Nuclear Medicine

Deep learning image reconstruction to improve accuracy of iodine quantification and image quality in dual-energy CT of the abdomen: a phantom and clinical study.

OBJECTIVES: To investigate the effect of deep learning image reconstruction (DLIR) on the accuracy of iodine quantification and image quality of dual-energy CT (DECT) compared to that of other reconstruction algorithms in a phantom experiment and an abdominal clinical study.

Direct mapping from PET coincidence data to proton-dose and positron activity using a deep learning approach.

. Obtaining the intrinsic dose distributions in particle therapy is a challenging problem that needs to be addressed by imaging algorithms to take advantage of secondary particle detectors. In this work, we investigate the utility of deep learning methods for achieving direct mapping from detector data to the intrinsic dose distribution.. We performed Monte Carlo simulations using GATE/Geant4 10.4...

Deep Learning of Coronary Calcium Scores From PET/CT Attenuation Maps Accurately Predicts Adverse Cardiovascular Events.

BACKGROUND: Assessment of coronary artery calcium (CAC) by computed tomographic (CT) imaging provides an accurate measure of atherosclerotic burden. C...

Polar map-free 3D deep learning algorithm to predict obstructive coronary artery disease with myocardial perfusion CZT-SPECT.

PURPOSE: Deep learning (DL) models have been shown to outperform total perfusion deficit (TPD) quantification in predicting obstructive coronary arter...

Analysis of Smart Lung Tumour Detector and Stage Classifier Using Deep Learning Techniques with Internet of Things.

The use of artificial intelligence (AI) and the Internet of Things (IoT), which is a developing technology in medical applications that assists physic...

Deep-learning-based estimation of attenuation map improves attenuation correction performance over direct attenuation estimation for myocardial perfusion SPECT.

BACKGROUND: Deep learning (DL)-based attenuation correction (AC) is promising to improve myocardial perfusion (MP) SPECT. We aimed to optimize and com...

Deep-TOF-PET: Deep learning-guided generation of time-of-flight from non-TOF brain PET images in the image and projection domains.

We aim to synthesize brain time-of-flight (TOF) PET images/sinograms from their corresponding non-TOF information in the image space (IS) and sinogram...

DeepMTS: Deep Multi-Task Learning for Survival Prediction in Patients With Advanced Nasopharyngeal Carcinoma Using Pretreatment PET/CT.

Nasopharyngeal Carcinoma (NPC) is a malignant epithelial cancer arising from the nasopharynx. Survival prediction is a major concern for NPC patients,...

Automated extraction of information of lung cancer staging from unstructured reports of PET-CT interpretation: natural language processing with deep-learning.

BACKGROUND: Extracting metastatic information from previous radiologic-text reports is important, however, laborious annotations have limited the usab...

Mapping the association between tau-PET and Aβ-amyloid-PET using deep learning.

In Alzheimer's disease, the molecular pathogenesis of the extracellular Aβ-amyloid (Aβ) instigation of intracellular tau accumulation is poorly unders...

Multimodal deep learning model on interim [F]FDG PET/CT for predicting primary treatment failure in diffuse large B-cell lymphoma.

OBJECTIVES: The prediction of primary treatment failure (PTF) is necessary for patients with diffuse large B-cell lymphoma (DLBCL) since it serves as ...

Deep learning-based denoising in projection-domain and reconstruction-domain for low-dose myocardial perfusion SPECT.

BACKGROUND: Low-dose (LD) myocardial perfusion (MP) SPECT suffers from high noise level, leading to compromised diagnostic accuracy. Here we investiga...

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 th...

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 de...

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...