In this study, 18F-FDG PET/CT brain scans of 50 patients with head and neck malignant lesions were employed to systematically assess the relationship between the amount of injected dose (10%, 8%, 6%, 5%, 4%, 3%, 2%, and 1% of standard dose) and the image quality through measuring standard image quality metrics (peak-signal-to-noise-ration (PSNR), structural similarity index (SSIM), root mean square error (RMSE), and standard uptake value (SUV) bias) for the whole head region as well as within the malignant lesions, considering the standard-dose PET images as reference. Furthermore, we evaluated the impact of post-reconstruction Gaussian filtering on the PET images in order to reduce noise and improve the signal-to-noise ratio at different low-dose levels. Significant degradation of PET image quality and tumor detectability was observed with a decrease in the injected dose by more than 5%, leading to a remarkable increase in RMSE from 0.173 SUV (at 5%) to 1.454 SUV (at 1%). The quantitative investigation of the malignant lesions demonstrated that SUVmax bias greatly increased in low-dose PET images (in particular at 1%, 2%, 3% levels) before applying the post-reconstruction filter, while applying the Gaussian filter on low-dose PET images led to a significant reduction in SUVmax bias. The SUVmean bias within the malignant lesions was negligible (less than 1%) in low-dose PET images; however, this bias increased significantly after applying the post-reconstruction filter. In conclusion, it is strongly recommended that the SUVmax bias and SUVmean bias in low-dose PET images should be considered prior to and following the application of the post-reconstruction Guassain filter, respectively.
Cycle-Constrained Adversarial Denoising Convolutional Network for PET Image Denoising: Multi-Dimensional Validation on Large Datasets with Reader Study and Real Low-Dose Data
Exploiting network optimization stability for enhanced PET image denoising using deep image prior
Central Research Laboratory, Hamamatsu Photonics K. K.（滨松光子学株式会社中央研究所）；Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology (QST)（国立量子科学技术研究开发机构量子医学科学研究所）；J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida（佛罗里达大学J. Crayton Pruitt家族生物医学工程系）
Reliability of PET/CT shape and heterogeneity features in functional and morphological components of Non-Small Cell Lung Cancer tumors: a repeatability analysis in a prospective multi-center cohort
INSERM, UMR 1101, LaTIM, University of Brest, IBSAM（法国国家健康与医学研究院，UMR 1101，LaTIM，布雷斯特大学，IBSAM）；Academic department of nuclear Medicine, CHU Milétrie（米莱特里大学医院核医学科）；Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）；Mallinckrodt Institute of Radiology and the Siteman Cancer Center, Washington University School of Medicine（华盛顿大学医学院马林克罗特放射学研究所及Siteman癌症中心）
Post-Radiotherapy PET Image Outcome Prediction by Deep Learning under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application
University of California Los Angeles（加利福尼亚大学洛杉矶分校）；Duke University Medical Center（杜克大学医学中心）
Towards Routine AI-Based PET/CT and SPECT/CT Lesion Segmentation and Tracking in PSMA Theranostics
BC Cancer Research Institute（BC癌症研究所）；University of British Columbia（不列颠哥伦比亚大学）；Université Laval（拉瓦尔大学）；CHU de Québec - Université Laval Research Centre（魁北克大学医院研究中心-拉瓦尔大学）；CHU de Québec – Université Laval（魁北克大学医院-拉瓦尔大学）
Generative-Model-Based Fully 3D PET Image Reconstruction by Conditional Diffusion Sampling
School of Biomedical Engineering and Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与影像科学学院）；Institute of Psychiatry, Psychology and Neuroscience, King’s College London（伦敦国王学院精神病学、心理学与神经科学研究所）；King’s College London & Guy’s and St Thomas’ PET Centre（伦敦国王学院与盖伊和圣托马斯 PET 中心）
Machine Learning-Based Inter-Crystal Scatter Recovery for Ultra-High Resolution PET Imaging
Université de Sherbrooke（谢布鲁克大学）；Interdisciplinary Institute for Technological Innovation（跨学科技术创新研究所）；Sherbrooke Molecular Imaging Center of CRCHUS（CRCHUS谢布鲁克分子成像中心）；Institut Fresnel（弗雷内尔研究所）；Aix Marseille Univ（艾克斯-马赛大学）；Centrale Med（中央医学院）；Imaging Research & Technology (IR&T) Inc.（成像研究与技术公司）
Multibranch Generative Models for Multichannel Imaging with an Application to PET/CT Synergistic Reconstruction
Univ. Brest, LaTIM, INSERM, UMR 1101（布雷斯特大学、LaTIM研究所、法国国家健康与医学研究院、UMR 1101联合研究单位）；Nuclear Medicine Department, Poitiers University Hospital（普瓦捷大学医院核医学科）
Patlak Parametric Image Estimation from Dynamic PET Using Diffusion Model Prior
J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida（J. Crayton Pruitt家族生物医学工程系，佛罗里达大学）；Department of Radiology, University of Florida（放射学系，佛罗里达大学）；Interdisciplinary Program in Bioengineering, Seoul National University（生物工程跨学科项目，首尔国立大学）；Department of Nuclear Medicine, Seoul National University College of Medicine（核医学系，首尔国立大学医学院）；Department of Radiology, University of California Davis Health（放射学系，加州大学戴维斯医学中心）
A Positron Range Correction with Texture Preservation Framework in PET Imaging
Universidad Complutense de Madrid（马德里康普顿斯大学）；Instituto de Investigacion Sanitaria, Hospital Clinico San Carlos (IdISSC)（圣卡洛斯临床医院卫生研究所）；Department of Nuclear Medicine, Bern University Hospital（伯尔尼大学医院核医学科）；Siemens Medical Solutions USA, Inc.（西门子医疗解决方案美国公司）
Model-based Deep Medical Imaging: the roadmap of generalizing iterative reconstruction model using deep learning
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院保罗·C·劳特伯生物医学成像研究中心）；Research center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院医学人工智能研究中心）；Nanchang University（南昌大学）；University at Buffalo, the State University of New York（纽约州立大学布法罗分校）
