User profiles for Amirhossein Sanaat
Amirhossein SanaatPET Instrumentation & Neuroimaging Laboratory (PINLab) Geneva University, Department … Verified email at etu.unige.ch Cited by 1115 |
[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 (SPECT…
deep learning (DL) algorithms, in single-photon emission computed tomography (SPECT…
[HTML][HTML] 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 …
[HTML][HTML] Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning
Purpose Attenuation correction and scatter compensation (AC/SC) are two main steps toward
quantitative PET imaging, which remain challenging in PET-only and PET/MRI systems. …
quantitative PET imaging, which remain challenging in PET-only and PET/MRI systems. …
[HTML][HTML] Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network
Objectives The current study aimed to design an ultra-low-dose CT examination protocol
using a deep learning approach suitable for clinical diagnosis of COVID-19 patients. Methods …
using a deep learning approach suitable for clinical diagnosis of COVID-19 patients. Methods …
[HTML][HTML] 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 performed …
data of 14,339 COVID-19 patients. Methods Whole lung segmentations were performed …
Projection space implementation of deep learning–guided low-dose brain PET imaging improves performance over implementation in image space
Our purpose was to assess the performance of full-dose (FD) PET image synthesis in both
image and sinogram space from low-dose (LD) PET images and sinograms without sacrificing …
image and sinogram space from low-dose (LD) PET images and sinograms without sacrificing …
[HTML][HTML] Decentralized distributed multi-institutional PET image segmentation using a federated deep learning framework
Purpose The generalizability and trustworthiness of deep learning (DL)–based algorithms
depend on the size and heterogeneity of training datasets. However, because of patient …
depend on the size and heterogeneity of training datasets. However, because of patient …
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 (…
[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 developed a …
radiation exposure and maximize patients’ comfort. To achieve this goal, we developed a …
Deep‐TOF‐PET: Deep learning‐guided generation of time‐of‐flight from non‐TOF brain PET images in the image and projection domains
… Amirhossein Sanaat and Habib Zaidi contributed to the study conception and design.
Amirhossein Sanaat … Amirhossein Sanaat, Azadeh Akhavanalaf, Isaac Shiri, Yazdan Salimi, …
Amirhossein Sanaat … Amirhossein Sanaat, Azadeh Akhavanalaf, Isaac Shiri, Yazdan Salimi, …