AIMC Topic: Positron Emission Tomography Computed Tomography
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Single-Subject Deep-Learning Image Reconstruction With a Neural Optimization Transfer Algorithm for PET-Enabled Dual-Energy CT Imaging.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Jul 4, 2024
Combining dual-energy computed tomography (DECT) with positron emission tomography (PET) offers many potential clinical applications but typically requires expensive hardware upgrades or increases radiation doses on PET/CT scanners due to an extra X-...
Nuclear medicine technologists practice impacted by AI denoising applications in PET/CT images.
Radiography (London, England : 1995)
Jun 24, 2024
PURPOSE: Artificial intelligence (AI) in positron emission tomography/computed tomography (PET/CT) can be used to improve image quality when it is useful to reduce the injected activity or the acquisition time. Particular attention must be paid to en...
Using machine learning to improve the diagnostic accuracy of the modified Duke/ESC 2015 criteria in patients with suspected prosthetic valve endocarditis - a proof of concept study.
European journal of nuclear medicine and molecular imaging
Jun 21, 2024
INTRODUCTION: Prosthetic valve endocarditis (PVE) is a serious complication of prosthetic valve implantation, with an estimated yearly incidence of at least 0.4-1.0%. The Duke criteria and subsequent modifications have been developed as a diagnostic ...
Multi-modal segmentation with missing image data for automatic delineation of gross tumor volumes in head and neck cancers.
Medical physics
Jun 19, 2024
BACKGROUND: Head and neck (HN) gross tumor volume (GTV) auto-segmentation is challenging due to the morphological complexity and low image contrast of targets. Multi-modality images, including computed tomography (CT) and positron emission tomography...
Explainable deep-learning-based ischemia detection using hybrid O-15 HO perfusion positron emission tomography and computed tomography imaging with clinical data.
Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
Jun 8, 2024
BACKGROUND: We developed an explainable deep-learning (DL)-based classifier to identify flow-limiting coronary artery disease (CAD) by O-15 HO perfusion positron emission tomography computed tomography (PET/CT) and coronary CT angiography (CTA) imagi...
Anatomically Guided PET Image Reconstruction Using Conditional Weakly-Supervised Multi-Task Learning Integrating Self-Attention.
IEEE transactions on medical imaging
Jun 3, 2024
To address the lack of high-quality training labels in positron emission tomography (PET) imaging, weakly-supervised reconstruction methods that generate network-based mappings between prior images and noisy targets have been developed. However, the ...
Clinical evaluation of deep learning-enhanced lymphoma pet imaging with accelerated acquisition.
Journal of applied clinical medical physics
May 29, 2024
PURPOSE: This study aims to evaluate the clinical performance of a deep learning (DL)-enhanced two-fold accelerated PET imaging method in patients with lymphoma.
Aged
Image Interpretation, Computer-Assisted
Aged, 80 and over
Young Adult
Fluorodeoxyglucose F18
Adult
Signal-To-Noise Ratio
Whole Body Imaging
Male
Lymphoma
Middle Aged
Positron Emission Tomography Computed Tomography
Deep Learning
Radiopharmaceuticals
Humans
Image Processing, Computer-Assisted
Prognosis
Female
Probability maps for deep learning-based head and neck tumor segmentation: Graphical User Interface design and test.
Computers in biology and medicine
May 28, 2024
BACKGROUND: The different tumor appearance of head and neck cancer across imaging modalities, scanners, and acquisition parameters accounts for the highly subjective nature of the manual tumor segmentation task. The variability of the manual contours...
Deep-learning features based on F18 fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) to predict preoperative colorectal cancer lymph node metastasis.
Clinical radiology
May 27, 2024
AIM: The objective of this study was to create and authenticate a prognostic model for lymph node metastasis (LNM) in colorectal cancer (CRC) that integrates clinical, radiomics, and deep transfer learning features.
Preliminary study on the ability of the machine learning models based on F-FDG PET/CT to differentiate between mass-forming pancreatic lymphoma and pancreatic carcinoma.
European journal of radiology
May 25, 2024
PURPOSE: The objective of this study was to preliminarily assess the ability of metabolic parameters and radiomics derived from F-fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) to distinguish mass-forming pancreati...
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