[HTML][HTML] The promise of artificial intelligence and deep learning in PET and SPECT imaging
This review sets out to discuss the foremost applications of artificial intelligence (AI),
particularly deep learning (DL) algorithms, in single-photon emission computed tomography …
particularly deep learning (DL) algorithms, in single-photon emission computed tomography …
Artificial intelligence-driven assessment of radiological images for COVID-19
Y Bouchareb, PM Khaniabadi, F Al Kindi… - Computers in biology …, 2021 - Elsevier
Artificial Intelligence (AI) methods have significant potential for diagnosis and prognosis of
COVID-19 infections. Rapid identification of COVID-19 and its severity in individual patients …
COVID-19 infections. Rapid identification of COVID-19 and its severity in individual patients …
Deep learning-assisted ultra-fast/low-dose whole-body PET/CT imaging
Purpose Tendency is to moderate the injected activity and/or reduce acquisition time in PET
examinations to minimize potential radiation hazards and increase patient comfort. This …
examinations to minimize potential radiation hazards and increase patient comfort. This …
COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients
Background We aimed to analyze the prognostic power of CT-based radiomics models
using data of 14,339 COVID-19 patients. Methods Whole lung segmentations were …
using data of 14,339 COVID-19 patients. Methods Whole lung segmentations were …
Deep learning-based auto-segmentation of organs at risk in high-dose rate brachytherapy of cervical cancer
Background and purpose Delineation of organs at risk (OARs), such as the bladder, rectum
and sigmoid, plays an important role in the delivery of optimal absorbed dose to the target …
and sigmoid, plays an important role in the delivery of optimal absorbed dose to the target …
COLI‐Net: deep learning‐assisted fully automated COVID‐19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography …
We present a deep learning (DL)‐based automated whole lung and COVID‐19 pneumonia
infectious lesions (COLI‐Net) detection and segmentation from chest computed tomography …
infectious lesions (COLI‐Net) detection and segmentation from chest computed tomography …
Artificial intelligence in nuclear medicine: opportunities, challenges, and responsibilities toward a trustworthy ecosystem
Trustworthiness is a core tenet of medicine. The patient–physician relationship is evolving
from a dyad to a broader ecosystem of health care. With the emergence of artificial …
from a dyad to a broader ecosystem of health care. With the emergence of artificial …
[HTML][HTML] DeepTOFSino: A deep learning model for synthesizing full-dose time-of-flight bin sinograms from their corresponding low-dose sinograms
Purpose Reducing the injected activity and/or the scanning time is a desirable goal to
minimize radiation exposure and maximize patients' comfort. To achieve this goal, we …
minimize radiation exposure and maximize patients' comfort. To achieve this goal, we …
[HTML][HTML] Personalized brachytherapy dose reconstruction using deep learning
Background and purpose Accurate calculation of the absorbed dose delivered to the tumor
and normal tissues improves treatment gain factor, which is the major advantage of …
and normal tissues improves treatment gain factor, which is the major advantage of …
Robust-Deep: a method for increasing brain imaging datasets to improve deep learning models' performance and robustness
A small dataset commonly affects generalization, robustness, and overall performance of
deep neural networks (DNNs) in medical imaging research. Since gathering large clinical …
deep neural networks (DNNs) in medical imaging research. Since gathering large clinical …