Medical Engineering & Physics最新文献

In response to the difficulties in accurately reproducing the resistance drop generated by puncturing key tissue layers with a needle and the poor experience in existing simulators, based on the continuous controllability and rapid response of magnetorheological fluid under the influence of a magnetic field, this paper proposes a lumbar puncture training simulator(LPTS) that can accurately simulate the puncture feedback force within tissues such as the skin, subcutaneous fat, and supraspinous ligament throughout the entire process. By using a dual rod structure and reasonably arranging the damping channel gap, the influence of mechanical friction and zero-field damping force on the feedback force during tissue progression is minimized. This paper introduces the acquisition and modeling analysis of raw data, and based on this, the design, simulation, and mechanical testing of the simulator are carried out. Finally, a performance testing platform for the simulator is established to evaluate its tracking performance of the expected puncture strength and the reproducibility of the puncture sensation. The results show that the experimental puncture strength deviates from the expected puncture strength by 0.35 N to 0.61 N in the crucial steps of breaking through the supraspinous ligament, interspinous ligament, ligamentum flavum, and dura mater, with a relative error below 10 %.

In this paper, the mechanical behaviour and failure of porous additively manufactured (AM) polylactide (PLA) scaffolds based on the triply periodic minimal surfaces (TPMS) is investigated using numerical calculations of their unit cells and representative volumes. The strain-amplification factor is chosen as the main parameter, and the most critical locations for failure of different types of scaffold structures are evaluated. The results obtained are presented in comparison with a multiple-crack-growth algorithm using the extended finite element method (XFEM), underpinned by the experimentally obtained fracture properties of PLA. The effect of morphology of TPMS structures on the pre-critical, critical and post-critical behaviours of scaffolds under monotonic loading regimes is assessed. The results provide an understanding of the fracture behaviour and main risk points for crack initiation in structures of AM-PLA scaffolds based on typical commonly used types of TPMS, as well as the influence of structure type and external load on this behaviour.

Robotic minimally invasive surgery (MIS) has changed numerous surgical techniques in the past few years and enhanced their results. Haptic feedback is integrated into robotic surgical systems to restore the surgeon's perception of forces in response to interaction with objects in the surgical environment. The ideal exact emulation of the robot's interaction with its physical environment in free space is a very challenging problem to solve completely. Previously, we introduced the surgical robotic platform (SRP) with a novel concentric connector joint (CCJ). This study aims to develop a haptic control system that integrates an active constraint controller into a surgical robot platform. We have successfully established haptic feedback control for the surgical robot using constraint control and inverse kinematic relationships integrated into the overall positioning structure. A preliminary feasibility study, modelling, and simulation were presented.

Compared to traditional unipolar radiofrequency ablation (RFA), bipolar RFA offers advantages such as more precise heat transfer and higher ablation efficiency. Clinically, myocardial baseline impedance (BI) is one of the important factors affecting the effectiveness of ablation. We aim at finding suitable ablation protocols and coping strategies by analyzing the ablation effects and myocardial impedance changes of bipolar RFA under different BIs. In this research, a three-dimensional local myocardial computer model was constructed for bipolar RFA simulation, and in vitro experimental data were used to validate accuracy. Four fixed low-power levels (20 W, 25 W, 30 W, and 35 W) and six myocardial BIs (91.02 Ω, 99.83 Ω, 111.03 Ω, 119.77 Ω, 130.03 Ω, and 135.45 Ω) were set as initial conditions, with an ablation duration of 120-s. In the context of low-power and long-duration (LPLD) ablation, the maximum TID (TID_M) decreased by 21–32 Ω, depending on the BI. In cases where steam pop did not occur, TID_M increased with the increase in power. For the same power, there was no significant difference in TID_M for the range of BIs. In cases where steam pop occurred, for every 1 Ω increase in BI, TID_M increased by 0.34–0.41 Ω. The simulation results also showed that using a higher power resulted in a smaller decrease in TID_M. This study provided appropriate ablation times and impedance decrease ranges for bipolar LPLD RFA. The combination of 25 W for 120-s offered optimal performance when considering effectiveness and safety simultaneously.

Different types of noise contaminating the surface electromyogram (EMG) signal may degrade the recognition performance. For noise removal, the type of noise has to first be identified. In this paper, we propose a real-time efficient system for identifying a clean EMG signal and noisy EMG signals contaminated with any one of the following three types of noise: electrocardiogram interference, spike noise, and power line interference. Two statistical descriptors, kurtosis and skewness, are used as input features for the cascading quadratic discriminant analysis classifier. An efficient simplification of kurtosis and skewness calculations that can reduce computation time and memory storage is proposed. The experimental results from the real-time system based on an ATmega 2560 microcontroller demonstrate that the kurtosis and skewness values show root mean square errors between the traditional and proposed efficient techniques of 0.08 and 0.09, respectively. The identification accuracy with five-fold cross-validation resulting from the quadratic discriminant analysis classifier is 96.00%.

Temporal-bone milling is a delicate process commonly performed during otologic surgery to gain access to the middle and inner ear structures. Because of the numerous at-risk structures of this anatomic area, extensive surgeon training is required. Artificial temporal bones offer an interesting alternative to cadaveric training. However, the evaluation of such simulators has not been systematic, with an absence of objective validation of their milling response, especially in a surgical context.

By measuring the milling forces obtained during the classical steps of otologic surgery on six 3D-printed and three cadaveric temporal bones, this work aims at evaluating the ability of the OTOtwin® synthetic temporal bone to reproduce human bone behavior.

A better repeatability was obtained for artificial bones than for cadaveric ones. However, the level of forces recorded during artificial bone milling was close to the one measured with cadaveric samples. The effects of both surgical phase and irrigation on milling force levels were also quantified. The experiments conducted in this study confirmed the suitability of OTOtwin® temporal bone model for both otologic surgery training and research purposes. Valuable insights were also gained from this study regarding the understanding of the otologic milling process.

This article introduces a fast fractal method for classifying breast cancerous lesions in mammography. While fractal methods are valuable for extracting information, they often come with a high computational load and time consumption. This paper demonstrates that extracting optimal fractal information and focusing only on valuable information for classification not only improves computation speed and reduces process load but also enhances classification accuracy. To achieve this, we define an objective function based on accurate classification of benign and malignant masses to identify the best scale. Instead of extracting information from all nine scales, we extract and employ information solely from the best scale for classification. We validate the obtained scales using three classifiers: Support Vector Machine (SVM), Genetic Algorithm (GA), and Deep Learning (DL), which confirm the effectiveness of the proposed method. Comparative analysis with other studies reveals improved classification performance with the presented method.