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An image-quality CT phantom was scanned with three different 3D X-ray imaging guidance devices in the operating theatre: O-Arm, Loop-X, and Airo TruCT. Default acquisition and reconstruction parameters for lumbar spine procedures were used on each device. The tube current was set to a dose level of around 27 mGy. A task-based image quality assessment was performed by calculating the noise power spectrum (NPS) and task transfer function (TTF). A detectability index (d') was calculated for three simulated bone lesions. The noise magnitude of the O-Arm was higher than the Airo TruCT, and the Loop-X had higher noise than the Airo TruCT. The highest average NPS frequency was for the O-Arm images, and the lowest was for the Loop-X. The TTFs at 50% values were similar for the Airo TruCT and Loop-X devices. Compared to Airo TruCT, the TTF at 50% value increased with the O-Arm by 53.12% and 41.20% for the Teflon and Delrin inserts, respectively. Compared to Airo TruCT, the d' value was lower with Loop-X by −26.73%, −27.02%, and −23.95% for lytic lesions, sclerotic lesions, and high-density bone, respectively. Each 3D-imaging spine surgery guidance device has its own strengths and weaknesses in terms of image quality. Cone-beam CT systems apparently offer the best compromise between noise and spatial resolution for spine surgery. [ABSTRACT FROM AUTHOR]

The French regulations concerning the involvement of medical physicists in medical imaging procedures are relatively vague. In May 2013, the ASN and the SFPM issued recommendations regarding Medical Physics Personnel for Medical Imaging: Requirements, Conditions of Involvement and Staffing Levels. In these recommendations, the various areas of activity of medical physicists in radiology and nuclear medicine have been identified and described, and the time required to perform each task has been evaluated. Criteria for defining medical physics staffing levels are thus proposed. These criteria are defined according to the technical platform, the procedures and techniques practised on it, the number of patients treated and the number of persons in the medical and paramedical teams requiring periodic training. The result of this work is an aid available to each medical establishment to determine their own needs in terms of medical physics. [ABSTRACT FROM AUTHOR]

The French regulations concerning the involvement of medical physicists in medical imaging procedures are relatively vague. In May 2013, the ASN and the SFPM issued recommendations regarding Medical Physics Personnel for Medical Imaging: Requirements, Conditions of Involvement and Staffing Levels. In these recommendations, the various areas of activity of medical physicists in radiology and nuclear medicine have been identified and described, and the time required to perform each task has been evaluated. Criteria for defining medical physics staffing levels are thus proposed. These criteria are defined according to the technical platform, the procedures and techniques practised on it, the number of patients treated and the number of persons in the medical and paramedical teams requiring periodic training. The result of this work is an aid available to each medical establishment to determine their own needs in terms of medical physics. [ABSTRACT FROM AUTHOR]

Abstract The [14 C-N-methyl]-erythromycin breath test (14 C-ERMBT) is one of the most valuable probes for liver cytochrome P450-3A4 activity in humans. In order to extend the use of this test in France, we herein provide safety data regarding either patient dosimetry or worker exposure to [14 C-N-methyl]-erythromycin. In order to determine the maximum radiation exposure for patient and nuclear medicine technician following one intravenous 14 C-ERMBT [111 kiloBequerel (kBq)], we have used the dosimetric data gathered in animal studies and extrapolated to humans using a weight-based method, approximate data provided by the French Society of Radioprotection and erythromycin pharmacokinetics in humans, considering always the worst conditions for the patient and worker exposure determination. The radioactivity administered to a patient after one 14 C-ERMBT was equal to 108.8 kBq (i.e. 98% of the total radioactivity in the 14 C-erythromycin vial) leading to a patient effective dose of 20 microsievert (μSv) and a maximum effective dose after 14 CO2 inhalation by the exposed worker of 16 μSv compared with a mean individual annual effective dose from natural and artificial radioactivity exposure of 3500 μSv in France. The 14 C-ERMBT is safe and complies with the European regulations regarding the administration of 14 C-labelled compounds in humans. It can therefore be used in clinical research in France without any particular safety requirement. [ABSTRACT FROM AUTHOR]

