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Radiology Subcategories: Diagnostic Radiology Nuclear Medicine Therapeutic Radiology

Showing 316-336 of 5,486 articles

Machine learning approach using F-FDG-PET-radiomic features and the visibility of right ventricle F-FDG uptake for predicting clinical events in patients with cardiac sarcoidosis.

OBJECTIVES: To investigate the usefulness of machine learning (ML) models using pretreatment F-FDG-P...

Mar 2024 PMID: 38491333

Artificial intelligence-based, volumetric assessment of the bone marrow metabolic activity in [F]FDG PET/CT predicts survival in multiple myeloma.

PURPOSE: Multiple myeloma (MM) is a highly heterogeneous disease with wide variations in patient out...

Mar 2024 PMID: 38456971

Effects of Intravenous Infusion of Iodine Contrast Media on the Tracheal Diameter and Lung Volume Measured with Deep Learning-Based Algorithm.

This study aimed to investigate the effects of intravenous injection of iodine contrast agent on the...

Mar 2024 PMID: 38448759

Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single-institutional study.

PURPOSE: Predicting recurrence following stereotactic body radiotherapy (SBRT) for non-small cell lu...

Mar 2024 PMID: 38436611

Advanced hybrid attention-based deep learning network with heuristic algorithm for adaptive CT and PET image fusion in lung cancer detection.

Lung cancer is one of the most deadly diseases in the world. Lung cancer detection can save the pati...

Mar 2024 PMID: 38621836

Deep Learning Imaging Reconstruction Algorithm for Carotid Dual Energy CT Angiography: Opportunistic Evaluation of Cervical Intervertebral Discs-A Preliminary Study.

Thus, the aim of this study is to evaluate the performance of deep learning imaging reconstruction (...

Mar 2024 PMID: 38429560

Recovery of the spatially-variant deformations in dual-panel PET reconstructions using deep-learning.

Dual panel PET systems, such as Breast-PET (B-PET) scanner, exhibit strong asymmetric and anisotropi...

Feb 2024 PMID: 38330448

Clinical Feasibility of Deep Learning-Based Attenuation Correction Models for Tl-201 Myocardial Perfusion SPECT.

PURPOSE: We aimed to develop deep learning (DL)-based attenuation correction models for Tl-201 myoca...

Feb 2024 PMID: 38409758

Segmentation of liver and liver lesions using deep learning.

Segmentation of organs and lesions could be employed for the express purpose of dosimetry in nuclear...

Feb 2024 PMID: 38381270

Diagnostic accuracy of artificial intelligence-enabled vectorcardiography versus myocardial perfusion SPECT in patients with suspected or known coronary heart disease.

AIM: The present study evaluated with myocardial perfusion SPECT (MPS) the diagnostic accuracy of an...

Feb 2024 PMID: 38378022

A fully automatic deep learning-based method for segmenting regions of interest and predicting renal function in pediatric dynamic renal scintigraphy.

OBJECTIVE: Accurate delineation of renal regions of interest (ROIs) is critical for the assessment o...

Feb 2024 PMID: 38376629

Greater accuracy of radiomics compared to deep learning to discriminate normal subjects from patients with dementia: a whole brain 18FDG PET analysis.

METHODS: 18F-FDG brain PET and clinical score were collected in 85 patients with dementia and 125 he...

Feb 2024 PMID: 38189449

Learning CT-free attenuation-corrected total-body PET images through deep learning.

OBJECTIVES: Total-body PET/CT scanners with long axial fields of view have enabled unprecedented ima...

Feb 2024 PMID: 38355987

Artificial intelligence in immunotherapy PET/SPECT imaging.

OBJECTIVE: Immunotherapy has dramatically altered the therapeutic landscape for oncology, but more r...

Feb 2024 PMID: 38355986

Low-tube-voltage whole-body CT angiography with extremely low iodine dose: a comparison between hybrid-iterative reconstruction and deep-learning image-reconstruction algorithms.

AIM: To evaluate arterial enhancement, its depiction, and image quality in low-tube potential whole-...

Feb 2024 PMID: 38403540

Radiology

Browse Specialties

Machine learning approach using F-FDG-PET-radiomic features and the visibility of right ventricle F-FDG uptake for predicting clinical events in patients with cardiac sarcoidosis.

Artificial intelligence-based, volumetric assessment of the bone marrow metabolic activity in [F]FDG PET/CT predicts survival in multiple myeloma.

Effects of Intravenous Infusion of Iodine Contrast Media on the Tracheal Diameter and Lung Volume Measured with Deep Learning-Based Algorithm.

Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single-institutional study.

Advanced hybrid attention-based deep learning network with heuristic algorithm for adaptive CT and PET image fusion in lung cancer detection.

Deep Learning Imaging Reconstruction Algorithm for Carotid Dual Energy CT Angiography: Opportunistic Evaluation of Cervical Intervertebral Discs-A Preliminary Study.

Recovery of the spatially-variant deformations in dual-panel PET reconstructions using deep-learning.

Clinical Feasibility of Deep Learning-Based Attenuation Correction Models for Tl-201 Myocardial Perfusion SPECT.

Segmentation of liver and liver lesions using deep learning.

Diagnostic accuracy of artificial intelligence-enabled vectorcardiography versus myocardial perfusion SPECT in patients with suspected or known coronary heart disease.

A fully automatic deep learning-based method for segmenting regions of interest and predicting renal function in pediatric dynamic renal scintigraphy.

Greater accuracy of radiomics compared to deep learning to discriminate normal subjects from patients with dementia: a whole brain 18FDG PET analysis.

Learning CT-free attenuation-corrected total-body PET images through deep learning.

Artificial intelligence in immunotherapy PET/SPECT imaging.

Low-tube-voltage whole-body CT angiography with extremely low iodine dose: a comparison between hybrid-iterative reconstruction and deep-learning image-reconstruction algorithms.

Attenuation correction and truncation completion for breast PET/MR imaging using deep learning.

Verification of image quality improvement of low-count bone scintigraphy using deep learning.

Transforming clinical cardiology through neural networks and deep learning: A guide for clinicians.

Empowering PET: harnessing deep learning for improved clinical insight.

Predicting T-Cell Lymphoma in Children From F-FDG PET-CT Imaging With Multiple Machine Learning Models.

Quasi-supervised learning for super-resolution PET.

Browse Specialties