In the biopharmaceutical industry, Raman spectroscopy is now a proven PAT tool that enables in-line simultaneous monitoring of several CPPs and CQAs in real-time. However, as Raman monitoring requires multivariate modeling, variabilities unknown by models can impact the monitoring prediction accuracy. With the widespread use of Raman PAT tools, it is necessary to fix instrumental variability impacts, encountered for instance during a device replacement. In this work, we investigated the impact of instrumental variability between probes inside a multi-channel analyzer and between two analyzers, and explored solutions to correct them on model prediction errors in cell cultures. It is shown that the Kennard Stone piecewise direct standardization (KS PDS) method enables to lower model prediction errors between probes of a multi-channel analyzer from 20% to 10% on the cell densities (TCD/VCD). Considering the integration of a new device or the replacement of a previous one, it has been determined that a first cell culture monitoring can be directly performed with the new analyzer calibrated by the KS PDS method based on the dataset from the previous analyzer, with an accuracy better than 10% on the main components of the culture like glucose, lactate, and the cell densities. Then, the new data obtained by the new analyzer can be inserted in a global calibration dataset to integrate instrumental variability in the chemometric model: it is shown that only one batch with the new device in a consistent and equilibrated calibration dataset was sufficient to correct the prediction gap induced by instrumental variability, allowing to exploit the data from previous analyzers considering optimized methods. This methodology provides good multivariate calibration model

International audienceRealistic medical procedure simulators improve the learn-ing curve of the clinicians if they can reproduce real conditions and use. This paper describes the improvement of a transrectal ultrasound guided prostate biopsy simulator by adding the simulation of real-time prostate movements and deformations. A discrete bio-mechanical model is used to modify a 3D texture of an ultrasound image volume in order to quickly simulate the actual displacements and deformations. This pa-per describes this model and presents how the mesh deformation is used to induce the UltraSound volume deformation. The validation of the method is based on both a quantitative and a qualitative assessment. Experimental images acquired on a phantom are compared using mutual information metrics to the resulting generated images. This comparison shows that the proposed method offers realistic deformed 3D ultrasound images at interactive time. The method was successfully integrated to improve the transrectal ultrasound simulator

Computer Assisted Medical Intervention (CAMI hereafter) is a complex multi-disciplinary field. CAMI research requires the collaboration of experts in several fields as diverse as medicine, computer science, mathematics, instrumentation, signal processing, mechanics, modeling, automatics, optics, etc. CamiTK is a modular framework that helps researchers and clinicians to collaborate together in order to prototype CAMI applications by regrouping the knowledge and expertise from each discipline. It is an open-source, cross-platform generic and modular tool written in C++ which can handle medical images, surgical navigation, biomedicals simulations and robot control. This paper presents the Computer Assisted Medical Intervention ToolKit (CamiTK) and how it is used in various applications in our research team.