Objective. To simultaneously deblur and supersample prostate specific membrane antigen (PSMA) positron emission tomography (PET) images using neural blind deconvolution.Approach. Blind deconvolution is a method of estimating the hypothetical \'deblurred\' image along with the blur kernel (related to the point spread function) simultaneously. Traditionalmaximum a posterioriblind deconvolution methods require stringent assumptions and suffer from convergence to a trivial solution. A method of modelling the deblurred image and kernel with independent neural networks, called \'neural blind deconvolution\' had demonstrated success for deblurring 2D natural images in 2020. In this work, we adapt neural blind deconvolution to deblur PSMA PET images while simultaneous supersampling to double the original resolution. We compare this methodology with several interpolation methods in terms of resultant blind image quality metrics and test the model\'s ability to predict accurate kernels by re-running the model after applying artificial \'pseudokernels\' to deblurred images. The methodology was tested on a retrospective set of 30 prostate patients as well as phantom images containing spherical lesions of various volumes.Main results. Neural blind deconvolution led to improvements in image quality over other interpolation methods in terms of blind image quality metrics, recovery coefficients, and visual assessment. Predicted kernels were similar between patients, and the model accurately predicted several artificially-applied pseudokernels. Localization of activity in phantom spheres was improved after deblurring, allowing small lesions to be more accurately defined.Significance. The intrinsically low spatial resolution of PSMA PET leads to partial volume effects (PVEs) which negatively impact uptake quantification in small regions. The proposed method can be used to mitigate this issue, and can be straightforwardly adapted for other imaging modalities.

Polyvinyl ether siloxane (PVES) possesses ideal characteristics for making precise and accurate dental impressions. PVES dimensional stability owes to its better polymeric properties derived from its parent materials poly ethers and polyvinyl siloxanes. As recommended use of chemical disinfecting agents is getting more popular, there is a growing concern associated with the effect of disinfectants on PVES dimensional stability. This study was aimed to understand the PVES behavior when subjected to chemical disinfectants.
The data was collected from research studies retrieved from Google Scholar, Scopus, and PubMed using MeSH terms of keywords \"vinyl polyether siloxane AND Disinfection\" or (Vinyl polyether siloxane OR polyvinyl siloxane ether OR PVES) AND (disinfectant OR disinfection)\" without any restriction to publication date. The PRISMA (Preferred Reporting Items for Systemic Review and Meta-Analysis) directions were observed during the data collection, screening of studies, and meta-analysis. The primary data were retrieved, and batch exported from databases using Harzing\'s Publish or Perish software; primary analysis was performed in Microsoft Excel, while statistical analysis for effect size, two-tailed p-values, and heterogeneity among studies was performed using Meta Essentials. The effect size was calculated using Hedge\'s g values at the 95% confidence level using the random-effects model. Heterogeneity among studies was measured using the Cochrane Q and I2.
Dental impressions made from the PVES elastomeric impression materials showed no significant changes in dimensional stability. Immersion in the chemical disinfectant for 10 min was associated with clinically irrelevant changes in the dimensions of the PVES impressions. Disinfection with sodium hypochlorite was associated with clinically significant changes in dimensions, with a two-tailed p-value of 0.049. Disinfection with 2-2.5% glutaraldehyde solution was not associated with any significant dimensional variability.