User profiles for Amirhossein Sanaat

Amirhossein Sanaat

PET 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

H Arabi, A AkhavanAllaf, A Sanaat, I Shiri, H Zaidi - Physica Medica, 2021 - Elsevier
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…

[HTML][HTML] Deep learning-assisted ultra-fast/low-dose whole-body PET/CT imaging

A Sanaat, I Shiri, H Arabi, I Mainta, R Nkoulou… - European journal of …, 2021 - Springer
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 …

[HTML][HTML] Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning

…, A Vafaei Sadr, A Akhavan, Y Salimi, A Sanaat… - European Journal of …, 2023 - Springer
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. …

[HTML][HTML] Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network

I Shiri, A Akhavanallaf, A Sanaat, Y Salimi, D Askari… - European …, 2021 - Springer
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 …

[HTML][HTML] COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

…, M Pakbin, G Hajianfar, AH Avval, A Sanaat… - Computers in biology …, 2022 - Elsevier
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 …

Projection space implementation of deep learning–guided low-dose brain PET imaging improves performance over implementation in image space

A Sanaat, H Arabi, I Mainta, V Garibotto… - Journal of Nuclear …, 2020 - Soc Nuclear Med
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 …

[HTML][HTML] Decentralized distributed multi-institutional PET image segmentation using a federated deep learning framework

…, AV Sadr, M Amini, Y Salimi, A Sanaat… - Clinical Nuclear …, 2022 - journals.lww.com
Purpose The generalizability and trustworthiness of deep learning (DL)–based algorithms
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 …

I Shiri, H Arabi, Y Salimi, A Sanaat… - … journal of imaging …, 2022 - Wiley Online Library
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 (…

[HTML][HTML] DeepTOFSino: A deep learning model for synthesizing full-dose time-of-flight bin sinograms from their corresponding low-dose sinograms

A Sanaat, H Shooli, S Ferdowsi, I Shiri, H Arabi, H Zaidi - Neuroimage, 2021 - Elsevier
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 …

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

A Sanaat, A Akhavanalaf, I Shiri, Y Salimi… - Human brain …, 2022 - Wiley Online Library
Amirhossein Sanaat and Habib Zaidi contributed to the study conception and design.
Amirhossein SanaatAmirhossein Sanaat, Azadeh Akhavanalaf, Isaac Shiri, Yazdan Salimi, …