239 research outputs found

    Analyse des capacitĆ©s dā€™agir dā€™un syndicat local en matiĆØre de prise en charge des risques psychosociaux au travail : une Ć©tude de cas dans le secteur de la mĆ©tallurgie au QuĆ©bec

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    En cette eĢ€re dā€™hypertravail (Charest et RheĢaume, 2008) menant aĢ€ une plus grande charge de travail, les travailleurs souffrent. MalgreĢ cela, peu dā€™eĢtudes ont abordeĢ la question de la prise en charge de la santeĢ mentale au travail dans une perspective syndicale. Nous avons ainsi approfondi nos connaissances sur les initiatives syndicales de prise en charge des risques psychosociaux (RPS) au QueĢbec. Ce meĢmoire cherche donc aĢ€ reĢpondre aĢ€ la question de recherche : comment se facĢ§onnent les capaciteĢs dā€™agir dā€™un syndicat local en matieĢ€re de prise en charge des risques psychosociaux au travail? Pour y reĢpondre, nous avons utiliseĢ plusieurs eĢcrits portant sur les ressources de pouvoir aĢ€ la disposition des syndicats, les cadres identitaires et les ressources normatives pouvant venir influencer les capaciteĢs dā€™agir des syndicats en matieĢ€re de prise en charge des risques psychosociaux. En plus de ces eĢcrits, nous avons aussi porteĢ une attention sur lā€™influence que peut exercer la collaboration patronale-syndicale sur le deĢveloppement des capaciteĢs dā€™agir des syndicats. Nos donneĢes de recherche ont eĢteĢ amasseĢes graĢ‚ce aĢ€ une eĢtude de cas, ouĢ€ nous avons fait 8 entretiens semi-dirigeĢs aupreĢ€s dā€™un syndicat local affilieĢ aĢ€ la FTQ dans le secteur de la meĢtallurgie au QueĢbec. Nos reĢsultats deĢmontrent que certaines ressources et certains eĢleĢments des cadres identitaires exercent une influence positive sur les capaciteĢs dā€™agir du syndicat en matieĢ€re de prise en charge des RPS : le reĢseautage externe et les inteĢreĢ‚ts partageĢs par le groupe de reĢfeĢrence et le groupe dā€™opposition. Toutefois, les ressources et les eĢleĢments des cadres identitaires exercent une influence neĢgative sur les capaciteĢs dā€™agir du syndicat en matieĢ€re de prise en charge des RPS : nous pensons au projet syndical et aux ressources organisationnelles.In this era of hyper-work (Charest and RheĢaume, 2008) leading to a greater workload and a strong subjective investment at work, workers are suffering. Despite this, few studies have addressed the issue of managing mental health in the workplace from a trade union perspective. We have thus deepened our knowledge of union initiatives for the management of psychosocial risks (PSR) in Quebec. This thesis therefore seeks to answer the research question: how are the capacities of a local union to act in terms of taking charge of psychosocial risks at work? To answer this, we used several writings on the power resources available to unions, identity frameworks and normative resources that can influence the development of unions' capacities to act in terms of taking charge of psychosocial risks. In addition to these writings, we also paid attention to the influence that labour-management collaboration can exert on the development of union capacities to act. Our research data was gathered through a case study, where we conducted 8 semi-structured interviews with a local union affiliated with the FTQ in the metallurgy sector in Quebec. Our results show that certain resources and certain elements of identity frameworks have a positive influence on the union's capacities to act in terms of taking charge of PSR: external networking and the interests shared by the reference group and the group of opposition. However, certain resources and elements of identity frameworks have a negative influence on the unionā€™s capacity to act in terms of taking charge of RPS: we are thinking of the union project and organizational resources

    Process economical effects of implementation of ready-to-use micro carriers in cell- based virus vaccine production

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    Micro-carriers are used as support for the growth of adherent cells. By providing a large cultivation surface in bioreactor cultures, micro-carriers have replaced, to a great extent, cultivation in Cell Factoryā„¢ systems or roller bottles over the last decades. At Sanofi Pasteur, one of the world leaders in human vaccines, Cytodexā„¢ 1 microcarriers have been used in the production of viral vaccines on Vero cells for several years. In accordance with the supplierā€™s recommendation, the microcarriers that are delivered dry are swollen in buffer, washed, and heat-sterilized before use. Since October 2016 a ready-to-use Cytodexā„¢ 1 alternative, delivered presterilized by gamma irradiation, is available. Before implementing the change, the presterilized alternative was first evaluated with regards to reduced preparation time and cost. With a two-year shelf-life, the presterilized alternative reduced utility cost and added flexibility to operations by decreasing the need for steam and stainless steel materials in viral production facilities, and in alignment with extended use of single-use bioreactors equipment. The second step was to compare the cell growth and viral productivity using this ready-to-use alternative with that of the prior referenced product in place. Both cell growth and viral productivity were comparable between the two products, which supported further the documentation for the implementation of this ready-to-use alternative in GMP manufacturing for new R&D vaccine projects. The qualification process covered technical, quality, and analytical aspects based on the supplier documentation, and internal analyses and justification regarding our requirements in upstream vaccine production. While the presterilized Cytodexā„¢ 1 microcarriers are now implemented in process development for new vaccines and qualified for manufacturing of clinical batches of new vaccine products, the next step will be to evaluate the benefits and impacts of replacing the microcarrier reference product with the gamma sterilized alternative on industrial products

