Latest AI and machine learning research in nuclear medicine for healthcare professionals.
BACKGROUND: Assessment of coronary artery calcium (CAC) by computed tomographic (CT) imaging provide...
PURPOSE: Deep learning (DL) models have been shown to outperform total perfusion deficit (TPD) quant...
The use of artificial intelligence (AI) and the Internet of Things (IoT), which is a developing tech...
BACKGROUND: Deep learning (DL)-based attenuation correction (AC) is promising to improve myocardial ...
We aim to synthesize brain time-of-flight (TOF) PET images/sinograms from their corresponding non-TO...
Nasopharyngeal Carcinoma (NPC) is a malignant epithelial cancer arising from the nasopharynx. Surviv...
BACKGROUND: Extracting metastatic information from previous radiologic-text reports is important, ho...
In Alzheimer's disease, the molecular pathogenesis of the extracellular Aβ-amyloid (Aβ) instigation ...
OBJECTIVES: The prediction of primary treatment failure (PTF) is necessary for patients with diffuse...
BACKGROUND: Low-dose (LD) myocardial perfusion (MP) SPECT suffers from high noise level, leading to ...
PURPOSE: Recently, deep learning-based methods have been established to denoise the low-count positr...
PURPOSE: To investigate whether the iodine density of liver parenchyma in the equilibrium phase and ...
PURPOSE: To develop an automated lung tumor segmentation method for radiation therapy planning based...
BACKGROUND: Studies have shown that the conventional parameters characterizing left ventricular mech...
Potential radioactive hazards in full-dose positron emission tomography (PET) imaging remain a conce...
Perception can influence individuals' behaviour and attitude affecting responses and compliance to p...
PURPOSE: Attenuation correction is a critically important step in data correction in positron emissi...
Though artificial intelligence (AI) has been used in nuclear medicine for more than 50 years, more p...
The segmentation of magnetic resonance (MR) images is a crucial task for creating pseudo computed to...
. Deep learning denoising networks are typically trained with images that are representative of the ...
In this work, a dedicated end-to-end deep convolutional neural network, named as Triple-CBCT, is pro...