Nuclear Medicine

Virtual high-count PET image generation using a deep learning method.

PURPOSE: Recently, deep learning-based methods have been established to denoise the low-count positron emission tomography (PET) images and predict their standard-count image counterparts, which could achieve reduction of injected dosage and scan time, and improve image quality for equivalent lesion detectability and clinical diagnosis. In clinical settings, the majority scans are still acquired u...

A novel fast kilovoltage switching dual-energy computed tomography technique with deep learning: Utility for non-invasive assessments of liver fibrosis.

PURPOSE: To investigate whether the iodine density of liver parenchyma in the equilibrium phase and extracellular volume fraction (ECV) measured by deep learning-based spectral computed tomography (CT) can enable noninvasive liver fibrosis staging.

Automated Lung Cancer Segmentation Using a PET and CT Dual-Modality Deep Learning Neural Network.

PURPOSE: To develop an automated lung tumor segmentation method for radiation therapy planning based on deep learning and dual-modality positron emiss...

A method using deep learning to discover new predictors from left-ventricular mechanical dyssynchrony for CRT response.

BACKGROUND: Studies have shown that the conventional parameters characterizing left ventricular mechanical dyssynchrony (LVMD) measured on gated SPECT...

3D Segmentation Guided Style-Based Generative Adversarial Networks for PET Synthesis.

Potential radioactive hazards in full-dose positron emission tomography (PET) imaging remain a concern, whereas the quality of low-dose images is neve...

Machine learning and features for the prediction of thermal sensation and comfort using data from field surveys in Cyprus.

Perception can influence individuals' behaviour and attitude affecting responses and compliance to precautionary measures. This study aims to investig...

Quantitative evaluation of a deep learning-based framework to generate whole-body attenuation maps using LSO background radiation in long axial FOV PET scanners.

PURPOSE: Attenuation correction is a critically important step in data correction in positron emission tomography (PET) image formation. The current s...

3D Convolutional Neural Network Framework with Deep Learning for Nuclear Medicine.

Though artificial intelligence (AI) has been used in nuclear medicine for more than 50 years, more progress has been made in deep learning (DL) and ma...

Deep learning with multiresolution handcrafted features for brain MRI segmentation.

The segmentation of magnetic resonance (MR) images is a crucial task for creating pseudo computed tomography (CT) images which are used to achieve pos...

A personalized deep learning denoising strategy for low-count PET images.

. Deep learning denoising networks are typically trained with images that are representative of the testing data. Due to the large variability of the ...

Feasibility study of three-material decomposition in dual-energy cone-beam CT imaging with deep learning.

In this work, a dedicated end-to-end deep convolutional neural network, named as Triple-CBCT, is proposed to demonstrate the feasibility of reconstruc...

Comparison of the performances of machine learning and deep learning in improving the quality of low dose lung cancer PET images.

PURPOSE: To compare the performances of machine learning (ML) and deep learning (DL) in improving the quality of low dose (LD) lung cancer PET images ...

Improving Breast Tumor Segmentation in PET via Attentive Transformation Based Normalization.

Positron Emission Tomography (PET) has become a preferred imaging modality for cancer diagnosis, radiotherapy planning, and treatment responses monito...

Deep learning exploration for SPECT MPI polar map images classification in coronary artery disease.

OBJECTIVE: The exploration and the implementation of a deep learning method using a state-of-the-art convolutional neural network for the classificati...

Deep learning based low-activity PET reconstruction of [C]PiB and [F]FE-PE2I in neurodegenerative disorders.

PURPOSE: Positron Emission Tomography (PET) can support a diagnosis of neurodegenerative disorder by identifying disease-specific pathologies. Our aim...

Eliminating CT radiation for clinical PET examination using deep learning.

Clinical PET/CT examinations rely on CT modality for anatomical localization and attenuation correction of the PET data. However, the use of CT signif...

Colposcopic multimodal fusion for the classification of cervical lesions.

: Cervical cancer is one of the two biggest killers of women and early detection of cervical precancerous lesions can effectively improve the survival...

A neural network-based algorithm for simultaneous event positioning and timestamping in monolithic scintillators.

. Monolithic scintillator crystals coupled to silicon photomultiplier (SiPM) arrays are promising detectors for PET applications, offering spatial res...

Adapting a low-count acquisition of the bone scintigraphy using deep denoising super-resolution convolutional neural network.

PURPOSE: Deep-layer learning processing may improve contrast imaging with greater precision in low-count acquisition. However, no data on noise reduct...

Deep-learning-based methods of attenuation correction for SPECT and PET.

Attenuation correction (AC) is essential for quantitative analysis and clinical diagnosis of single-photon emission computed tomography (SPECT) and po...