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Objective: Minimal literature exists investigating changes in inflammation with respect to the main nasal cavity (MNC) and paranasal sinuses (PS) before and after maximal medical therapy (MMT) for chronic rhinosinusitis (CRS). We hypothesized that MMT produces a differential level of change in the volume of air space in the MNC and PS, and that resolution of mucosal disease associated with the osteomeatal complex (OMC) influences clinical response to MMT. Study Design: Retrospective study of 12 pre- and post-MMT sinus-CT scans from 6 subjects with CRS, of which three succeeded and three failed therapy. Methods: Mimics was used to create 3D-models of the MNC and PS, and then analysis of the models was performed. Results: Mean differences in the sinonasal volume were 7866.5+/-4339.9 mm3 and 17869.10+/-19472.70 mm3, amongst the failures and successes, respectively. There is wide variability in the contribution of PS and MNC airspace volume change to the overall change in the sinonasal volume. In two subjects, the direction of volume change in the MNC and PS diverged with respect to the overall change in volume. Line-of-sight analysis demonstrated that successful responders to MMT had more patent MNC with direct access to the OMC. Conclusions: There is a differential contribution to sinonasal, airspace volume change after MMT, when comparing the MNC and PS. Response to MMT may not be solely attributable to PS change and may include a function of MNC change. Line-of-sight models suggest that direct access to the OMC may impact response to MMT.
Introduction: Topical intranasal drugs are widely prescribed for Chronic Rhinosinusitis (CRS), although delivery can vary with device type and droplet size. The study objective was to compare nebulized and sprayed droplet deposition in the paranasal
Background: Investigation of the functioning of the brain in living systems has been a major effort amongst scientists and medical practitioners. Amongst the various disorder of the brain, epilepsy has drawn the most attention because this disorder c
Purpose: To develop an automated machine-learning-based method for the discovery of rapid and quantitative chemical exchange saturation transfer (CEST) MR fingerprinting acquisition and reconstruction protocols. Methods: An MR physics governed AI s
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