PET/CT Imaging

CT: Computed Tomography · PET: Positron Emission Tomography

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2 papers in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.

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Virtual Scanning for NSCLC Histology: Investigating the Discriminatory Power of Synthetic PET
May 4, 2026Fatih Aksu, Laura Ciuffetti, Francesco Di Feola +6PET/CT Imaging Multimodal Classification
Università Campus Bio-Medico di Roma · Umeå University · Università Vita-Salute San Raffaele
Advanced Tumor Segmentation in PET/CT Imaging: A Training Strategy Study with nnU-Net for AutoPET III
May 4, 2026Hussain AlasmawiPET/CT Imaging Medical Image Segmentation
Mohamed bin Zayed University of Artificial Intelligence
CT-Guided Spatially-varying Regularization for Voxel-Wise Deformable Whole-Body PET Registration
Apr 24, 2026Xiangcen Wu, Ruohua Chen, Sichun Li +4Medical Image Registration PET/CT Imaging
Institute of Medical Technology, Peking University Health Science Center, Beijing, China · Ren Ji Hospital, Shanghai Jiao Tong University, Shanghai, China · University of Oxford, Oxford, United Kingdom
Generalizable CT-Free PET Attenuation and Scatter Correction for Pediatric Patients
Apr 24, 2026Jia-Mian Wu, Jun Liu, Siqi Li +7Medical Imaging Domain Generalization
School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu 611130, China · Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China · Department of Radiology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 100045, China
Region-Grounded Report Generation for 3D Medical Imaging: A Fine-Grained Dataset and Graph-Enhanced Framework
Apr 20, 2026Cong Huy Nguyen, Son Dinh Nguyen, Guanlin Li +8Radiology Report Generation Medical Report Generation
AI4LIFE, Hanoi University of Science and Technology, Vietnam · SAMOVAR, Télécom SudParis, Institut Polytechnique de Paris, France · Military Central Hospital, Vietnam
Rethinking Cross-Dose PET Denoising: Mitigating Averaging Effects via Residual Noise Learning
Apr 18, 2026Yichao Liu, Zongru Shao, Yueyang Teng +1Residual Learning PET/CT Imaging
IWR, Heidelberg University, 69120, Heidelberg, Germany · Silicon Austria Labs, 4040, Linz, Austria · College of Medicine and Biological Information Engineering, Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, 110169, Shenyang, China +1

May 4, 2026, cs.CVJ/K move · Enter open · S save

Virtual Scanning for NSCLC Histology: Investigating the Discriminatory Power of Synthetic PET

Fatih Aksu, Laura Ciuffetti, Francesco Di Feola, Filippo Ruffini+5

Università Campus Bio-Medico di Roma · Umeå University · Università Vita-Salute San Raffaele

PET/CT Imaging Multimodal Classification Medical Image Synthesis

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Accurate histological differentiation between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) is critical for personalized treatment in non-small cell lung cancer (NSCLC). While [

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F]FDG PET/CT is a standard tool for the clinical evaluation of lung cancer, its utility is often limited by high costs and radiation exposure. In this paper, we investigate the feasibility of "virtual scanning" as a feature-enhancement strategy by evaluating whether synthetic PET data can provide complementary feature representations to supplement anatomical CT scans in histological subtype classification. We propose a framework that leverages a 3D Pix2Pix Generative Adversarial Network (GAN), pretrained on the FDG-PET/CT Lesions dataset, to synthesize pseudo-PET volumes from anatomical CT scans. These synthetic volumes are integrated with structural CT data within the MINT framework, a multi-stage intermediate fusion architecture. Our experiments, conducted on a multi-center dataset of 714 subjects, demonstrate that the inclusion of synthetic metabolic features significantly improves classification performance over a CT-only baseline. The multimodal approach achieved a statistically significant increase in the Area Under the Curve (AUC) from 0.489 to 0.591 and improved the Geometric Mean (GMean) from 0.305 to 0.524. These results suggest that synthetic PET scans provide discriminatory metabolic cues that enable deep learning models to exploit complementary cross-modal information, offering a potential feature-enhancement strategy for clinical scenarios where physical PET scans are unavailable.

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Latest papers 36

Virtual Scanning for NSCLC Histology: Investigating the Discriminatory Power of Synthetic PET

Advanced Tumor Segmentation in PET/CT Imaging: A Training Strategy Study with nnU-Net for AutoPET III

CT-Guided Spatially-varying Regularization for Voxel-Wise Deformable Whole-Body PET Registration

Generalizable CT-Free PET Attenuation and Scatter Correction for Pediatric Patients

Region-Grounded Report Generation for 3D Medical Imaging: A Fine-Grained Dataset and Graph-Enhanced Framework

Rethinking Cross-Dose PET Denoising: Mitigating Averaging Effects via Residual Noise Learning