Obstetric analgesia using continuous epidural perfusion of bupivacaine, adrenaline, and fentanyl

OBJECTIVES: To determine the efficacy and complications of continuous epidural perfusion of bupivacaine, adrenaline and fentanyl in the relief of pain during first and second stage labour during vaginal birth. PATIENTS AND METHODS: Between January 1990 and March 1993 we used continuous epidural perfusion for control of pain during labor in 1307 women. The solution administered through an epidural catheter and maintained until expulsion was one 10 ml bolus of bupivacaine 0.25% with adrenaline 1:200,000 and fentanyl 25 micrograms followed by continuous perfusion of bupivacaine 0.0625% with adrenaline 1:200,000 and fentanyl 2 micrograms/ml at an infusion rate of 12 ml/h. When analgesia was insufficient, a bolus of local anesthetic was administered or a pudendal block was carried out. RESULTS: Ninety-two percent of the birthing women reported good analgesic effect during the first stage; for 7% the effect was fair and for 0.55% it was poor. During the second stage 88% reported satisfactory analgesia, and 8% fair or poor. Assessment was not possible for the remaining women, who underwent cesarean sections. Complications were few and easily controllable. CONCLUSIONS: Maintenance of epidural perfusion with 0.0625% bupivacaine with adrenaline 1:200,000 and fentanyl 2 micrograms/ml provides sufficient analgesia during all stages of childbirth.     

4D MRI Flow Coupled to Physics‐Based Fluid Simulation for Blood‐Flow Visualization

Modern MRI measurements deliver volumetric and time‐varying blood‐flow data of unprecedented quality. Visual analysis of these data potentially leads to a better diagnosis and risk assessment of various cardiovascular diseases. Recent advances have improved the speed and quality of the imaging data considerably. Nevertheless, the data remains compromised by noise and a lack of spatiotemporal resolution. Besides imaging data, also numerical simulations are employed. These are based on mathematical models of specific features of physical reality. However, these models require realistic parameters and boundary conditions based on measurements. We propose to use data assimilation to bring measured data and physically‐based simulation together, and to harness the mutual benefits. The accuracy and noise robustness of the coupled approach is validated using an analytic flow field. Furthermore, we present a comparative visualization that conveys the differences between using conventional interpolation and our coupled approach.     

PID autotuning for weighted servo/regulation control operation

This paper analyzes optimal controller settings for controllers with One-Degree-of-Freedom (1-DoF) Proportional-Integral-Derivative (PID) structure. A new analysis is conducted from the point of view of the operating mode (either servo or regulation mode) of the control-loop and tuning mode of the controller. Performance of the optimal tuning settings can be degraded when the operating mode is different from that selected for tuning and obviously both situations can be present in any control system. In this context, a Weighted Performance Degradation index, that considers the importance and balance between the servo and regulation operation modes, is minimized and based on this minimization, an autotuning procedure as a function of the normalized process dead-time is proposed.     

Interactive Fibre Structure Visualization of the Heart

The heart consists of densely packed muscle fibres. The orientation of these fibres can be acquired by using Diffusion Tensor Imaging (DTI) ex vivo. A good way to visualize the fibre structure in a cross section of the heart is by showing short line segments originating from the cross section and aligned with the local direction of the fibres. If the line segments are placed dense enough, one can see how the fibre orientations change. However, generation of the line segments takes time and thus the user has to wait for new geometry to be generated when the plane defining the cross section is changed. We present a new direct rendering method for the visualization of the 3D vector field in a 2D user‐definable cross section of a heart. On the intersection of the plane with the vector field, the full 3D vectors are rendered as 3D line segments with a local ray casting approach. No preprocessing of the data is needed and no geometry is generated. This technique allows a fast inspection of the data to identify interesting areas where further analysis is necessary (e.g. quantification or generation of streamlines). We also show how the technique is generalized to other glyph shapes than line segments by implementing ellipsoids.     

Robust tuning of Two-Degree-of-Freedom (2-DoF) PI/PID based cascade control systems

A design approach for Two-Degree-of-Freedom (2-DoF) PID controllers within a cascade control configuration that guarantees robust and smooth control is presented in this paper. The use of a cascade control configuration comes into place when the use of an additional (intermediate) sensor provides the possibility for a compensation of a load-disturbance before it affects the output variable. The rationale of operation associated to both the inner and outer controllers determines the need of good performance for disturbance attenuation (regulation) as well as set-point following (tracking). Therefore, the use of 2-DoF controllers is introduced. However, the use of 2-DoF controllers, introduces additional parameters that need to be tuned appropriately. Specially for the case of PI/PID controllers there are not known clear auto-tuning guidelines for such situation. The approach undertaken in this paper provides the complete set of tuning parameters for the inner (2-DoF PI) controller and the outer (2-DoF PID) controller. The trade-off among control system performance (measured in terms of closed-loop response speed) and robustness allows to derive a recommendation for the design-parameter lower limit. The design equations are formulated in such a way that a non-oscillatory response is specified for both the inner and outer loop. A side advantage of providing the complete set of parameters is that it avoids the need for the usual identification experiment for the tuning of the outer controller.     

