AIMC Topic: Positron Emission Tomography Computed Tomography
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Prediction of Lymph Node Metastasis in Lung Cancer Using Deep Learning of Endobronchial Ultrasound Images With Size on CT and PET-CT Findings.
Respirology (Carlton, Vic.)
Mar 3, 2025
BACKGROUND AND OBJECTIVE: Echo features of lymph nodes (LNs) influence target selection during endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA). This study evaluates deep learning's diagnostic capabilities on EBUS images f...
Data-efficient generalization of AI transformers for noise reduction in ultra-fast lung PET scans.
European journal of nuclear medicine and molecular imaging
Feb 26, 2025
PURPOSE: Respiratory motion during PET acquisition may produce lesion blurring. Ultra-fast 20-second breath-hold (U2BH) PET reduces respiratory motion artifacts, but the shortened scanning time increases statistical noise and may affect diagnostic qu...
Artificial intelligence algorithm for preoperative prediction of FIGO stage in ovarian cancer based on clinical features integrated 18F-FDG PET/CT metabolic and radiomics features.
Journal of cancer research and clinical oncology
Feb 20, 2025
PURPOSE: The International Federation of Gynecology and Obstetric (FIGO) stage is critical to guiding the treatments of ovarian cancer (OC). We tried to develop a model to predict the FIGO stage of OC through machine learning algorithms with patients...
Prediction of adverse pathology in prostate cancer using a multimodal deep learning approach based on [F]PSMA-1007 PET/CT and multiparametric MRI.
European journal of nuclear medicine and molecular imaging
Feb 19, 2025
PURPOSE: Accurate prediction of adverse pathology (AP) in prostate cancer (PCa) patients is crucial for formulating effective treatment strategies. This study aims to develop and evaluate a multimodal deep learning model based on [F]PSMA-1007 PET/CT ...
Automated quantification of brain PET in PET/CT using deep learning-based CT-to-MR translation: a feasibility study.
European journal of nuclear medicine and molecular imaging
Feb 18, 2025
PURPOSE: Quantitative analysis of PET images in brain PET/CT relies on MRI-derived regions of interest (ROIs). However, the pairs of PET/CT and MR images are not always available, and their alignment is challenging if their acquisition times differ c...
Predicting malignant risk of ground-glass nodules using convolutional neural networks based on dual-time-point F-FDG PET/CT.
Cancer imaging : the official publication of the International Cancer Imaging Society
Feb 18, 2025
BACKGROUND: Accurately predicting the malignant risk of ground-glass nodules (GGOs) is crucial for precise treatment planning. This study aims to utilize convolutional neural networks based on dual-time-point F-FDG PET/CT to predict the malignant ris...
Comparative analysis of intestinal tumor segmentation in PET CT scans using organ based and whole body deep learning.
BMC medical imaging
Feb 17, 2025
BACKGROUND: 18-Fluoro-deoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) is a valuable imaging tool widely used in the management of cancer patients. Deep learning models excel at segmenting highly metabolic tumors but face ch...
Eliminating the second CT scan of dual-tracer total-body PET/CT via deep learning-based image synthesis and registration.
European journal of nuclear medicine and molecular imaging
Feb 11, 2025
PURPOSE: This study aims to develop and validate a deep learning framework designed to eliminate the second CT scan of dual-tracer total-body PET/CT imaging.
Optimizing MR-based attenuation correction in hybrid PET/MR using deep learning: validation with a flatbed insert and consistent patient positioning.
European journal of nuclear medicine and molecular imaging
Feb 6, 2025
PURPOSE: To address the challenges of verifying MR-based attenuation correction (MRAC) in PET/MR due to CT positional mismatches and alignment issues, this study utilized a flatbed insert and arms-down positioning during PET/CT scans to achieve preci...
Deep Learning-Based Precontrast CT Parcellation for MRI-Free Brain Amyloid PET Quantification.
Clinical nuclear medicine
Jan 29, 2025
PURPOSE: This study aimed to develop a deep learning (DL) model for brain region parcellation using CT data from PET/CT scans to enable accurate amyloid quantification in 18 F-FBB PET/CT without relying on high-resolution MRI.
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