Monte Carlo validation of a mu-SPECT imaging system on the lightweight grid CiGri

à paraître dans Future Generation Computer SystemsMonte Carlo Simulations (MCS) are nowadays widely used in the field of nuclear medicine for system and algorithms designs. They are valuable for accurately reproducing experimental data, but at the expense of a long computing time. An efficient solution for shorter elapsed time has recently been proposed: grid computing. The aim of this work is to validate a small animal gamma camera MCS and to confirm the usefulness of grid computing for such a study. Good matches between measured and simulated data were achieved and a crunching factor up to 70 was attained on a lightweight campus grid

Validation de la simulation Monte-Carlo de la gamma-caméra petit animal Biospace sur la grille de calcul légère CiGri. Application à l'évaluation de l'algorithme de l'inversion analytique de la transformée de Radon atténuée

DU 1973, EDSF 619Monte Carlo Simulation MCS is nowadays a powerful approach to nuclear medical imaging for detector design and optimization, reconstruction method evaluation and physical effect modelling. Degradations due to attenuation and scattering can be then compensated for in order to improve image reconstruction. The major drawback of MCS is huge computation time. During this PhD work, we have benefited from the GATE MCS (Geant4 Application for Tomographic Emission) for SPECT/PET modelling and from the lightweight grid CiGri (CIMENT GRID) to reduce the elapsed computing time. Our first goal is the modelling of the Biospace small animal gamma camera with GATE. The gamma camera model is validated by a confrontation of the data generated by the model to data obtained experimentally on the true camera. The real data acquired on the camera are accurately predicted by the MCS. The model of the camera is then used to evaluate the performance of the Novikov-Natterer analytic inversion of the attenuated Radon transform. We show that the Novikov-Natterer method improves qualitatively but also quantitatively, the reconstruction compared to the classical FBP analytical algorithm.Les Simulations Monte-Carlo SMC représentent actuellement en imagerie médicale nucléaire un outil puissant d'aide à la conception et à l'optimisation des détecteurs, et à l'évaluation des algorithmes de reconstruction et des méthodes de correction des effets physiques responsables de la dégradation des images reconstruites (atténuation, diffusion, etc.). L'inconvénient majeur des simulations Monte-Carlo réside dans le temps de calcul important qu'elles nécessitent. Au cours de cette thèse, nous avons tiré parti de la plate-forme de SMC GATE (Geant4 Application for Tomographic Emission) dédiée aux examens SPECT/PET pour une modélisation réaliste des phénomènes physiques, et de la grille de calcul légère CiGri (CIMENT Grid) afin de réduire le temps de calcul. Le premier objectif de cette thèse consiste à modéliser la gamma-caméra Biospace dédiée à l'imagerie petit animal à l'aide du logiciel GATE. Le modèle de la gamma-caméra est validé en comparant les résultats issus des simulations GATE avec les données acquises expérimentalement. Les résultats des simulations reproduisent avec précision les performances mesurées de la gamma-caméra. Le modèle validé est ensuite utilisé pour l'évaluation de l'algorithme de Novikov-Natterer de reconstruction analytique de la transformée de Radon atténuée. Les résultats de cette étude montrent que l'algorithme de reconstruction de Novikov-Natterer permet d'améliorer les images d'un point de vue qualitatif et quantitatif par rapport à la méthode analytique standard FB

Fusion SPECT/CT par conditions de cohérence des données et simulations Monte Carlo pour l'imagerie SPECT

Séminair

Fusion SPECT/CT par conditions de cohérence des données et simulations Monte Carlo pour l'imagerie SPECT

Séminair

Fusion SPECT/CT par conditions de cohérence des données et simulations Monte Carlo pour l'imagerie SPECT

Séminair

Validation of the Small Animal Biospace Gamma Imager Model Using GATE Monte Carlo Simulations on the Grid

