Dynamic Analysis of X-ray Angiography for Image-Guided Coronary Interventions

Percutaneous coronary intervention (PCI) is a minimally-invasive procedure for treating patients with coronary artery disease. PCI is typically performed with image guidance using X-ray angiograms (XA) in which coronary arter

Development of a Surgical Assistance System for Guiding Transcatheter Aortic Valve Implantation

Development of image-guided interventional systems is growing up rapidly in the recent years. These new systems become an essential part of the modern minimally invasive surgical procedures, especially for the cardiac surgery. Transcatheter aortic valve implantation (TAVI) is a recently developed surgical technique to treat severe aortic valve stenosis in elderly and high-risk patients. The placement of stented aortic valve prosthesis is crucial and typically performed under live 2D fluoroscopy guidance. To assist the placement of the prosthesis during the surgical procedure, a new fluoroscopy-based TAVI assistance system has been developed. The developed assistance system integrates a 3D geometrical aortic mesh model and anatomical valve landmarks with live 2D fluoroscopic images. The 3D aortic mesh model and landmarks are reconstructed from interventional angiographic and fluoroscopic C-arm CT system, and a target area of valve implantation is automatically estimated using these aortic mesh models. Based on template-based tracking approach, the overlay of visualized 3D aortic mesh model, landmarks and target area of implantation onto fluoroscopic images is updated by approximating the aortic root motion from a pigtail catheter motion without contrast agent. A rigid intensity-based registration method is also used to track continuously the aortic root motion in the presence of contrast agent. Moreover, the aortic valve prosthesis is tracked in fluoroscopic images to guide the surgeon to perform the appropriate placement of prosthesis into the estimated target area of implantation. An interactive graphical user interface for the surgeon is developed to initialize the system algorithms, control the visualization view of the guidance results, and correct manually overlay errors if needed. Retrospective experiments were carried out on several patient datasets from the clinical routine of the TAVI in a hybrid operating room. The maximum displacement errors were small for both the dynamic overlay of aortic mesh models and tracking the prosthesis, and within the clinically accepted ranges. High success rates of the developed assistance system were obtained for all tested patient datasets. The results show that the developed surgical assistance system provides a helpful tool for the surgeon by automatically defining the desired placement position of the prosthesis during the surgical procedure of the TAVI.Die Entwicklung bildgeführter interventioneller Systeme wächst rasant in den letzten Jahren. Diese neuen Systeme werden zunehmend ein wesentlicher Bestandteil der technischen Ausstattung bei modernen minimal-invasiven chirurgischen Eingriffen. Diese Entwicklung gilt besonders für die Herzchirurgie. Transkatheter Aortenklappen-Implantation (TAKI) ist eine neue entwickelte Operationstechnik zur Behandlung der schweren Aortenklappen-Stenose bei alten und Hochrisiko-Patienten. Die Platzierung der Aortenklappenprothese ist entscheidend und wird in der Regel unter live-2D-fluoroskopischen Bildgebung durchgeführt. Zur Unterstützung der Platzierung der Prothese während des chirurgischen Eingriffs wurde in dieser Arbeit ein neues Fluoroskopie-basiertes TAKI Assistenzsystem entwickelt. Das entwickelte Assistenzsystem überlagert eine 3D-Geometrie des Aorten-Netzmodells und anatomischen Landmarken auf live-2D-fluoroskopische Bilder. Das 3D-Aorten-Netzmodell und die Landmarken werden auf Basis der interventionellen Angiographie und Fluoroskopie mittels eines C-Arm-CT-Systems rekonstruiert. Unter Verwendung dieser Aorten-Netzmodelle wird das Zielgebiet der Klappen-Implantation automatisch geschätzt. Mit Hilfe eines auf Template Matching basierenden Tracking-Ansatzes wird die Überlagerung des visualisierten 3D-Aorten-Netzmodells, der berechneten Landmarken und der Zielbereich der Implantation auf fluoroskopischen Bildern korrekt überlagert. Eine kompensation der Aortenwurzelbewegung erfolgt durch Bewegungsverfolgung eines Pigtail-Katheters in Bildsequenzen ohne Kontrastmittel. Eine starrere Intensitätsbasierte Registrierungsmethode wurde verwendet, um kontinuierlich die Aortenwurzelbewegung in Bildsequenzen mit Kontrastmittelgabe zu detektieren. Die Aortenklappenprothese wird in die fluoroskopischen Bilder eingeblendet und dient dem Chirurg als Leitfaden für die richtige Platzierung der realen Prothese. Eine interaktive Benutzerschnittstelle für den Chirurg wurde zur Initialisierung der Systemsalgorithmen, zur Steuerung der Visualisierung und für manuelle Korrektur eventueller Überlagerungsfehler entwickelt. Retrospektive Experimente wurden an mehreren Patienten-Datensätze aus der klinischen Routine der TAKI in einem Hybrid-OP durchgeführt. Hohe Erfolgsraten des entwickelten Assistenzsystems wurden für alle getesteten Patienten-Datensätze erzielt. Die Ergebnisse zeigen, dass das entwickelte chirurgische Assistenzsystem ein hilfreiches Werkzeug für den Chirurg bei der Platzierung Position der Prothese während des chirurgischen Eingriffs der TAKI bietet

