AIMC Topic: Fluorodeoxyglucose F18

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A cross-scanner and cross-tracer deep learning method for the recovery of standard-dose imaging quality from low-dose PET.

European journal of nuclear medicine and molecular imaging
PURPOSE: A critical bottleneck for the credibility of artificial intelligence (AI) is replicating the results in the diversity of clinical practice. We aimed to develop an AI that can be independently applied to recover high-quality imaging from low-...

Comparison of deep learning-based emission-only attenuation correction methods for positron emission tomography.

European journal of nuclear medicine and molecular imaging
PURPOSE: This study aims to compare two approaches using only emission PET data and a convolution neural network (CNN) to correct the attenuation (μ) of the annihilation photons in PET.

Prediction of post-stroke cognitive impairment using brain FDG PET: deep learning-based approach.

European journal of nuclear medicine and molecular imaging
PURPOSE: Post-stroke cognitive impairment can affect up to one third of stroke survivors. Since cognitive function greatly contributes to patients' quality of life, an objective quantitative biomarker for early prediction of dementia after stroke is ...

An [18F]FDG-PET/CT deep learning method for fully automated detection of pathological mediastinal lymph nodes in lung cancer patients.

European journal of nuclear medicine and molecular imaging
PURPOSE: The identification of pathological mediastinal lymph nodes is an important step in the staging of lung cancer, with the presence of metastases significantly affecting survival rates. Nodes are currently identified by a physician, but this pr...

Application of Pet-CT Fusion Deep Learning Imaging in Precise Radiotherapy of Thyroid Cancer.

Journal of healthcare engineering
This article explores the value of wall F-FDG PET/Cr imaging in the diagnosis of thyroid cancer, studies its ability to distinguish benign and malignant thyroid lesions, and seeks ways to improve the accuracy of diagnosis. The normal control group se...

A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimer's disease, and mild cognitive impairment using brain 18F-FDG PET.

European journal of nuclear medicine and molecular imaging
PURPOSE: The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimer's disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimer's disease (MCI-...

Generation of synthetic PET images of synaptic density and amyloid from F-FDG images using deep learning.

Medical physics
PURPOSE: Positron emission tomography (PET) imaging with various tracers is increasingly used in Alzheimer's disease (AD) studies. However, access to PET scans using new or less-available tracers with sophisticated synthesis and short half-life isoto...

Deep neural network for automatic volumetric segmentation of whole-body CT images for body composition assessment.

Clinical nutrition (Edinburgh, Scotland)
BACKGROUND & AIMS: Body composition analysis on CT images is a valuable tool for sarcopenia assessment. We aimed to develop and validate a deep neural network applicable to whole-body CT images of PET-CT scan for the automatic volumetric segmentation...

The predictive power of artificial intelligence on mediastinal lymphnode metastasis.

General thoracic and cardiovascular surgery
OBJECTIVE: The aim of this study was to create the preoperative predictive model on mediastinal lymph-node metastasis based on artificial intelligence in surgically resected lung adenocarcinoma.