A5011709611- Radiation Dose and Imaging
- Advanced X-ray and CT Imaging
- Medical Imaging Techniques and Applications
- Radiation Therapy and Dosimetry
- Pelvic and Acetabular Injuries
- Radiology practices and education
- Radiomics and Machine Learning in Medical Imaging
- MRI in cancer diagnosis
- Dental Radiography and Imaging
- Photoacoustic and Ultrasonic Imaging
- Digital Radiography and Breast Imaging
- Chemical Reactions and Isotopes
- Hepatocellular Carcinoma Treatment and Prognosis
- Welding Techniques and Residual Stresses
- Ion-surface interactions and analysis
- Advanced Radiotherapy Techniques
- Cardiac, Anesthesia and Surgical Outcomes
- Vascular Anomalies and Treatments
- Vascular Malformations Diagnosis and Treatment
- Electron and X-Ray Spectroscopy Techniques
- Renal cell carcinoma treatment
- Glioma Diagnosis and Treatment
- Endometrial and Cervical Cancer Treatments
- Ovarian cancer diagnosis and treatment
- Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Université de Nîmes
2021-2025
Université de Montpellier
2021-2025
DMS Imaging (France)
2021-2025
Centre Hospitalier Universitaire de Nîmes
2020-2023
Patients First
2022
Société Française de Cardiologie
2022
Centre Hospitalier Universitaire d'Angers
2015-2022
Neuropsychiatrie : Recherche Epidemiologique et Clinique
2020-2021
Biologie Labor
2021
Université d'Angers
2016
This study is an assessment of the impact acquisition times on SUV with [(18)F]FDG-PET/CT healthy livers (reference organ stable uptake over time) and tumors.One hundred six were acquired in list mode a single-bed position (livers (n = 48) or tumors 58)). Six independent datasets different durations reconstructed (from 1.5 to 10 min). SUVmax (hottest voxel), SUVpeak (maximum average within 1-cm(3) spherical volume), SUVaverage measured 3-cm-diameter volume interest (VOI) right lobe liver....
Recently, computed tomography (CT) manufacturers have developed deep-learning-based reconstruction algorithms to compensate for the limitations of iterative (IR) algorithms, such as image smoothing and spatial resolution's dependence on contrast dose levels.To assess impact an artificial intelligence deep-learning (AI-DLR) algorithm quality reduction compared with a hybrid IR in chest CT different clinical indications.Acquisitions American College Radiology (ACR) 464 Torso CTU-41 phantoms...
To compare the impact on CT image quality and dose reduction of two versions a Deep Learning Image Reconstruction algorithm.Acquisitions ACR 464 phantom were performed at five levels (CTDIvol : 10/7.5/5/2.5/1 mGy) using chest or abdomen pelvis protocol parameters. Raw data reconstructed filtered-back projection (FBP), enhanced level AIDR 3D (AIDR 3De), three AiCE (Mild, Standard, Strong) for (AiCE V8 vs V10). The noise power spectrum (NPS) task-based transfer function (TTF) bone...
To compare the spectral performance of three rapid kV switching dual-energy CT (DECT) systems on virtual monoenergetic images (VMIs) at low-energy levels abdominal imaging. A multi-energy phantom was scanned DECT equipped with different gemstone imaging (GSI) platforms: GSI (1st generation, GSI-1st), GSI-Pro (2nd GSI-2nd ), and GSI-Xtream (3rd GSI-3rd). Acquisitions were performed a CTDIvol close to 11mGy. For all platforms, raw data reconstructed using filtered-back projection (FBP) hybrid...
Abstract Background To assess the potential of virtual monoenergetic images (VMIs) on a photon-counting computed tomography (PCCT) for reducing amount injected iodine contrast media compared to an energy-integrating CT (EICT). Methods A multienergy phantom was scanned with PCCT and EICT at 11 mGy abdomen-pelvis examination parameters. VMIs were generated 40 keV, 50 60 70 keV. For all VMIs, contrast-to-noise ratio (CNR) inserts concentrations 1 mg/mL, 2 5 10 15 mg/mL calculated by dividing...
The purpose of this study was to assess the image quality and dose reduction potential ultra-high resolution (UHR) mode compared with standard mode, both available on a commercial photon-counting detector computed tomography (PCCT) scanner. Images were acquired PCCT phantom using UHR modes at three levels (3/6/12 mGy). Raw data reconstructed soft tissue (Br36) bone (Br68) reconstruction kernels 0.4-mm slice thickness. Noise power spectrum (NPS) task-based transfer function (TTF) calculated...
Abstract Objectives To compare the fetal dose (FD) as calculated by four different software packages for pregnant women who have undergone CT acquisitions directly exposing whole fetus to X-rays. Materials and methods Pregnant underwent abdomen–pelvis and/or thorax–abdomen–pelvis from February 2018 May 2024 whom uterine FD was a medical physicist were retrospectively included. FDs computed per acquisition with VirtualDose-CT™ (VDCT), Duke Organ Dose (DOD), fetaldose.org, COnceptus Estimation...
New reconstruction algorithms based on deep learning have been developed to correct the image texture changes related use of iterative algorithms. The purpose this study was evaluate impact a new [Advanced intelligent Clear-IQ Engine (AiCE)] algorithm image-quality and dose reduction compared hybrid (AIDR 3D) model-based (FIRST) algorithm.Acquisitions were carried out using ACR 464 phantom (and its body ring) at six levels (volume computed tomography index 15/10/7.5/5/2.5/1 mGy). Raw data...
To assess the impact of new version a deep learning (DL) spectral reconstruction on image quality virtual monoenergetic images (VMIs) for contrast-enhanced abdominal computed tomography in rapid kV-switching platform.Two phantoms were scanned with CT using abdomen-pelvic examination parameters at dose 12.6 mGy. Images reconstructed two versions DL algorithms (DLSR V1 and V2) three levels. The noise power spectrum (NSP) task-based transfer function 50% (TTF50) 40/50/60/70 keV. A detectability...
To assess the spectral performance of rapid kV switching dual-energy CT (KVSCT-Canon) equipped with a Deep-Learning reconstruction algorithm on virtual-monoenergetic images at low-energy levels and to compare its performances four other (DECT) platforms iterative algorithms.Two phantoms were scanned five DECT platforms: KVSCT-Canon, fast kV-switching (KVSCT-GE), split filter CT, dual-source (DSCT), dual-layer (DLCT). The classical parameters abdomen-pelvic examinations used for all phantom...
The purpose of this study was to compare the quality low-energy virtual monoenergetic images (VMIs) obtained with three Dual-Energy CT (DECT) platforms according phantom diameter. Three sections Mercury Phantom 4.0 were scanned on two generations split-filter CTs (SFCT-1st and SFCT-2nd) one Dual-source (DSCT). noise power spectrum (NPS), task-based transfer function (TTF), detectability index (d') assessed VMIs from 40 70 keV. highest magnitude values found SFCT-1st higher DSCT than SFCT-2nd...