Automated detection of electrically evoked stapedius reflexes (eSR) during cochlear implantation.

INTRODUCTION: In cochlear implantation, objective fitting methods are needed to optimize audiological results in small children or patients with poor compliance. Intraoperatively measured electrically evoked stapedius reflexes (eSR) can be used as a marker for the patient's discomfort level. The aim of this study was to develop and evaluate an automated detection method for eSR and to compare it to the detection rate of the surgeon and independent observers. METHODS: Cochlear implantation using a fully digital surgical microscope was performed. Movements of the stapedius tendon were recorded and analyzed by means of computer vision technique. Differences in eSR elicited by stimulating electrodes at different cochlear locations (basal, middle and apical) were analyzed. The eSR detection rate of the image processing algorithm was compared to the surgeon's detection rate and to those of two less experienced observers. RESULTS: A total of 387 electrically impulses were applied. The stimulation of middle turn electrodes showed significantly higher detection rates (50.4%) compared to the basal (40.0%; p = 0.001) and apical (43.6%; p = 0.03) turn. The software identified significantly more of the applied stimuli (58.4%) compared to the surgeon (46.3%; p = 0.0007), the intermediate observer (37.7%; p < 0.0001) and the unexperienced observer (41.3%; p < 0.0001). CONCLUSION: The feasibility of an automated intraoperative software-based detection of eSR is demonstrated. By improving the eSR detection methods and their clinical applicability, their utility in objective cochlear implant fitting may be substantially increased.

Total Variation Regularization of Pose Signals with an Application to 3D Freehand Ultrasound.

Three-dimensional freehand imaging techniques are gaining wider adoption due to their ?exibility and cost ef?ciency. Typical examples for such a combination of a tracking system with an imaging device are freehand SPECT or freehand 3D ultrasound. However, the quality of the resulting image data is heavily dependent on the skill of the human operator and on the level of noise of the tracking data. The latter aspect can introduce blur or strong artifacts, which can signi?cantly hamper the interpretation of image data. Unfortunately, the most commonly used tracking systems to date, i.e. optical and electromagnetic, present a trade-off between invading the surgeon's workspace (due to line-of-sight requirements) and higher levels of noise and sensitivity due to the interference of surrounding metallic objects. In this work, we propose a novel approach for total variation regularization of data from tracking systems (which we term pose signals) based on a variational formulation in the manifold of Euclidean transformations. The performance of the proposed approach was evaluated using synthetic data as well as real ultrasound sweeps executed on both a Lego phantom and human anatomy, showing signi?cant improvement in terms of tracking data quality and compounded ultrasound images. Source code can be found at https://github.com/IFL-CAMP/pose_regularization.

X-ray computed tomography using curvelet sparse regularization.

PURPOSE: Reconstruction of x-ray computed tomography (CT) data remains a mathematically challenging problem in medical imaging. Complementing the standard analytical reconstruction methods, sparse regularization is growing in importance, as it allows inclusion of prior knowledge. The paper presents a method for sparse regularization based on the curvelet frame for the application to iterative reconstruction in x-ray computed tomography. METHODS: In this work, the authors present an iterative reconstruction approach based on the alternating direction method of multipliers using curvelet sparse regularization. RESULTS: Evaluation of the method is performed on a specifically crafted numerical phantom dataset to highlight the method's strengths. Additional evaluation is performed on two real datasets from commercial scanners with different noise characteristics, a clinical bone sample acquired in a micro-CT and a human abdomen scanned in a diagnostic CT. The results clearly illustrate that curvelet sparse regularization has characteristic strengths. In particular, it improves the restoration and resolution of highly directional, high contrast features with smooth contrast variations. The authors also compare this approach to the popular technique of total variation and to traditional filtered backprojection. CONCLUSIONS: The authors conclude that curvelet sparse regularization is able to improve reconstruction quality by reducing noise while preserving highly directional features.

A mixed-culture model of a probiotic biofilm control system.

We present a mathematical model and computer simulations for the control of a pathogenic biofilm by a probiotic biofilm. This is a substantial extension of a previous model of control of a pathogenic biofilm by microbial control agents that are suspended in the aqueous bulk phase (H. Khassehkhan and H.J. Eberl, Comp. Math. Meth. Med, 9(1) (2008), pp. 47-67). The mathematical model is a system of double-degenerate diffusion-reaction equations for the microbial biomass fractions probiotics, pathogens and inert bacteria, coupled with convection-diffusion-reaction equations for two growth controlling substrates, protonated lactic acids and hydrogen ions (pH). The latter are produced by the bacteria and become detrimental at high concentrations. In simulation studies, we find that the site of attachment of probiotics in the flow channel is crucial for success and efficacy of the probiotic control mechanism.