A miniaturized ultrasonic micro-hole perforator for minimally invasive craniotomy

Objective: Micro-hole perforation on skull is urgently desired for minimally invasive insertion of micro-tools in brain for diagnostic or treatment purpose. However, a micro drill bit would easily fracture, making it difficult to safely generate a micro-hole on the hard skull. Methods: In this study, we present a method for ultrasonic vibration assisted micro-hole perforation on skull in a manner similar to subcutaneous injection on soft tissue. For this purpose, a high amplitude miniaturized ultrasonic tool with a 500 μm tip diameter micro-hole perforator was developed with simulation and experimental characterization. In-depth investigation of micro-hole generation mechanism was performed with systematic experiments on animal skull with a bespoke test rig; effects of vibration amplitude and feed rate on hole forming characteristics were systematically studied. It was observed that by exploiting skull bone's unique structural and material properties, the ultrasonic micro-perforator could locally damage bone tissue with micro-porosities, induce sufficient plastic deformation to bone tissue around the micro-hole and refrain elastic recovery after tool withdraw, generating a micro-hole on skull without material. Results : Under optimized conditions, high quality micro-holes could be formed on the hard skull with a force (< 1N) even smaller than that for subcutaneous injection on soft skin. Conclusion: This study would provide a safe and effective method and a miniaturized device for micro-hole perforation on skull for minimally invasive neural interventions

BIOMECHANICS ANALYSIS OF UMBILICAL VESSELS AND PLACENTA TISSUE IN NORMAL AND INTRAUTERINE GROWTH RESTRICTION PREGNANCIES

Ph.DDOCTOR OF PHILOSOPH

MOTORIZING AND OPTIMIZING ULTRASOUND STRAIN ELASTOGRAPHY FOR DETECTING INTRAUTERINE GROWTH RESTRICTION PREGNANCIES

International Fetal Medicine and Surgery Societ

Differences in placental capillary shear stress in fetal growth restriction may affect endothelial cell function and vascular network formation

10.1038/s41598-019-46151-6Scientific Reports9

Machine learning improves early prediction of small-for-gestational-age births and reveals nuchal fold thickness as unexpected predictor

10.1002/pd.5903Prenatal Diagnosis414505-51

A deep learning-based pipeline for analyzing the influences of interfacial mechanochemical microenvironments on spheroid invasion using differential interference contrast microscopic images

Metastasis is the leading cause of cancer-related deaths. During this process, cancer cells are likely to navigate discrete tissue-tissue interfaces, enabling them to infiltrate and spread throughout the body. Three-dimensional (3D) spheroid modeling is receiving more attention due to its strengths in studying the invasive behavior of metastatic cancer cells. While microscopy is a conventional approach for investigating 3D invasion, post-invasion image analysis, which is a time-consuming process, remains a significant challenge for researchers. In this study, we presented an image processing pipeline that utilized a deep learning (DL) solution, with an encoder-decoder architecture, to assess and characterize the invasion dynamics of tumor spheroids. The developed models, equipped with feature extraction and measurement capabilities, could be successfully utilized for the automated segmentation of the invasive protrusions as well as the core region of spheroids situated within interfacial microenvironments with distinct mechanochemical factors. Our findings suggest that a combination of the spheroid culture and DL-based image analysis enable identification of time-lapse migratory patterns for tumor spheroids above matrix-substrate interfaces, thus paving the foundation for delineating the mechanism of local invasion during cancer metastasis

Model learning analysis of 3D optoacoustic mesoscopy images for the classification of atopic dermatitis

10.1364/boe.415105Biomedical Optics Express1263671-368