Robust semi-automated path extraction for visualising stenosis of the coronary arteries

Computed tomography angiography (CTA) is useful for diagnosing and planning treatment of heart disease. However, contrast agent in surrounding structures (such as the aorta and left ventricle) makes 3-D visualisation of the coronary arteries difficult. This paper presents a composite method employing segmentation and volume rendering to overcome this issue. A key contribution is a novel Fast Marching minimal path cost function for vessel centreline extraction. The resultant centreline is used to compute a measure of vessel lumen, which indicates the degree of stenosis (narrowing of a vessel). Two volume visualisation techniques are presented which utilise the segmented arteries and lumen measure. The system is evaluated and demonstrated using synthetic and clinically obtained datasets

Autonomous Tissue Scanning under Free-Form Motion for Intraoperative Tissue Characterisation

In Minimally Invasive Surgery (MIS), tissue scanning with imaging probes is required for subsurface visualisation to characterise the state of the tissue. However, scanning of large tissue surfaces in the presence of deformation is a challenging task for the surgeon. Recently, robot-assisted local tissue scanning has been investigated for motion stabilisation of imaging probes to facilitate the capturing of good quality images and reduce the surgeon's cognitive load. Nonetheless, these approaches require the tissue surface to be static or deform with periodic motion. To eliminate these assumptions, we propose a visual servoing framework for autonomous tissue scanning, able to deal with free-form tissue deformation. The 3D structure of the surgical scene is recovered and a feature-based method is proposed to estimate the motion of the tissue in real-time. A desired scanning trajectory is manually defined on a reference frame and continuously updated using projective geometry to follow the tissue motion and control the movement of the robotic arm. The advantage of the proposed method is that it does not require the learning of the tissue motion prior to scanning and can deal with free-form deformation. We deployed this framework on the da Vinci surgical robot using the da Vinci Research Kit (dVRK) for Ultrasound tissue scanning. Since the framework does not rely on information from the Ultrasound data, it can be easily extended to other probe-based imaging modalities.Comment: 7 pages, 5 figures, ICRA 202

3D segmentation of the tracheobronchial tree using multiscale morphology enhancement filter

In this article we present a new region growing algorithm for airway segmentation based on multiscale black tophat enhancement filter. Lung airways are tubular structures that display specific characteristics, such as highly variable intensity levels within the lumen and proximity to vessels. The proposed airways enhancement filter aims to separate airways from adjacent lung parenchyma and vessel. Based on the filter ouput, the region growing is performed in order to delineate the airways and then to reconstruct the tracheobronchial tree. The proposed method has been applied on various CT scans. In this paper, an experimental comparison study between our filter and the "gold standard" filters used to enhance tubular structures (Frangi, Sato and Krissian filters) followed by a region growing process is performed on data from the VESSEL12 challenge framework. Our approach outperforms the other considered methods in terms of retrieved bronchi and computing time

Artificial neural network-statistical approach for PET volume analysis and classification

Copyright © 2012 The Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.This article has been made available through the Brunel Open Access Publishing Fund.The increasing number of imaging studies and the prevailing application of positron emission tomography (PET) in clinical oncology have led to a real need for efficient PET volume handling and the development of new volume analysis approaches to aid the clinicians in the clinical diagnosis, planning of treatment, and assessment of response to therapy. A novel automated system for oncological PET volume analysis is proposed in this work. The proposed intelligent system deploys two types of artificial neural networks (ANNs) for classifying PET volumes. The first methodology is a competitive neural network (CNN), whereas the second one is based on learning vector quantisation neural network (LVQNN). Furthermore, Bayesian information criterion (BIC) is used in this system to assess the optimal number of classes for each PET data set and assist the ANN blocks to achieve accurate analysis by providing the best number of classes. The system evaluation was carried out using experimental phantom studies (NEMA IEC image quality body phantom), simulated PET studies using the Zubal phantom, and clinical studies representative of nonsmall cell lung cancer and pharyngolaryngeal squamous cell carcinoma. The proposed analysis methodology of clinical oncological PET data has shown promising results and can successfully classify and quantify malignant lesions.This study was supported by the Swiss National Science Foundation under Grant SNSF 31003A-125246, Geneva Cancer League, and the Indo Swiss Joint Research Programme ISJRP 138866. This article is made available through the Brunel Open Access Publishing Fund

Coronary Artery Segmentation and Motion Modelling

Conventional coronary artery bypass surgery requires invasive sternotomy and the use of a cardiopulmonary bypass, which leads to long recovery period and has high infectious potential. Totally endoscopic coronary artery bypass (TECAB) surgery based on image guided robotic surgical approaches have been developed to allow the clinicians to conduct the bypass surgery off-pump with only three pin holes incisions in the chest cavity, through which two robotic arms and one stereo endoscopic camera are inserted. However, the restricted field of view of the stereo endoscopic images leads to possible vessel misidentification and coronary artery mis-localization. This results in 20-30% conversion rates from TECAB surgery to the conventional approach. We have constructed patient-specific 3D + time coronary artery and left ventricle motion models from preoperative 4D Computed Tomography Angiography (CTA) scans. Through temporally and spatially aligning this model with the intraoperative endoscopic views of the patient's beating heart, this work assists the surgeon to identify and locate the correct coronaries during the TECAB precedures. Thus this work has the prospect of reducing the conversion rate from TECAB to conventional coronary bypass procedures. This thesis mainly focus on designing segmentation and motion tracking methods of the coronary arteries in order to build pre-operative patient-specific motion models. Various vessel centreline extraction and lumen segmentation algorithms are presented, including intensity based approaches, geometric model matching method and morphology-based method. A probabilistic atlas of the coronary arteries is formed from a group of subjects to facilitate the vascular segmentation and registration procedures. Non-rigid registration framework based on a free-form deformation model and multi-level multi-channel large deformation diffeomorphic metric mapping are proposed to track the coronary motion. The methods are applied to 4D CTA images acquired from various groups of patients and quantitatively evaluated

X-ray computer tomography based numerical modelling of fibre reinforced composites

Non-crimp fabric reinforced polymers are commonly used to manufacture the load carrying parts in wind turbine blades. Since wind turbine blades have a large material usage, the favourable stiffness to price ratio of non-crimp fabric reinforced polymers is highly attractive for manufactures. Additionally, they are easy to manufacture, which is essential for mould sizes of up to approximately 100 m. Smaller turbine blades up to 75 m use glass fibres, lager blades require carbon fibres to meet the stiffness requirements.\ua0Wind turbine blades are ever increasing in length since the generated power is proportional to the length squared. In addition to the challenge to reduce the material usage, longer blades demand higher stiffness. Furthermore, wind turbines are one of the man-made structures that have to endure the highest numbers of load cycles. Even though wind turbine blades are mainly loaded in tension there are compressive loads present on the leeward side of the blade. Those three main material requirements demand highly tailored high-performance materials. At the same time wind turbine manufactures are under a high cost pressure as governments all over the world are cutting subsidies. As for any other high-performance material a constant production quality is essential. However, in particular composites are susceptible for manufacture flaws.\ua0X-ray computer tomography allows for the detection of some of the defects present after manufacture. X-ray computer tomography is a very promising tool for materials quality control and quantification when combined with numerical modelling. In the last years the image acquisition and analysis process has seen enormous progress that can now be exploited.\ua0In this research project the X-ray computer tomography aided engineering (XAE) process has been established. XAE systemically combines all work-steps from material image acquisition to the final finite element analysis results. The process provides an automated, accurate and fast image analysis and an element-wise and integration point-wise material orientation mapping. The analysis of the detailed stress and strain distributions after manufacture with XAE will allow for more reliable and low-cost wind turbine blades