Influence of ultrasonic activation in association with different final irrigants on intracanal smear layer removal

Aim: To evaluate the influence of ultrasonic activation (US) with different irrigant regimens in smear layer removal. Methods: One hundred bovine incisors were instrumented and divided into ten groups (n=10) according to final irrigation protocols: distilled water (DW); DW+US; 17% EDTA; QMix; 10% citric acid; 37% phosphoric acid; 17% EDTA+US; QMix+US; 10% citric acid+US; 37% phosphoric acid+US. The samples were then submitted to scanning electron microscopy where a score system was used to evaluate the images and effectiveness of proposed treatments. The data were statistically analyzed by Kruskal-Wallis and Mann-Whitney U tests for intergroup comparisons as well as the Wilcoxon and Friedman tests for intragroup comparisons at 5% significance level. Results: In the cervical third, groups 17% EDTA, QMix, 10% citric acid, 17% EDTA+US, QMix+US and 10% citric acid+US were more effective in smear layer removal (p<0.05); in the middle third, groups 17% EDTA+US and QMix+US were more effective in smear layer removal (p<0.05); in the apical third, groups 17% EDTA,17% EDTA+US and QMix+US were more effective in smear layer removal (p<0.05). Conclusions: US can aid 17% EDTA and QMix in smear layer removal at the middle third and QMix at the apical third, contributing to the cleaning of root canal system.

Efficient representation of texture details in medical images by fusion of Ripplet and DDCT transformed images

Purpose: To evaluate and compare the performance of Ripplet Type-1 transform and directional discrete cosine transform (DDCT) and their combinations for improved representation of MRI images while preserving its fine features such as edges along the smooth curves and textures. Methods: In a novel image representation method based on fusion of Ripplet type-1 and conventional/directional DCT transforms, source images were enhanced in terms of visual quality using Ripplet and DDCT and their various combinations. The enhancement achieved was quantified on the basis of peak signal to noise ratio (PSNR), mean square error (MSE), structural content (SC), average difference (AD), maximum difference (MD), normalized cross correlation (NCC), and normalized absolute error (NAE). To determine the attributes of both transforms, these transforms were combined to represent the entire image as well. All the possible combinations were tested to present a complete study of combinations of the transforms and the contrasts were evaluated amongst all the combinations. Results: While using the direct combining method (DDCT) first and then the Ripplet method, a PSNR value of 32.3512 was obtained which is comparatively higher than the PSNR values of the other combinations. This novel designed technique gives PSNR value approximately equal to the PSNR’s of parent techniques. Along with this, it was able to preserve edge information, texture information and various other directional image features. The fusion of DDCT followed by the Ripplet reproduced the best images. Conclusion: The transformation of images using Ripplet followed by DDCT ensures a more efficient method for the representation of images with preservation of its fine details like edges and textures.