Visual Analysis of Tumor Control Models for Prediction of Radiotherapy Response

In radiotherapy, tumors are irradiated with a high dose, while surrounding healthy tissues are spared. To quantify the probability that a tumor is effectively treated with a given dose, statistical models were built and employed in clinical research. These are called tumor control probability (TCP) models. Recently, TCP models started incorporating additional information from imaging modalities. In this way, patient‐specific properties of tumor tissues are included, improving the radiobiological accuracy of models. Yet, the employed imaging modalities are subject to uncertainties with significant impact on the modeling outcome, while the models are sensitive to a number of parameter assumptions. Currently, uncertainty and parameter sensitivity are not incorporated in the analysis, due to time and resource constraints. To this end, we propose a visual tool that enables clinical researchers working on TCP modeling, to explore the information provided by their models, to discover new knowledge and to confirm or generate hypotheses within their data. Our approach incorporates the following four main components: (1) It supports the exploration of uncertainty and its effect on TCP models; (2) It facilitates parameter sensitivity analysis to common assumptions; (3) It enables the identification of inter‐patient response variability; (4) It allows starting the analysis from the desired treatment outcome, to identify treatment strategies that achieve it. We conducted an evaluation with nine clinical researchers. All participants agreed that the proposed visual tool provides better understanding and new opportunities for the exploration and analysis of TCP modeling.     

Hierarchical Stochastic Neighbor Embedding

In recent years, dimensionality‐reduction techniques have been developed and are widely used for hypothesis generation in Exploratory Data Analysis. However, these techniques are confronted with overcoming the trade‐off between computation time and the quality of the provided dimensionality reduction. In this work, we address this limitation, by introducing Hierarchical Stochastic Neighbor Embedding (Hierarchical‐SNE). Using a hierarchical representation of the data, we incorporate the well‐known mantra of Overview‐First, Details‐On‐Demand in non‐linear dimensionality reduction. First, the analysis shows an embedding, that reveals only the dominant structures in the data (Overview). Then, by selecting structures that are visible in the overview, the user can filter the data and drill down in the hierarchy. While the user descends into the hierarchy, detailed visualizations of the high‐dimensional structures will lead to new insights. In this paper, we explain how Hierarchical‐SNE scales to the analysis of big datasets. In addition, we show its application potential in the visualization of Deep‐Learning architectures and the analysis of hyperspectral images.     

Visualization of myocardial perfusion derived from coronary anatomy

Visually assessing the effect of the coronary artery anatomy on the perfusion of the heart muscle in patients with coronary artery disease remains a challenging task. We explore the feasibility of visualizing this effect on perfusion using a numerical approach. We perform a computational simulation of the way blood is perfused throughout the myocardium purely based on information from a three-dimensional anatomical tomographic scan. The results are subsequently visualized using both three-dimensional visualizations and bull's eye plots, partially inspired by approaches currently common in medical practice. Our approach results in a comprehensive visualization of the coronary anatomy that compares well to visualizations commonly used for other scanning technologies. We demonstrate techniques giving detailed insight in blood supply, coronary territories and feeding coronary arteries of a selected region. We demonstrate the advantages of our approach through visualizations that show information which commonly cannot be directly observed in scanning data, such as a separate visualization of the supply from each coronary artery. We thus show that the results of a computational simulation can be effectively visualized and facilitate visually correlating these results to for example perfusion data.     

Simple robust autotuning rules for 2-DoF PI controllers

This paper addresses the problem of providing simple tuning rules for a Two-Degree-of-Freedom (2-DoF) PI controller (PI(2)) with robustness considerations. The introduction of robustness as a matter of primary concern is by now well established among the control community. Among the different ways of introducing a robustness constraint into the design stage, the purpose of this paper is to use the maximum sensitivity value as the design parameter. In order to deal with the well known performance/robustness tradeoff, an analysis is conducted first that allows the determination of the lowest closed-loop time constant that guarantees a desired robustness. From that point, an analytical design is conducted for the assignment of the load-disturbance dynamics followed by the tuning of the set-point weight factor in order to match, as much as possible, the set-point-to-output dynamics according to a first-order-plus-dead-time dynamics. Simple tuning rules are generated by considering specific values for the maximum sensitivity value. These tuning rules, provide all the controller parameters parameterized in terms of the open-loop normalized dead-time allowing the user to select a high/medium/low robust closed-loop control system. The proposed autotuning expressions are therefore compared with other well known tuning rules also conceived by using the same robustness measure, showing that the proposed approach is able to guarantee the same robustness level and improve the system time performance.     

Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches

Automatic segmentation of multiple sclerosis (MS) lesions in brain MRI has been widely investigated in recent years with the goal of helping MS diagnosis and patient follow-up. However, the performance of most of the algorithms still falls far below expert expectations. In this paper, we review the main approaches to automated MS lesion segmentation. The main features of the segmentation algorithms are analysed and the most recent important techniques are classified into different strategies according to their main principle, pointing out their strengths and weaknesses and suggesting new research directions. A qualitative and quantitative comparison of the results of the approaches analysed is also presented. Finally, possible future approaches to MS lesion segmentation are discussed.