    Optimizing dual energy cone beam CT protocols for preclinical imaging and radiation research

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    Objective: The aim of this work was to investigate whether quantitative dual-energy CT (DECT) imaging is feasible for small animal irradiators with an integrated cone-beam CT (CBCT) system. Methods: The optimal imaging protocols were determined by analyzing different energy combinations and dose levels. The influence of beam hardening effects and the performance of a beam hardening correction (BHC) were investigated. In addition, two systems from different manufacturers were compared in terms of errors in the extracted effective atomic numbers (Z(eff)) and relative electron densities (rho(e)) for phantom inserts with known elemental compositions and relative electron densities. Results: The optimal energy combination was determined to be 50 and 90kVp. For this combination, Z(eff) and r rho(e) can be extracted with a mean error of 0.11 and 0.010, respectively, at a dose level of 60cGy. Conclusion: Quantitative DECT imaging is feasible for small animal irradiators with an integrated CBCT system. To obtain the best results, optimizing the imaging protocols is required. Well-separated X-ray spectra and a sufficient dose level should be used to minimize the error and noise for Z(eff) and rho(e). When no BHC is applied in the image reconstruction, the size of the calibration phantom should match the size of the imaged object to limit the influence of beam hardening effects. No significant differences in Z(eff) and rho(e) errors are observed between the two systems from different manufacturers. Advances in knowledge: This is the first study that investigates quantitative DECT imaging for small animal irradiators with an integrated CBCT system

    Feasibility of CycleGAN enhanced low dose CBCT imaging for prostate radiotherapy dose calculation

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    Daily cone beam computed tomography (CBCT) imaging during the course of fractionated radiotherapy treatment can enable online adaptive radiotherapy but also expose patients to a non-negligible amount of radiation dose. This work investigates the feasibility of low dose CBCT imaging capable of enabling accurate prostate radiotherapy dose calculation with only 25% projections by overcoming under-sampling artifacts and correcting CT numbers by employing cycle-consistent generative adversarial networks (cycleGAN). Uncorrected CBCTs of 41 prostate cancer patients, acquired with āˆ¼350 projections (CBCTorg), were retrospectively under-sampled to 25% dose images (CBCTLD) with only āˆ¼90 projections and reconstructed using Feldkampā€“Davisā€“Kress. We adapted a cycleGAN including shape loss to translate CBCTLD into planning CT (pCT) equivalent images (CBCTLD_GAN). An alternative cycleGAN with a generator residual connection was implemented to improve anatomical fidelity (CBCTLD_ResGAN). Unpaired 4-fold cross-validation (33 patients) was performed to allow using the median of 4 models as output. Deformable image registration was used to generate virtual CTs (vCT) for Hounsfield units (HU) accuracy evaluation on 8 additional test patients. Volumetric modulated arc therapy plans were optimized on vCT, and recalculated on CBCTLD_GAN and CBCTLD_ResGAN to determine dose calculation accuracy. CBCTLD_GAN, CBCTLD_ResGAN and CBCTorg were registered to pCT and residual shifts were analyzed. Bladder and rectum were manually contoured on CBCTLD_GAN, CBCTLD_ResGAN and CBCTorg and compared in terms of Dice similarity coefficient (DSC), average and 95th percentile Hausdorff distance (HDavg, HD95). The mean absolute error decreased from 126 HU for CBCTLD to 55 HU for CBCTLD_GAN and 44 HU for CBCTLD_ResGAN. For PTV, the median differences of D98%, D50% and D2% comparing both CBCTLD_GAN to vCT were 0.3%, 0.3%, 0.3%, and comparing CBCTLD_ResGAN to vCT were 0.4%, 0.3% and 0.4%. Dose accuracy was high with both 2% dose difference pass rates of 99% (10% dose threshold). Compared to the CBCTorg-to-pCT registration, the majority of mean absolute differences of rigid transformation parameters were less than 0.20 mm/0.20Ā°. For bladder and rectum, the DSC were 0.88 and 0.77 for CBCTLD_GAN and 0.92 and 0.87 for CBCTLD_ResGAN compared to CBCTorg, and HDavg were 1.34 mm and 1.93 mm for CBCTLD_GAN, and 0.90 mm and 1.05 mm for CBCTLD_ResGAN. The computational time was āˆ¼2 s per patient. This study investigated the feasibility of adapting two cycleGAN models to simultaneously remove under-sampling artifacts and correct image intensities of 25% dose CBCT images. High accuracy on dose calculation, HU and patient alignment were achieved. CBCTLD_ResGAN achieved better anatomical fidelity

