Department of Neurosurgery, Academic Hospital Cologne-Merheim, Cologne, Germany.
Department of Engineering Technology (INDI), Vrije Universiteit Brussel, Brussels, Belgium.
Int J Comput Assist Radiol Surg. 2019 Jan;14(1):129-137. doi: 10.1007/s11548-018-1869-5. Epub 2018 Oct 6.
Reliable intraoperative delineation of tumor from healthy brain tissue is essentially based on the neurosurgeon's visual aspect and tactile impression of the considered tissue, which is-due to inherent low brain consistency contrast-a challenging task. Development of an intelligent artificial intraoperative tactile perception will be a relevant task to improve the safety during surgery, especially when-as for neuroendoscopy-tactile perception will be damped or-as for surgical robotic applications-will not be a priori existent. Here, we present the enhancements and the evaluation of a tactile sensor based on the use of a piezoelectric tactile sensor.
A robotic-driven piezoelectric bimorph sensor was excited using multisine to obtain the frequency response function of the contact between the sensor and fresh ex vivo porcine tissue probes. Based on load-depth, relaxation and creep response tests, viscoelastic parameters E and E for the elastic moduli and η for the viscosity coefficient have been obtained allowing tissue classification. Data analysis was performed by a multivariate cluster algorithm.
Cluster algorithm assigned five clusters for the assignment of white matter, basal ganglia and thalamus probes. Basal ganglia and white matter have been assigned to a common cluster, revealing a less discriminatory power for these tissue types, whereas thalamus was exclusively delineated; gray matter could even be separated in subclusters.
Bimorph-based, multisine-excited tactile sensors reveal a high sensitivity in ex vivo tissue-type differentiation. Although, the sensor principle has to be further evaluated, these data are promising.
可靠的术中肿瘤与健康脑组织的区分主要基于神经外科医生对所考虑组织的视觉和触觉印象，由于固有低脑一致性对比度，这是一项具有挑战性的任务。开发智能的术中触觉感知将是提高手术安全性的一项相关任务，尤其是在神经内镜检查中触觉感知会受到抑制，或者在手术机器人应用中，触觉感知不会先验存在的情况下。在这里，我们提出了一种基于压电触觉传感器使用的触觉传感器的增强和评估。
使用多正弦波激励机器人驱动的压电双晶传感器，以获得传感器与新鲜离体猪组织探针之间接触的频率响应函数。基于负载-深度、松弛和蠕变响应测试，获得了用于组织分类的弹性模量的粘弹性参数 E 和 E 以及粘度系数 η。数据分析采用多元聚类算法。
聚类算法为白质、基底节和丘脑探针分配了五个聚类。基底节和白质被分配到一个共同的聚类中，这表明这些组织类型的区分能力较低，而丘脑则被单独描绘出来；灰质甚至可以分为亚群。
基于双晶的多正弦激励触觉传感器在离体组织类型的区分中具有很高的灵敏度。尽管传感器原理需要进一步评估，但这些数据很有前途。
