This software is potentially beneficial to a variety of users: physicists working in hospitals, staff working in radiological departments, such as medical physicists, physicians, engineers. The plugin, together with a brief user manual, are freely available and can be found online (www.medphys.it/downloads.htm). With our plugin users can estimate all three most important parameters used for physical characterization (MTF, NPS, and also DQE). The plugin can run on any operating system equipped with ImageJ suite. The authors validated the software by comparing MTF and NPS curves on a common set of images with those obtained with other dedicated programs, achieving a very good agreement.

Des vaccins contre le Covid ont été développés en moins d’un an. Comment a-t-on pu aller aussi vite ? Comment et sur quelle durée sont développés les vaccins en temps normal ? Processus complexe et encadré par de nombreux textes européens et nationaux, mais aussi régi par un ensemble de bonnes pratiques, la mise au point d’un vaccin est une opération longue et coûteuse. Cette approche prudentielle permet de garantir une bonne tolérance aux vaccins et leur efficacité. Mais il est désormais nécessaire de rationaliser les exigences administratives et réglementaires pour pouvoir mettre ces vaccins à la disposition de la population dans de meilleurs délais.

Late enhancement cardiac magnetic resonance images (MRI) has the ability to precisely delineate myocardial scars. We present a semi-automated method for detecting scars in cardiac MRI. This model has the potential to improve routine clinical practice since quanti¯cation is not currently o®ered due to time constraints. A¯rst segmentation step was developed for extracting the target regions for potential scar and determining pre-candidate objects. Pattern recognition methods are then applied to the segmented images in order to detect the position of the myocardial scar. The database of late gadolinium enhancement (LE) cardiac MR images consists of 111 blocks of images acquired from 63 patients at the University Hospital Southampton NHS Foundation Trust (UK). At least one scar was present for each patient, and all the scars were manually annotated by an expert. A group of images (around one third of the entire set) was used for training the system which was subsequently tested on all the remaining images. Four di®erent classi¯ers were trained (Support Vector Machine (SVM), k-nearest neighbor Int. J. Mod. Phys. C 2015.26. Downloaded from www.worldscientific.com by UNIVERSITY OF CALIFORNIA @ DAVIS on 02/07/15. For personal use only.Bayesian and feed-forward neural network) and their performance was evaluated by using Free response Receiver Operating Characteristic (FROC) analysis. Feature selection was implemented for analyzing the importance of the various features. The segmentation method proposed allowed the region a®ected by the scar to be extracted correctly in 96% of the blocks of images. The SVM was shown to be the best classi¯er for our task, and our system reached an overall sensitivity of 80% with less than 7 false positives per patient. The method we present provides an e®ective tool for detection of scars on cardiac MRI. This may be of value in clinical practice by permitting routine reporting of scar quanti¯cation.

No abstract