    Intraā€frame motion deterioration effects and deepā€learningā€based compensation in MRā€guided radiotherapy

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    Background Current commercially available hybrid magnetic resonance linear accelerators (MR-Linac) use 2D+t cine MR imaging to provide intra-fractional motion monitoring. However, given the limited temporal resolution of cine MR imaging, target intra-frame motion deterioration effects, resulting in effective time latency and motion artifacts in the image domain, can be appreciable, especially in the case of fast breathing. Purpose The aim of this work is to investigate intra-frame motion deterioration effects in MR-guided radiotherapy (MRgRT) by simulating the motion-corrupted image acquisition, and to explore the feasibility of deep-learning-based compensation approaches, relying on the intra-frame motion information which is spatially and temporally encoded in the raw data (k-space). Methods An intra-frame motion model was defined to simulate motion-corrupted MR images, with 4D anthropomorphic digital phantoms being exploited to provide ground truth 2D+t cine MR sequences. A total number of 10 digital phantoms were generated for lung cancer patients, with randomly selected eight patients for training or validation and the remaining two for testing. The simulation code served as the data generator, and a dedicated motion pattern perturbation scheme was proposed to build the intra-frame motion database, where three degrees of freedom were designed to guarantee the diversity of intra-frame motion trajectories, enabling a thorough exploration in the domain of the potential anatomical structure positions. U-Nets with three types of loss functions: L1 or L2 loss defined in image or Fourier domain, referred to as NNImgLoss-L1, NNFloss-L1 and NNL2-Loss were trained to extract information from the motion-corrupted image and used to estimate the ground truth final-position image, corresponding to the end of the acquisition. Images before and after compensation were evaluated in terms of (i) image mean-squared error (MSE) and mean absolute error (MAE), and (ii) accuracy of gross tumor volume (GTV) contouring, based on optical-flow image registration. Results Image degradation caused by intra-frame motion was observed: for a linearly and fully acquired Cartesian readout k-space trajectory, intra-frame motion resulted in an imaging latency of approximately 50% of the acquisition time; in comparison, the motion artifacts exhibited only a negligible contribution to the overall geometric errors. All three compensation models led to a decrease in image MSE/MAE and GTV position offset compared to the motion-corrupted image. In the investigated testing dataset for GTV contouring, the average dice similarity coefficients (DSC) improved from 88% to 96%, and the 95th percentile Hausdorff distance (HD95) dropped from 4.8 mm to 2.1 mm. Different models showed slight performance variations across different intra-frame motion amplitude categories: NNImgLoss-L1 excelled for small/medium amplitudes, whereas NNFloss-L1 demonstrated higher DSC median values at larger amplitudes. The saliency maps of the motion-corrupted image highlighted the major contribution of the later acquired k-space data, as well as the edges of the moving anatomical structures at their final positions, during the model inference stage. Conclusions Our results demonstrate the deep-learning-based approaches have the potential to compensate for intra-frame motion by utilizing the later acquired data to drive the convergence of the earlier acquired k-space components

    Feasibility of automated proton therapy plan adaptation for head and neck tumors using cone beam CT images

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    Background: Intensity modulated proton therapy (IMPT) of head and neck (H&N) tumors may benefit from plan adaptation to correct for the dose perturbations caused by weight loss and tumor volume changes observed in these patients. As cone beam CT (CBCT) is increasingly considered in proton therapy, it may be possible to use available CBCT images following intensity correction for plan adaptation. This is the first study exploring IMPT plan adaptation on CBCT images corrected and delineated by deformable image registration of the planning CT (pCT) to the CBCT, yielding a virtual CT (vCT). Methods: A Morphons algorithm was used to deform the pCTs and corresponding delineations of 9 H&N cancer patients to a weekly CBCT acquired within +/- 3 days of a control replanning CT scan (rpCT). The IMPT treatment plans were adapted using the vCT and the adapted and original plans were recalculated on the rpCT for dose/volume parameter evaluation of the impact of adaptation. Results: On the rpCT, the adapted plans were equivalent to the original plans in terms of target volumes D-95 and V-95, but showed a significant reduction of D-2 in these volumes. OAR doses were mostly equivalent or reduced. In particular, the adapted plans did not reduce parotid gland D-mean, but the dose to the optical system. For three patients the spinal cord or brain stem received higher, though well below tolerance, maximum dose. Subsequent tightening of the treatment planning constraints for these OARs on new vCT-adapted plans did not degrade target coverage and yielded pCT equivalent plans on the vCT. Conclusions: An offline automated procedure to generate an adapted IMPT plan on CBCT images was developed and investigated. When evaluating the adapted plan on a control rpCT we observed reduced D-2 in target volumes as major improvement. OAR sparing was only partially improved by the procedure. Despite potential limitations in the accuracy of the vCT approach, an improved quality of the adapted plans could be achieved

