AIMC Topic: Fluorodeoxyglucose F18

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Normal twin PET: personalized generative modeling for confounder correction and anomaly detection in whole-body PET/CT.

Scientific reports
Variable physiological [F]FDG uptake patterns and a lack of labelled data make it challenging to automatically distinguish normal from pathological suspicious uptake in whole-body PET/CT imaging. We propose a deep learning method that generates patie...

Torso synthetic CT generation by integrating deep learning and segmentation for FDG-PET/MR attenuation correction.

Biomedical physics & engineering express
Positron Emission Tomography/Magnetic Resonance () offers benefits over PET/CT including simultaneous PET and MR acquisition, intrinsic spatial registration accuracy, MR-based functional information, and superior soft tissue contrast. However, accura...

Deep Learning for Automated Measures of SUV and Molecular Tumor Volume in [Ga]PSMA-11 or [F]DCFPyL, [F]FDG, and [Lu]Lu-PSMA-617 Imaging with Global Threshold Regional Consensus Network.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Metastatic castration-resistant prostate cancer has a high rate of mortality with a limited number of effective treatments after hormone therapy. Radiopharmaceutical therapy with [Lu]Lu-prostate-specific membrane antigen-617 (LuPSMA) is one treatment...

Local large arterial perivascular adipose tissue metabolic and anatomical features are associated with hypertensive clinical outcomes: a PET/CT-based study.

Annals of medicine
OBJECTIVE: This study investigated the relationship between anatomical and metabolic characteristics of large arterial perivascular adipose tissue (PVAT) and hypertensive clinical outcomes using positron emission tomography-computed tomography (PET/C...

Characteristics of brain glucose metabolism in Parkinson's disease patients with freezing of gait: a study based on F-FDG PET imaging and deep learning.

BMC neurology
OBJECTIVE: Freezing of gait (FOG) is a common gait disorder in the advanced stages of Parkinson's disease (PD), closely associated with impaired balance and executive function. This study aimed to investigate specific changes in brain glucose metabol...

Comparative Diagnostic Accuracy of AI-Assisted Fluorine-18 Fluorodeoxyglucose Positron Emission Tomography Versus Structural Magnetic Resonance Imaging in Alzheimer Disease: Systematic Review and Meta-Analysis.

JMIR aging
BACKGROUND: Neuroimaging is crucial in the diagnosis of Alzheimer disease (AD). In recent years, artificial intelligence (AI)-based neuroimaging technology has rapidly developed, providing new methods for accurate diagnosis of AD, but its performance...

A proof of concept study of F-FDG PET/CT patient-level radiomics identify refractory/relapsed diffuse large B-cell lymphoma.

Scientific reports
This study aimed to evaluate diffuse large B-cell lymphoma (DLBCL) patients who have refractory/relapsed disease and characterize the heterogeneity of DLBCL using patient-level radiomics analysis based on F-FDG PET/CT. A total of 132 patients diagnos...

Assessing the feasibility of deep learning-based attenuation correction using photon emission data inF-FDG images for dedicated head and neck PET scanners.

Biomedical physics & engineering express
This study aimed to evaluate the use of deep learning techniques to produce measured attenuation-corrected (MAC) images from non-attenuation-corrected (NAC) F-FDG PET images, focusing on head and neck imaging. A Residual Network (ResNet) was used to ...

External validation of deep learning-derived 18F-FDG PET/CT delta biomarkers for loco-regional control in head and neck cancer.

Acta oncologica (Stockholm, Sweden)
BACKGROUND AND PURPOSE: Delta biomarkers that reflect changes in tumour burden over time can support personalised follow-up in head and neck cancer. However, their clinical use can be limited by the need for manual image segmentation. This study exte...

Machine Learning-Driven radiomics on 18 F-FDG PET for glioma diagnosis: a systematic review and meta-analysis.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: Machine learning (ML) applied to radiomics has revolutionized neuro-oncological imaging, yet the diagnostic performance of ML models based specifically on ^18F-FDG PET features in glioma remains poorly characterized.