teng-Hwee; Tham, Ivan; Alexander, Daniel C.; Reilhac, Anthonin

doi:10.1016/j.ejmp.2020.11.027

Cited by 10 publications

(5 citation statements)

References 25 publications

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“…Positron emission tomography (PET) is a quantitative imaging modality that is used to study functional processes using specific radiotracers (e.g. metabolism using [ 18 F]-fluorodeoxyglucose (FDG), prostate cancer detection using [ 68 Ga]-PSMA). The quality and quantitative accuracy of PET images are influenced by several factors such as scanner specifications (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…DLE methods have shown promising performance for image denoising [9] with comparable performance to DLR methods [14]. Moreover, image-based machine/deep learning techniques have enabled ultra-low dose PET scans maintaining clinically relevant information in terms of diagnostic accuracy and quantitative SUV measurements [18,19]. The great potential for DLE motivated the current study, in which we trained and evaluated a DLE model.…”

Section: Introductionmentioning

confidence: 99%

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Image enhancement of whole-body oncology [18F]-FDG PET scans using deep neural networks to reduce noise

Mehranian

Wollenweber²,

Walker

et al. 2021

Eur J Nucl Med Mol Imaging

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Purpose To enhance the image quality of oncology [18F]-FDG PET scans acquired in shorter times and reconstructed by faster algorithms using deep neural networks. Methods List-mode data from 277 [18F]-FDG PET/CT scans, from six centres using GE Discovery PET/CT scanners, were split into ¾-, ½- and ¼-duration scans. Full-duration datasets were reconstructed using the convergent block sequential regularised expectation maximisation (BSREM) algorithm. Short-duration datasets were reconstructed with the faster OSEM algorithm. The 277 examinations were divided into training (n = 237), validation (n = 15) and testing (n = 25) sets. Three deep learning enhancement (DLE) models were trained to map full and partial-duration OSEM images into their target full-duration BSREM images. In addition to standardised uptake value (SUV) evaluations in lesions, liver and lungs, two experienced radiologists scored the quality of testing set images and BSREM in a blinded clinical reading (175 series). Results OSEM reconstructions demonstrated up to 22% difference in lesion SUVmax, for different scan durations, compared to full-duration BSREM. Application of the DLE models reduced this difference significantly for full-, ¾- and ½-duration scans, while simultaneously reducing the noise in the liver. The clinical reading showed that the standard DLE model with full- or ¾-duration scans provided an image quality substantially comparable to full-duration scans with BSREM reconstruction, yet in a shorter reconstruction time. Conclusion Deep learning–based image enhancement models may allow a reduction in scan time (or injected activity) by up to 50%, and can decrease reconstruction time to a third, while maintaining image quality.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image enhancement of whole-body oncology [18F]-FDG PET scans using deep neural networks to reduce noise

Mehranian

Wollenweber²,

Walker

et al. 2021

Eur J Nucl Med Mol Imaging

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show abstract

“…Our approximation differs from Fahey et al [22] recommendations, who proposed to maintain tube voltage at 120 kVp and to use the lowest tube current-time product setting (5 mAs). Nai et al [27] have also proposed a low dose PET/CT protocol for lung cancer screening with reduced current-time product (from 140 mAs to 40 mAs with CAE) but tube voltage fixed at 120 kV. However, in our experience, the reduction of tube voltage from 120 kV to 100 kV achieves a significant CTDI vol reduction without any image degradation.…”

Section: Discussionmentioning

confidence: 74%

Ultra-low dose whole-body CT for attenuation correction in a dual tracer PET/CT protocol for multiple myeloma

et al. 2021

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“…Artificial intelligence has had a substantial global impact across all fields, and nuclear medicine is no exception. Numerous machine or deep learning techniques have been developed to reduce noise, and nonlinear transformations have been studied to predict standard doses based on low-dose images [84][85][86][87]. In addition, manufacturers have made attempts to improve PET image quality by developing AI filters that can perform Timeof-Flight (ToF) modeling, even for non-ToF systems [88].…”

Section: Future Perspectivesmentioning

confidence: 99%

Current and Future Use of Long Axial Field-of-View Positron Emission Tomography/Computed Tomography Scanners in Clinical Oncology

Roya,

Mostafapour,

Mohr

et al. 2023

Cancers

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The latest technical development in the field of positron emission tomography/computed tomography (PET/CT) imaging has been the extension of the PET axial field-of-view. As a result of the increased number of detectors, the long axial field-of-view (LAFOV) PET systems are not only characterized by a larger anatomical coverage but also by a substantially improved sensitivity, compared with conventional short axial field-of-view PET systems. In clinical practice, this innovation has led to the following optimization: (1) improved overall image quality, (2) decreased duration of PET examinations, (3) decreased amount of radioactivity administered to the patient, or (4) a combination of any of the above. In this review, novel applications of LAFOV PET in oncology are highlighted and future directions are discussed.

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Validation of low-dose lung cancer PET-CT protocol and PET image improvement using machine learning

Cited by 10 publications

References 25 publications

Image enhancement of whole-body oncology [18F]-FDG PET scans using deep neural networks to reduce noise

Image enhancement of whole-body oncology [18F]-FDG PET scans using deep neural networks to reduce noise

Ultra-low dose whole-body CT for attenuation correction in a dual tracer PET/CT protocol for multiple myeloma

Current and Future Use of Long Axial Field-of-View Positron Emission Tomography/Computed Tomography Scanners in Clinical Oncology

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