Bayesian segmentation of human facial tissue using 3D MR-CT information fusion, resolution enhancement and partial volume modelling

Single-image Bayesian Restoration and Multi-image Super-resolution Restoration for B-mode Ultrasound Using an Accurate System Model Involving Correlated Nature of the Speckle Noise

B-mode ultrasound is an essential part of radiological examinations due to its low cost, safety, and portability, but has the drawbacks of the speckle noise and output of most systems is two-dimensional (2D) cross sections. Image restoration techniques, using mathematical models for image degradation and noise, can be used to boost resolution (deconvolution) as well as to reduce the speckle. In this study, new single-image Bayesian restoration (BR) and multi-image super-resolution restoration (BSRR) methods are proposed for in-plane B-mode ultrasound images. The spatially correlated nature of the speckle was modeled, allowing for examination of two different models for BR and BSRR for uncorrelated Gaussian (BR-UG, BSRR-UG) and correlated Gaussian (BR-CG, BSRR-CG). The performances of these models were compared with common image restoration methods (Wiener filter, bilateral filtering, and anisotropic diffusion). Well-recognized metrics (peak signal-to-noise ratio, contrast-to-noise ratio, and normalized information density) were used for algorithm free-parameter estimation and objective evaluations. The methods were tested using superficial tissue (2D scan data collected from volunteers, tissue-mimicking resolutions, and breast phantoms). Improvement in image quality was assessed by experts using visual grading analysis. In general, BSRR-CG performed better than all other methods. A potential downside of BSRR-CG is increased computation time, which can be addressed by the use of high-performance graphics processing units (GPUs).

Navigation-guided resection of maxillary tumors: Can a new volumetric virtual planning method improve outcomes in terms of control of resection margins?

INTRODUCTION

In the present study, our aim was to confirm the role of navigation-guided surgery in reducing the percentage of positive margins in advanced malignant pathologies of the mid-face, by introducing a new volumetric virtual planning method for resection.

MATERIALS AND METHODS

Twenty-eight patients were included in this study. Eighteen patients requiring surgery to treat malignant midface tumors were prospectively selected and stratified into two different study groups. Patients enrolled in the Reference Points Resection group (RPR - 10 patients) underwent resection planning using the anatomical landmarks on CT scan; patients enrolled in the Volume Resection group (VR - 8 patients) underwent resection using the new volumetric virtual planning method. The remaining 10 patients (Control group) were treated without the use of a navigation system.

RESULTS

In total, 127 margins were pathologically assessed in the RPR group, 75 in the VR group, and 85 in the control group. In the control group, 16% of the margins were positive, while in the RPR group the value was 9%, and in the VR group 1%.

CONCLUSIONS

The volumetric tumor resection planning associated to the navigation-guide resection appeared to be an improvement in terms of control of surgical margins in advanced tumors involving the mid-face.