20 citations · 44 across the 9 of their papers we have counts for
10 papers
Position-Sensitive Silicon Photomultiplier Array with Enhanced Position Reconstruction by means of a Deep Neural Network
Cyril Alispach, Fabio Acerbi, Hossein Arabi +4
Single-photon sensitive detectors like Silicon Photomultipliers are widely used in many medical imaging applications. By using detectors with position resolutions, it is possible t…
Performance Evaluation of a Position-Sensitive SiPM-based Gamma Camera for Intraoperative Imaging
Aramis Raiola, Fabio Acerbi, Cyril Alispach +4
The POSiCS camera is a handheld, small field-of-view gamma camera developed for multipurpose use in radio-guided surgery (RGS), with sentinel lymph node biopsy (SLNB) as its benchm…
Quantitative Determination of Spatial Resolution and Linearity of Position-Sensitive LG-SiPMs at Sub-Millimeter Scale via Ricean Distribution Fitting
Aramis Raiola, Fabio Acerbi, Cyril Alispach +4
Position-sensitive SiPMs are useful in all light detection applications requiring a small number of readout channels while preserving the information about the incoming light's int…
A novel shape-based loss function for machine learning-based seminal organ segmentation in medical imaging
Reza Karimzadeh, Emad Fatemizadeh, Hossein Arabi
Automated medical image segmentation is an essential task to aid/speed up diagnosis and treatment procedures in clinical practices. Deep convolutional neural networks have exhibite…
A novel unsupervised covid lung lesion segmentation based on the lung tissue identification
Faeze Gholamian Khah, Samaneh Mostafapour, Seyedjafar Shojaerazavi +2
This study aimed to evaluate the performance of a novel unsupervised deep learning-based framework for automated infections lesion segmentation from CT images of Covid patients. In…
Quantitative and Qualitative Performance Evaluation of Commercial Metal Artifact Reduction Methods: Dosimetric Effects on the Treatment Planning
Mohammad Ghorbanzadeh, Seyed Abolfazl Hosseini, Bijan Vosoughi Vahdat +3
The presence of metal implants within CT imaging causes severe attenuation of the X-ray beam. Due to the incomplete information recorded by CT detectors, artifacts in the form of s…
Comparison of different deep learning architectures for synthetic CT generation from MR images
Abbas Bahrami, Alireza Karimian, Hossein Arabi
MRI-guided radiation treatment planning is widely applied because of its superior soft-tissue contrast and no ionization radiation compared to CT-based planning. In this regard, sy…
Automated lung segmentation from CT images of normal and COVID-19 pneumonia patients
Faeze Gholamiankhah, Samaneh Mostafapour, Nouraddin Abdi Goushbolagh +4
Automated semantic image segmentation is an essential step in quantitative image analysis and disease diagnosis. This study investigates the performance of a deep learning-based mo…
Deep learning-based noise reduction in low dose SPECT Myocardial Perfusion Imaging: Quantitative assessment and clinical performance
Narges Aghakhan Olia, Alireza Kamali-Asl1, Sanaz Hariri Tabrizi +4
Clinical SPECT-MPI images of 345 patients acquired from a dedicated cardiac SPECT in list-mode format were retrospectively employed to predict normal-dose images from low-dose data…
Quantitative investigation of low-dose PET imaging and post-reconstruction smoothing
Behnoush Sanaei, Reza Faghihi, Hossein Arabi
In this study, 18F-FDG PET/CT brain scans of 50 patients with head and neck malignant lesions were employed to systematically assess the relationship between the amount of injected…