Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Positron emission tomography (PET) is an ill-posed inverse problem and suffers high noise due to limited number of detected events. Prior information can be used to improve the quality of reconstructed PET images. Deep neural networks have also been applied to regularized image reconstruction. One method is to use a pretrained denoising neural network to represent the PET image and to perform a constrained maximum likelihood estimation. In this work, we propose to use a generative adversarial network (GAN) to further improve the network performance. We also modify the objective function to include a data-matching term on the network input. Experimental studies using computer-based Monte Carlo simulations and real patient datasets demonstrate that the proposed method leads to noticeable improvements over the kernel-based and U-net-based regularization methods in terms of lesion contrast recovery versus background noise trade-offs.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7413644 | PMC |
| http://dx.doi.org/10.1088/1361-6560/ab8f72 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Hybrid Convolutional Neural Network and Generative Adversarial Network Framework for Robust Deepfake Detection: Advancing Accuracy, Scalability, and Multimodal Integration.
J Vis Exp
August 2025
Department of Computer Science and Engineering, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India.
Deepfakes pose critical threats to digital media integrity and societal trust. This paper presents a hybrid deepfake detection framework combining Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) to address challenges in scalability, generalizability, and adversarial robustness. The framework integrates adversarial training, a temporal decay analysis model, and multimodal detection across audio, video, and text domains.
View Article and Find Full Text PDFAdversarial training for dynamics matching in coarse-grained models.
J Chem Phys
September 2025
Department of Chemistry, Chicago Center for Theoretical Chemistry, Institute for Biophysical Dynamics, and James Franck Institute, The University of Chicago, 5735 S. Ellis Ave., SCL 123, Chicago, Illinois 60637, USA.
Molecular dynamics simulations are essential for studying complex molecular systems, but their high computational cost limits scalability. Coarse-grained (CG) models reduce this cost by simplifying the system, yet traditional approaches often fail to maintain dynamic consistency, compromising their reliability in kinetics-driven processes. Here, we introduce an adversarial training framework that aligns CG trajectory ensembles with all-atom (AA) reference dynamics, ensuring both thermodynamic and kinetic fidelity.
View Article and Find Full Text PDFMultimodal Deep Learning for Generating Potential Anti-Dengue Peptides.
ACS Omega
September 2025
Division of Pharmaceutical Chemistry, Faculty of Pharmaceutical Sciences, Khon Kaen University, Khon Kaen 40002, Thailand.
Dengue virus remains a significant global health threat, imposing a substantial disease burden on nearly half of the world's population. The urgent need for effective antiviral therapeutics, including therapeutic peptides targeting the Dengue virus, is critical in the current healthcare landscape. However, the availability of anti-Dengue peptides (ADPs) data remains limited in existing data sets, posing a challenge for computational modeling and discovery.
View Article and Find Full Text PDFSurvey on sampling conditioned brain images and imaging measures with generative models.
Biomed Eng Lett
September 2025
Computer Science and Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro. Nam-Gu, Pohang, Gyeongbuk 37673 Korea.
Generative models have become innovative tools across various domains, including neuroscience, where they enable the synthesis of realistic brain imaging data that captures complex anatomical and functional patterns. These models, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and diffusion models, leverage deep learning to generate high-quality brain images while maintaining biological and clinical relevance. These models address critical challenges in brain imaging, e.
View Article and Find Full Text PDFLung nodule synthesis guided by customized multi-confidence masks.
Biomed Eng Lett
September 2025
Department of Radiology, Guizhou International Science and Technology Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guizhou Provincial People's Hospital, Guiyang, Guizhou China.
The generated lung nodule data plays an indispensable role in the development of intelligent assisted diagnosis of lung cancer. Existing generative models, primarily based on Generative Adversarial Networks (GANs) and Denoising Diffusion Probabilistic Models (DDPM), have demonstrated effectiveness but also come with certain limitations: GANs often produce artifacts and unnatural boundaries, and due to dataset limitations, they struggle with irregular nodules. While DDPMs are capable of generating a diverse range of nodules, their inherent randomness and lack of control limit their applicability in tasks such as segmentation.
View Article and Find Full Text PDF