Intraoperative Needle Tip Tracking with an Integrated Fibre-Optic Ultrasound Sensor.

Ultrasound is an essential tool for guidance of many minimally-invasive surgical and interventional procedures, where accurate placement of the interventional device is critical to avoid adverse events. Needle insertion procedures for anaesthesia, fetal medicine and tumour biopsy are commonly ultrasound-guided, and misplacement of the needle may lead to complications such as nerve damage, organ injury or pregnancy loss. Clear visibility of the needle tip is therefore critical, but visibility is often precluded by tissue heterogeneities or specular reflections from the needle shaft.

Ultrasonic Needle Tracking with Dynamic Electronic Focusing.

Accurate identification of the needle tip is a key challenge with ultrasound-guided percutaneous interventions in regional anaesthesia, foetal surgery and cardiovascular medicine. In this study, we developed an ultrasonic needle tracking system in which the measured needle tip location was used to set the electronic focus of the external ultrasound imaging probe. In this system, needle tip tracking was enabled with a fibre-optic ultrasound sensor that was integrated into a needle stylet, and the A-lines recorded by the sensor were processed to generate tracking images of the needle tip.

Zero-Shot Super-Resolution With a Physically-Motivated Downsampling Kernel for Endomicroscopy.

Super-resolution (SR) methods have seen significant advances thanks to the development of convolutional neural networks (CNNs). CNNs have been successfully employed to improve the quality of endomicroscopy imaging. Yet, the inherent limitation of research on SR in endomicroscopy remains the lack of ground truth high-resolution (HR) images, commonly used for both supervised training and reference-based image quality assessment (IQA).

Learning from irregularly sampled data for endomicroscopy super-resolution: a comparative study of sparse and dense approaches.

Purpose: Probe-based confocal laser endomicroscopy (pCLE) enables performing an optical biopsy via a probe. pCLE probes consist of multiple optical fibres arranged in a bundle, which taken together generate signals in an irregularly sampled pattern. Current pCLE reconstruction is based on interpolating irregular signals onto an over-sampled Cartesian grid, using a naive linear interpolation.

Pruning strategies for efficient online globally consistent mosaicking in fetoscopy.

Twin-to-twin transfusion syndrome is a condition in which identical twins share a certain pattern of vascular connections in the placenta. This leads to an imbalance in the blood flow that, if not treated, may result in a fatal outcome for both twins. To treat this condition, a surgeon explores the placenta with a fetoscope to find and photocoagulate all intertwin vascular connections.

Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction.

Purpose: Probe-based confocal laser endomicroscopy (pCLE) is a recent imaging modality that allows performing in vivo optical biopsies. The design of pCLE hardware, and its reliance on an optical fibre bundle, fundamentally limits the image quality with a few tens of thousands fibres, each acting as the equivalent of a single-pixel detector, assembled into a single fibre bundle. Video registration techniques can be used to estimate high-resolution (HR) images by exploiting the temporal information contained in a sequence of low-resolution (LR) images.

Retrieval and registration of long-range overlapping frames for scalable mosaicking of in vivo fetoscopy.

Purpose: The standard clinical treatment of Twin-to-Twin transfusion syndrome consists in the photo-coagulation of undesired anastomoses located on the placenta which are responsible to a blood transfer between the two twins. While being the standard of care procedure, fetoscopy suffers from a limited field-of-view of the placenta resulting in missed anastomoses. To facilitate the task of the clinician, building a global map of the placenta providing a larger overview of the vascular network is highly desired.

NiftyNet: a deep-learning platform for medical imaging.

Background And Objectives: Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this domain of application requires substantial implementation effort. Consequently, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups.

Probabilistic visual and electromagnetic data fusion for robust drift-free sequential mosaicking: application to fetoscopy.

The most effective treatment for twin-to-twin transfusion syndrome is laser photocoagulation of the shared vascular anastomoses in the placenta. Vascular connections are extremely challenging to locate due to their caliber and the reduced field-of-view of the fetoscope. Therefore, mosaicking techniques are beneficial to expand the scene, facilitate navigation, and allow vessel photocoagulation decision-making.

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration.

We have developed a calibration target for use with fluid-immersed endoscopes within the context of the GIFT-Surg (Guided Instrumentation for Fetal Therapy and Surgery) project. One of the aims of this project is to engineer novel, real-time image processing methods for intra-operative use in the treatment of congenital birth defects, such as spina bifida and the twin-to-twin transfusion syndrome. The developed target allows for the sterility-preserving optical distortion calibration of endoscopes within a few minutes.

GIFT-Cloud: A data sharing and collaboration platform for medical imaging research.

Objectives: Clinical imaging data are essential for developing research software for computer-aided diagnosis, treatment planning and image-guided surgery, yet existing systems are poorly suited for data sharing between healthcare and academia: research systems rarely provide an integrated approach for data exchange with clinicians; hospital systems are focused towards clinical patient care with limited access for external researchers; and safe haven environments are not well suited to algorithm development. We have established GIFT-Cloud, a data and medical image sharing platform, to meet the needs of GIFT-Surg, an international research collaboration that is developing novel imaging methods for fetal surgery. GIFT-Cloud also has general applicability to other areas of imaging research.

Acquisition models in intraoperative positron surface imaging.

Purpose: Intraoperative imaging aims at identifying residual tumor during surgery. Positron Surface Imaging (PSI) is one of the solutions to help surgeons in a better detection of resection margins of brain tumor, leading to an improved patient outcome. This system relies on a tracked freehand beta probe, using [Formula: see text]F-based radiotracer.

Detection models for freehand SPECT reconstruction.

Nuclear imaging modalities are commonly used tools in today's diagnostics and therapy planning. However for interventional use they suffer from drawbacks which limit their application. Freehand SPECT was developed to provide 3D functional imaging during interventions.

Towards intra-operative PET for head and neck cancer: lymph node localization using high-energy probes.

We present a novel approach for intra-operative localization of lymph nodes and metastases in the head and neck region using the radio-tracer [18F]FDG. By combining an optical tracking system with a high-energy gamma probe to detect 511keV annihilation gammas, we enable intra-operative PET to visualize activity distributions. Detection of these gammas is modeled ad-hoc analytically, taking into account several factors affecting the detection process.