4.7 Article

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

Related references

Note: Only part of the references are listed.
Article Radiology, Nuclear Medicine & Medical Imaging

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

Ying-Hwey Nai et al.

Summary: The study aimed to conduct a lesion-detection task using a low-dose PET-CT protocol and investigate the feasibility of increasing clinical value of low-statistics scans through machine learning. Results showed that the LD PET-CT protocol had good performance in lesion detection, and machine learning methods could achieve substantial image quality improvement or additional dose reduction while preserving clinical values, although SUV quantification may be biased.

PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Artificial intelligence enables whole-body positron emission tomography scans with minimal radiation exposure

Yan-Ran (Joyce) Wang et al.

Summary: The study utilized a novel artificial intelligence algorithm to generate diagnostic F-18-FDG PET images of pediatric cancer patients from ultra-low-dose input images. The convolutional neural network (CNN) successfully reconstructed standard-dose PET images from simulated ultra-low-dose scans, improving image quality and diagnostic accuracy compared to traditional low-dose scans. The CNN augmentation also led to higher agreement in diagnostic confidence between standard clinical scans and AI-reconstructed PET scans.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Can a penalized-likelihood estimation algorithm be used to reduce the injected dose or the acquisition time in 68Ga-DOTATATE PET/CT studies?

Alexandre Chicheportiche et al.

Summary: The study compared the performance of Q.Clear and OSEM for Ga-68-DOTA PET studies, finding that Q.Clear at specific settings resulted in increased tumor SUVmax and improved signal-to-noise ratio (SNR) and signal-to-background ratio (SBR) at a similar noise level compared to 3D OSEM.

EJNMMI PHYSICS (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Artificial intelligence for reduced dose 18F-FDG PET examinations: a real-world deployment through a standardized framework and business case assessment

Katia Katsari et al.

Summary: This study demonstrated the non-inferiority of AI processed low-dose PET/CT images compared to standard dose native scans in terms of image quality and lesion detectability, with potential business benefits in cost savings.

EJNMMI PHYSICS (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Model-Based Deep Learning PET Image Reconstruction Using Forward-Backward Splitting Expectation-Maximization

Abolfazl Mehranian et al.

Summary: A forward-backward splitting algorithm incorporating deep learning into MAP PET image reconstruction was proposed. Experimental results showed comparable performance of the proposed algorithm to a U-Net denoising method.

IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES (2021)

Review Radiology, Nuclear Medicine & Medical Imaging

Deep Learning for PET Image Reconstruction

Andrew J. Reader et al.

Summary: This article reviews the application of deep learning in PET image reconstruction, discussing direct deep-learning methods and model-based deep-learning methods. Direct methods learn imaging physics and statistics from scratch, while model-based deep-learning utilizes existing advances in PET image reconstruction to replace conventional components with data-driven alternatives.

IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

FastPET: Near Real-Time Reconstruction of PET Histo-Image Data Using a Neural Network

William Whiteley et al.

Summary: Direct reconstruction of PET data using deep neural networks, FastPET is a novel convolutional neural network that is architecturally simple, memory efficient, suitable for 3-D image volumes, and significantly faster in reconstructing image volumes. Experimental results demonstrate that FastPET provides fast reconstruction, high-quality images, and lower noise level.

IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES (2021)

Article Engineering, Electrical & Electronic

Machine Learning in PET: From Photon Detection to Quantitative Image Reconstruction

Kuang Gong et al.

PROCEEDINGS OF THE IEEE (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

How fast can we scan patients with modern (digital) PET/CT systems?

Charline Lasnon et al.

EUROPEAN JOURNAL OF RADIOLOGY (2020)

Article Computer Science, Artificial Intelligence

Convolutional Neural Networks for Automated PET/CT Detection of Diseased Lymph Node Burden in Patients with Lymphoma

Amy J. Weisman et al.

RADIOLOGY-ARTIFICIAL INTELLIGENCE (2020)

Article Computer Science, Artificial Intelligence

DeepPET: A deep encoder-decoder network for directly solving the PET image reconstruction inverse problem

Ida Haggstrom et al.

MEDICAL IMAGE ANALYSIS (2019)

Article Engineering, Biomedical

An investigation of quantitative accuracy for deep learning based denoising in oncological PET

Wenzhuo Lu et al.

PHYSICS IN MEDICINE AND BIOLOGY (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

PET image denoising using unsupervised deep learning

Jianan Cui et al.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

Noise reduction using a Bayesian penalized-likelihood reconstruction algorithm on a time-of-flight PET-CT scanner

Paulo R. R. Caribe et al.

EJNMMI PHYSICS (2019)

Article Multidisciplinary Sciences

Image reconstruction by domain-transform manifold learning

Bo Zhu et al.

NATURE (2018)

Article Radiology, Nuclear Medicine & Medical Imaging

Phantom and Clinical Evaluation of the Bayesian Penalized Likelihood Reconstruction Algorithm Q.Clear on an LYSO PET/CT System

Eugene J. Teoh et al.

JOURNAL OF NUCLEAR MEDICINE (2015)

Article Radiology, Nuclear Medicine & Medical Imaging

FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0

Ronald Boellaard et al.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2015)

Review Engineering, Biomedical

Iterative reconstruction techniques in emission computed tomography

Jinyi Qi et al.

PHYSICS IN MEDICINE AND BIOLOGY (2006)