A highly compact packaging concept for ultrasound transducer arrays embedded in neurosurgical needles

State-of-the-art neurosurgery intervention relies heavily on information from tissue imaging taken at a pre-operative stage. However, the data retrieved prior to performing an opening in the patient’s skull may present inconsistencies with respect to the tissue position observed by the surgeon during intervention, due to both the pulsing vasculature and possible displacements of the brain. The consequent uncertainty of the actual tissue position during the insertion of surgical tools has resulted in great interest in real-time guidance techniques. Ultrasound guidance during neurosurgery is a promising method for imaging the tissue while inserting surgical tools, as it may provide high resolution images. Microfabrication techniques have enabled the miniaturisation of ultrasound arrays to fit needle gauges below 2 mm inner diameter. However, the integration of array transducers in surgical needles requires the development of advanced interconnection techniques that can provide an interface between the microscale array elements and the macroscale connectors to the driving electronics. This paper presents progress towards a novel packaging scheme that uses a thin flexible printed circuit board (PCB) wound inside a surgical needle. The flexible PCB is connected to a probe at the tip of the needle by means of magnetically aligned anisotropic conductive paste. This bonding technology offers higher compactness compared to conventional wire bonding, as the individual electrical connections are isolated from one another within the volume of the paste line, and applies a reduced thermal load compared to thermo-compression or eutectic packaging techniques. The reduction in the volume required for the interconnection allows for denser wiring of ultrasound probes within interventional tools. This allows the integration of arrays with higher element counts in confined packages, potentially enabling multi-modality imaging with Raman, OCT, and impediography. Promising experimental results and a prototype needle assembly are presented to demonstrate the viability of the proposed packaging scheme. The progress reported in this work are steps towards the production of fully-functional imaging-enabled needles that can be used as surgical guidance tools

Nothing Lasts Forever: Environmental Discourses on the Collapse of Past Societies

The study of the collapse of past societies raises many questions for the theory and practice of archaeology. Interest in collapse extends as well into the natural sciences and environmental and sustainability policy. Despite a range of approaches to collapse, the predominant paradigm is environmental collapse, which I argue obscures recognition of the dynamic role of social processes that lie at the heart of human communities. These environmental discourses, together with confusion over terminology and the concepts of collapse, have created widespread aporia about collapse and resulted in the creation of mixed messages about complex historical and social processes

Local coding based matching kernel method for image classification

This paper mainly focuses on how to effectively and efficiently measure visual similarity for local feature based representation. Among existing methods, metrics based on Bag of Visual Word (BoV) techniques are efficient and conceptually simple, at the expense of effectiveness. By contrast, kernel based metrics are more effective, but at the cost of greater computational complexity and increased storage requirements. We show that a unified visual matching framework can be developed to encompass both BoV and kernel based metrics, in which local kernel plays an important role between feature pairs or between features and their reconstruction. Generally, local kernels are defined using Euclidean distance or its derivatives, based either explicitly or implicitly on an assumption of Gaussian noise. However, local features such as SIFT and HoG often follow a heavy-tailed distribution which tends to undermine the motivation behind Euclidean metrics. Motivated by recent advances in feature coding techniques, a novel efficient local coding based matching kernel (LCMK) method is proposed. This exploits the manifold structures in Hilbert space derived from local kernels. The proposed method combines advantages of both BoV and kernel based metrics, and achieves a linear computational complexity. This enables efficient and scalable visual matching to be performed on large scale image sets. To evaluate the effectiveness of the proposed LCMK method, we conduct extensive experiments with widely used benchmark datasets, including 15-Scenes, Caltech101/256, PASCAL VOC 2007 and 2011 datasets. Experimental results confirm the effectiveness of the relatively efficient LCMK method