L'intelligence artificielle (IA) suscite un intérêt croissant auprès des chercheurs, cliniciens et radiologues. Le deep learning (apprentissage profond) est une forme d'IA dérivée du machine learning (apprentissage automatique) qui permet de générer des algorithmes capables d'identifier de façon autonome les caractéristiques d'imagerie les plus optimales pour résoudre une problématique donnée. Les réseaux de neurones convolutifs (CNN) constituent l'architecture la plus courante pour le traitement d'images médicales par deep learning. Les applications du deep learning en imagerie ostéoarticulaire sont la détection d'anomalies à la radiographie ou à l'imagerie en coupe transversale (scanner, IRM), comme les fractures, les déchirures méniscales, les ruptures du ligament croisé antérieur, les lésions dégénératives du rachis ou les métastases osseuses, la classification des images comme la sténose lombaire, la dégénérescence discale ou l'évaluation de l'âge osseux mais aussi la segmentation comme par exemple celle du cartilage. Le développement de logiciels a déjà modifié la pratique courante de l'imagerie orthopédique grâce à la détection automatique des fractures sur les radiographies. L'amélioration des protocoles d'acquisition et de la qualité des images scanner à faible dose, la réduction du temps d'acquisition en IRM ou l'amélioration de la résolution des images obtenues par résonance magnétique sont désormais possibles par la technique du deep learning. Le deep learning est une approche automatisée qui permet de se décharger des processus manuels fastidieux et d'améliorer les performances des praticiens. Cet article propose un état des lieux de l'IA appliquée à l'imagerie ostéo-articulaire. The interest of researchers, clinicians and radiologists, in artificial intelligence (AI) continues to grow. Deep learning is a subset of machine learning, in which the computer algorithm itself can determine the optimal imaging features to answer a clinical question. Convolutional neural networks are the most common architecture for performing deep learning on medical images. The various musculoskeletal applications of deep learning are the detection of abnormalities on X-rays or cross-sectional images (CT, MRI), for example the detection of fractures, meniscal tears, anterior cruciate ligament tears, degenerative lesions of the spine, bone metastases, classification of e.g. dural sac stenosis, degeneration of intervertebral discs, assessment of skeletal age, and segmentation, for example of cartilage. Software developments are already impacting the daily practice of orthopedic imaging by automatically detecting fractures on radiographs. Improving image acquisition protocols, improving the quality of low-dose CT images, reducing acquisition times in MRI, or improving MR image resolution is possible through deep learning. Deep learning offers an automated way to offload time-consuming manual processes and improve practitioner performance. This article reviews the current state of AI in musculoskeletal imaging. [ABSTRACT FROM AUTHOR]

The [14C-N-methyl]-erythromycin breath test (14C-ERMBT) is one of the most valuable probes for liver cytochrome P450-3A4 activity in humans. In order to extend the use of this test in France, we herein provide safety data regarding either patient dosimetry or worker exposure to [14C-N-methyl]-erythromycin. In order to determine the maximum radiation exposure for patient and nuclear medicine technician following one intravenous 14C-ERMBT [111 kiloBequerel (kBq)], we have used the dosimetric data gathered in animal studies and extrapolated to humans using a weight-based method, approximate data provided by the French Society of Radioprotection and erythromycin pharmacokinetics in humans, considering always the worst conditions for the patient and worker exposure determination. The radioactivity administered to a patient after one 14C-ERMBT was equal to 108.8 kBq (i.e. 98% of the total radioactivity in the 14C-erythromycin vial) leading to a patient effective dose of 20 microsievert (microSv) and a maximum effective dose after 14CO2 inhalation by the exposed worker of 16 microSv compared with a mean individual annual effective dose from natural and artificial radioactivity exposure of 3500 microSv in France. The 14C-ERMBT is safe and complies with the European regulations regarding the administration of 14C-labelled compounds in humans. It can therefore be used in clinical research in France without any particular safety requirement.