Fast catheter segmentation and tracking based on x-ray fluoroscopic and echocardiographic modalities for catheter-based cardiac minimally invasive interventions

X-ray fluoroscopy and echocardiography imaging (ultrasound, US) are two imaging modalities that are widely used in cardiac catheterization. For these modalities, a fast, accurate and stable algorithm for the detection and tracking of catheters is required to allow clinicians to observe the catheter location in real-time. Currently X-ray fluoroscopy is routinely used as the standard modality in catheter ablation interventions. However, it lacks the ability to visualize soft tissue and uses harmful radiation. US does not have these limitations but often contains acoustic artifacts and has a small field of view. These make the detection and tracking of the catheter in US very challenging. The first contribution in this thesis is a framework which combines Kalman filter and discrete optimization for multiple catheter segmentation and tracking in X-ray images. Kalman filter is used to identify the whole catheter from a single point detected on the catheter in the first frame of a sequence of x-ray images. An energy-based formulation is developed that can be used to track the catheters in the following frames. We also propose a discrete optimization for minimizing the energy function in each frame of the X-ray image sequence. Our approach is robust to tangential motion of the catheter and combines the tubular and salient feature measurements into a single robust and efficient framework. The second contribution is an algorithm for catheter extraction in 3D ultrasound images based on (a) the registration between the X-ray and ultrasound images and (b) the segmentation of the catheter in X-ray images. The search space for the catheter extraction in the ultrasound images is constrained to lie on or close to a curved surface in the ultrasound volume. The curved surface corresponds to the back-projection of the extracted catheter from the X-ray image to the ultrasound volume. Blob-like features are detected in the US images and organized in a graphical model. The extracted catheter is modelled as the optimal path in this graphical model. Both contributions allow the use of ultrasound imaging for the improved visualization of soft tissue. However, X-ray imaging is still required for each ultrasound frame and the amount of X-ray exposure has not been reduced. The final contribution in this thesis is a system that can track the catheter in ultrasound volumes automatically without the need for X-ray imaging during the tracking. Instead X-ray imaging is only required for the system initialization and for recovery from tracking failures. This allows a significant reduction in the amount of X-ray exposure for patient and clinicians.Open Acces

Medical Robotics

The first generation of surgical robots are already being installed in a number of operating rooms around the world. Robotics is being introduced to medicine because it allows for unprecedented control and precision of surgical instruments in minimally invasive procedures. So far, robots have been used to position an endoscope, perform gallbladder surgery and correct gastroesophogeal reflux and heartburn. The ultimate goal of the robotic surgery field is to design a robot that can be used to perform closed-chest, beating-heart surgery. The use of robotics in surgery will expand over the next decades without any doubt. Minimally Invasive Surgery (MIS) is a revolutionary approach in surgery. In MIS, the operation is performed with instruments and viewing equipment inserted into the body through small incisions created by the surgeon, in contrast to open surgery with large incisions. This minimizes surgical trauma and damage to healthy tissue, resulting in shorter patient recovery time. The aim of this book is to provide an overview of the state-of-art, to present new ideas, original results and practical experiences in this expanding area. Nevertheless, many chapters in the book concern advanced research on this growing area. The book provides critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies. This book is certainly a small sample of the research activity on Medical Robotics going on around the globe as you read it, but it surely covers a good deal of what has been done in the field recently, and as such it works as a valuable source for researchers interested in the involved subjects, whether they are currently “medical roboticists” or not

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

Combinatorial optimisation for arterial image segmentation.

Cardiovascular disease is one of the leading causes of the mortality in the western world. Many imaging modalities have been used to diagnose cardiovascular diseases. However, each has different forms of noise and artifacts that make the medical image analysis field important and challenging. This thesis is concerned with developing fully automatic segmentation methods for cross-sectional coronary arterial imaging in particular, intra-vascular ultrasound and optical coherence tomography, by incorporating prior and tracking information without any user intervention, to effectively overcome various image artifacts and occlusions. Combinatorial optimisation methods are proposed to solve the segmentation problem in polynomial time. A node-weighted directed graph is constructed so that the vessel border delineation is considered as computing a minimum closed set. A set of complementary edge and texture features is extracted. Single and double interface segmentation methods are introduced. Novel optimisation of the boundary energy function is proposed based on a supervised classification method. Shape prior model is incorporated into the segmentation framework based on global and local information through the energy function design and graph construction. A combination of cross-sectional segmentation and longitudinal tracking is proposed using the Kalman filter and the hidden Markov model. The border is parameterised using the radial basis functions. The Kalman filter is used to adapt the inter-frame constraints between every two consecutive frames to obtain coherent temporal segmentation. An HMM-based border tracking method is also proposed in which the emission probability is derived from both the classification-based cost function and the shape prior model. The optimal sequence of the hidden states is computed using the Viterbi algorithm. Both qualitative and quantitative results on thousands of images show superior performance of the proposed methods compared to a number of state-of-the-art segmentation methods

Imaging Sensors and Applications

In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome