[HTML][HTML] The role of artificial intelligence and machine learning in cardiovascular imaging and diagnosis
Cardiovascular diseases remain the leading cause of global mortality, underscoring the
critical need for accurate and timely diagnosis. This narrative review examines the current …
critical need for accurate and timely diagnosis. This narrative review examines the current …
Deep learning-aided respiratory motion compensation in PET/CT: addressing motion induced resolution loss, attenuation correction artifacts and PET-CT …
Purpose Respiratory motion (RM) significantly impacts image quality in thoracoabdominal
PET/CT imaging. This study introduces a unified data-driven respiratory motion correction …
PET/CT imaging. This study introduces a unified data-driven respiratory motion correction …
ReconU-Net: a direct PET image reconstruction using U-Net architecture with back projection-induced skip connection
Objective. This study aims to introduce a novel back projection-induced U-Net-shaped
architecture, called ReconU-Net, based on the original U-Net architecture for deep learning …
architecture, called ReconU-Net, based on the original U-Net architecture for deep learning …
The impact of long axial field of view (LAFOV) PET on oncologic imaging
The development of long axial field of view (LAFOV) positron emission tomography coupled
with computed tomography (PET/CT) scanners might be considered the biggest step forward …
with computed tomography (PET/CT) scanners might be considered the biggest step forward …
SPECT-MPI iterative denoising during the reconstruction process using a two-phase learned convolutional neural network
The problem of image denoising in single-photon emission computed tomography (SPECT)
myocardial perfusion imaging (MPI) is a fundamental challenge. Although various image …
myocardial perfusion imaging (MPI) is a fundamental challenge. Although various image …
Whole-body PET image denoising for reduced acquisition time
Purpose A reduced acquisition time positively impacts the patient's comfort and the PET
scanner's throughput. AI methods may allow for reducing PET acquisition time without …
scanner's throughput. AI methods may allow for reducing PET acquisition time without …
LpQcM: Adaptable Lesion-Quantification-Consistent Modulation for Deep Learning Low-Count PET Image Denoising
Deep learning-based positron emission tomography (PET) image denoising offers the
potential to reduce radiation exposure and scanning time by transforming low-count images …
potential to reduce radiation exposure and scanning time by transforming low-count images …
PET Image Denoising Based on 3D Denoising Diffusion Probabilistic Model: Evaluations on Total-Body Datasets
Due to various physical degradation factors and limited photon counts detected, obtaining
high-quality images from low-dose Positron emission tomography (PET) scans is …
high-quality images from low-dose Positron emission tomography (PET) scans is …
Modular GAN: positron emission tomography image reconstruction using two generative adversarial networks
Introduction The reconstruction of PET images involves converting sinograms, which
represent the measured counts of radioactive emissions using detector rings encircling the …
represent the measured counts of radioactive emissions using detector rings encircling the …
An intelligent mangosteen grading system based on an improved convolutional neural network
Efficient grading of mangosteens is vital in ensuring timely post-harvest storage and
preservation for maximizing profits. Currently, manual grading is susceptible to subjective …
preservation for maximizing profits. Currently, manual grading is susceptible to subjective …