    Assessment of quantitative information for radiation therapy at a first-generation clinical photon-counting computed tomography scanner

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    As one of the latest developments in X-ray computed tomography (CT), photon-counting technology allows spectral detection, demonstrating considerable advantages as compared to conventional CT. In this study, we investigated the use of a first-generation clinical photon-counting computed tomography (PCCT) scanner and estimated proton relative (to water) stopping power (RSP) of tissue-equivalent materials from virtual monoenergetic reconstructions provided by the scanner. A set of calibration and evaluation tissue-equivalent inserts were scanned at 120 kVp. Maps of relative electron density (RED) and effective atomic number (EAN) were estimated from the reconstructed virtual monoenergetic images (VMI) using an approach previously applied to a spectral CT scanner with dual-layer detector technology, which allows direct calculation of RSP using the Bethe-Bloch formula. The accuracy of RED, EAN, and RSP was evaluated by root-mean-square errors (RMSE) averaged over the phantom inserts. The reference RSP values were obtained experimentally using a water column in an ion beam. For RED and EAN, the reference values were calculated based on the mass density and the chemical composition of the inserts. Different combinations of low- and high-energy VMIs were investigated in this study, ranging from 40 to 190 keV. The overall lowest error was achieved using VMIs at 60 and 180 keV, with an RSP accuracy of 1.27% and 0.71% for the calibration and the evaluation phantom, respectively

    Simultaneous object detection and segmentation for patientā€specific markerless lung tumor tracking in simulated radiographs with deep learning

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    Background Real-time tumor tracking is one motion management method to address motion-induced uncertainty. To date, fiducial markers are often required to reliably track lung tumors with X-ray imaging, which carries risks of complications and leads to prolonged treatment time. A markerless tracking approach is thus desirable. Deep learning-based approaches have shown promise for markerless tracking, but systematic evaluation and procedures to investigate applicability in individual cases are missing. Moreover, few efforts have been made to provide bounding box prediction and mask segmentation simultaneously, which could allow either rigid or deformable multi-leaf collimator tracking. Purpose The purpose of this study was to implement a deep learning-based markerless lung tumor tracking model exploiting patient-specific training which outputs both a bounding box and a mask segmentation simultaneously. We also aimed to compare the two kinds of predictions and to implement a specific procedure to understand the feasibility of markerless tracking on individual cases. Methods We first trained a Retina U-Net baseline model on digitally reconstructed radiographs (DRRs) generated from a public dataset containing 875 CT scans and corresponding lung nodule annotations. Afterwards, we used an independent cohort of 97 lung patients to develop a patient-specific refinement procedure. In order to determine the optimal hyperparameters for automatic patient-specific training, we selected 13 patients for validation where the baseline model predicted a bounding box on planning CT (PCT)-DRR with intersection over union (IoU) with the ground-truth higher than 0.7. The final test set contained the remaining 84 patients with varying PCT-DRR IoU. For each testing patient, the baseline model was refined on the PCT-DRR to generate a patient-specific model, which was then tested on a separate 10-phase 4DCT-DRR to mimic the intrafraction motion during treatment. A template matching algorithm served as benchmark model. The testing results were evaluated by four metrics: the center of mass (COM) error and the Dice similarity coefficient (DSC) for segmentation masks, and the center of box (COB) error and the DSC for bounding box detections. Performance was compared to the benchmark model including statistical testing for significance. Results A PCT-DRR IoU value of 0.2 was shown to be the threshold dividing inconsistent (68%) and consistent (100%) success (defined as mean bounding box DSC > 0.6) of PS models on 4DCT-DRRs. Thirty-seven out of the eighty-four testing cases had a PCT-DRR IoU above 0.2. For these 37 cases, the mean COM error was 2.6 mm, the mean segmentation DSC was 0.78, the mean COB error was 2.7 mm, and the mean box DSC was 0.83. Including the validation cases, the model was applicable to 50 out of 97 patients when using the PCT-DRR IoU threshold of 0.2. The inference time per frame was 170 ms. The model outperformed the benchmark model on all metrics, and the comparison was significant (p 0.2 cases, but not over the undifferentiated 84 testing cases. Conclusions The implemented patient-specific refinement approach based on a pre-trained baseline model was shown to be applicable to markerless tumor tracking in simulated radiographs for lung cases
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