Monte Carlo simulations are nowadays widely used in the field of nuclear medicine. They are valuable for accurately reproducing experimental data, but at the expense of a long computing time. An efficient solution for shorter elapsed time was recently proposed: grid computing. The aim of this work is to validate a Monte Carlo simulation of the Biospace small animal γ Imager and to confirm the usefulness of grid computing for such a study. Simulated data obtained by the validated model of the gamma camera will enable to investigate new algorithms such as scatter and attenuation correction, and reconstruction methods. Good matches between measured and simulated data were achieved and a crunching factor as high as 70 was achieved on a campus grid

Perceptions of US Medical Students on Artificial Intelligence in Medicine: Mixed Methods Survey Study

BackgroundGiven the rapidity with which artificial intelligence is gaining momentum in clinical medicine, current physician leaders have called for more incorporation of artificial intelligence topics into undergraduate medical education. This is to prepare future physicians to better work together with artificial intelligence technology. However, the first step in curriculum development is to survey the needs of end users. There has not been a study to determine which media and which topics are most preferred by US medical students to learn about the topic of artificial intelligence in medicine. ObjectiveWe aimed to survey US medical students on the need to incorporate artificial intelligence in undergraduate medical education and their preferred means to do so to assist with future education initiatives. MethodsA mixed methods survey comprising both specific questions and a write-in response section was sent through Qualtrics to US medical students in May 2021. Likert scale questions were used to first assess various perceptions of artificial intelligence in medicine. Specific questions were posed regarding learning format and topics in artificial intelligence. ResultsWe surveyed 390 US medical students with an average age of 26 (SD 3) years from 17 different medical programs (the estimated response rate was 3.5%). A majority (355/388, 91.5%) of respondents agreed that training in artificial intelligence concepts during medical school would be useful for their future. While 79.4% (308/388) were excited to use artificial intelligence technologies, 91.2% (353/387) either reported that their medical schools did not offer resources or were unsure if they did so. Short lectures (264/378, 69.8%), formal electives (180/378, 47.6%), and Q and A panels (167/378, 44.2%) were identified as preferred formats, while fundamental concepts of artificial intelligence (247/379, 65.2%), when to use artificial intelligence in medicine (227/379, 59.9%), and pros and cons of using artificial intelligence (224/379, 59.1%) were the most preferred topics for enhancing their training. ConclusionsThe results of this study indicate that current US medical students recognize the importance of artificial intelligence in medicine and acknowledge that current formal education and resources to study artificial intelligence–related topics are limited in most US medical schools. Respondents also indicated that a hybrid formal/flexible format would be most appropriate for incorporating artificial intelligence as a topic in US medical schools. Based on these data, we conclude that there is a definitive knowledge gap in artificial intelligence education within current medical education in the US. Further, the results suggest there is a disparity in opinions on the specific format and topics to be introduced

Monte Carlo validation of a mu-SPECT imaging system on the lightweight grid CiGri

à paraître dans Future Generation Computer SystemsMonte Carlo Simulations (MCS) are nowadays widely used in the field of nuclear medicine for system and algorithms designs. They are valuable for accurately reproducing experimental data, but at the expense of a long computing time. An efficient solution for shorter elapsed time has recently been proposed: grid computing. The aim of this work is to validate a small animal gamma camera MCS and to confirm the usefulness of grid computing for such a study. Good matches between measured and simulated data were achieved and a crunching factor up to 70 was attained on a lightweight campus grid

Indium-catalysed ring opening of 2-hydroxybutyrolactone through the cleavage of C(sp 3 )–O bond

International audienceThe ring-opening by the cleavage of the C(sp 3)-O bond of 2-hydroxybutyrolactone with (ethylthio)trimethylsilane was effectively catalysed by indium triiodide. Screening the reaction conditions allowed optimizing the ring-opening. The effect of the nucleophile/catalyst ratio on the reaction efficiency has also been demonstrated. After optimization, 2-hydroxy-4-(ethylthio)butyric acid was isolated with an excellent yield (85%), thus making this method useful for the preparation of methionine analogues