153 research outputs found

    Computer-Assisted Electroanatomical Guidance for Cardiac Electrophysiology Procedures

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    Cardiac arrhythmias are serious life-threatening episodes affecting both the aging population and younger patients with pre-existing heart conditions. One of the most effective therapeutic procedures is the minimally-invasive catheter-driven endovascular electrophysiology study, whereby electrical potentials and activation patterns in the affected cardiac chambers are measured and subsequent ablation of arrhythmogenic tissue is performed. Despite emerging technologies such as electroanatomical mapping and remote intraoperative navigation systems for improved catheter manipulation and stability, successful ablation of arrhythmias is still highly-dependent on the operator’s skills and experience. This thesis proposes a framework towards standardisation in the electroanatomical mapping and ablation planning by merging knowledge transfer from previous cases and patient-specific data. In particular, contributions towards four different procedural aspects were made: optimal electroanatomical mapping, arrhythmia path computation, catheter tip stability analysis, and ablation simulation and optimisation. In order to improve the intraoperative electroanatomical map, anatomical areas of high mapping interest were proposed, as learned from previous electrophysiology studies. Subsequently, the arrhythmic wave propagation on the endocardial surface and potential ablation points were computed. The ablation planning is further enhanced, firstly by the analysis of the catheter tip stability and the probability of slippage at sparse locations on the endocardium and, secondly, by the simulation of the ablation result from the computation of convolutional matrices which model mathematically the ablation process. The methods proposed by this thesis were validated on data from patients with complex congenital heart disease, who present unusual cardiac anatomy and consequently atypical arrhythmias. The proposed methods also build a generic framework for computer guidance of electrophysiology, with results showing complementary information that can be easily integrated into the clinical workflow.Open Acces

    Cardiovascular magnetic resonance guided electrophysiology studies

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    Catheter ablation is a first line treatment for many cardiac arrhythmias and is generally performed under x-ray fluoroscopy guidance. However, current techniques for ablating complex arrhythmias such as atrial fibrillation and ventricular tachycardia are associated with suboptimal success rates and prolonged radiation exposure. Pre-procedure 3D CMR has improved understanding of the anatomic basis of complex arrhythmias and is being used for planning and guidance of ablation procedures. A particular strength of CMR compared to other imaging modalities is the ability to visualize ablation lesions. Post-procedure CMR is now being applied to assess ablation lesion location and permanence with the goal of indentifying factors leading to procedure success and failure. In the future, intra-procedure real-time CMR, together with the ability to image complex 3-D arrhythmogenic anatomy and target additional ablation to regions of incomplete lesion formation, may allow for more successful treatment of even complex arrhythmias without exposure to ionizing radiation. Development of clinical grade CMR compatible electrophysiology devices is required to transition intra-procedure CMR from pre-clinical studies to more routine use in patients

    Traversed Graph Representation for Sparse Encoding of Macro-Reentrant Tachycardia

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    © Springer International Publishing Switzerland 2016.Macro-reentrant atrial and ventricular tachycardias originate from additional circuits in which the activation of the cardiac chambers follows a high-frequency rotating pattern. The macro-reentrant circuit can be interrupted by targeted radiofrequency energy delivery with a linear lesion transecting the pathway. The choice of the optimal ablation site is determined by the operator’s experience, thus limiting the procedure success, increasing its duration and also unnecessarily extending the ablated tissue area in the case of incorrect ablation target estimation. In this paper, an algorithm for automatic intraoperative detection of the tachycardia reentry path is proposed by modelling the propagation as a graph traverse problem. Moreover, the optimal ablation point where the path should be transected is computed. Finally, the proposed method is applied to sparse electroanatomical data to demonstrate its use when undersampled mapping occurs. Thirteen electroanatomical maps of right ventricle and right and left atrium tachycardias from patients treated for congenital heart disease were analysed retrospectively in this study, with prediction accuracy tested against the recorded ablation sites and arrhythmia termination points

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

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    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

    Design, Modeling and Control of Micro-scale and Meso-scale Tendon-Driven Surgical Robots

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    Manual manipulation of passive surgical tools is time consuming with uncertain results in cases of navigating tortuous anatomy, avoiding critical anatomical landmarks, and reaching targets not located in the linear range of these tools. For example, in many cardiovascular procedures, manual navigation of a micro-scale passive guidewire results in increased procedure times and radiation exposure. This thesis introduces the design of two steerable guidewires: 1) A two degree-of-freedom (2-DoF) robotic guidewire with orthogonally oriented joints to access points in a three dimensional workspace, and 2) a micro-scale coaxially aligned steerable (COAST) guidewire robot that demonstrates variable and independently controlled bending length and curvature of the distal end. The 2-DoF guidewire features two micromachined joints from a tube of superelastic nitinol of outer diameter 0.78 mm. Each joint is actuated with two nitinol tendons. The joints that are used in this robot are called bidirectional asymmetric notch (BAN) joints, and the advantages of these joints are explored and analyzed. The design of the COAST robotic guidewire involves three coaxially aligned tubes with a single tendon running centrally through the length of the robot. The outer tubes are made from micromachined nitinol allowing for tendon-driven bending of the robot at variable bending curvatures, while an inner stainless steel tube controls the bending length of the robot. By varying the lengths of the tubes as well as the tendon, and by insertion and retraction of the entire assembly, various joint lengths and curvatures may be achieved. Kinematic and static models, a compact actuation system, and a controller for this robot are presented. The capability of the robot to accurately navigate through phantom anatomical bifurcations and tortuous angles is also demonstrated in three dimensional phantom vasculature. At the meso-scale, manual navigation of passive pediatric neuroendoscopes for endoscopic third ventriculostomy may not reach target locations in the patient's ventricle. This work introduces the design, analysis and control of a meso-scale two degree-of-freedom robotic bipolar electrocautery tool that increases the workspace of the neurosurgeon. A static model is proposed for the robot joints that avoids problems arising from pure kinematic control. Using this model, a control system is developed that comprises of a disturbance observer to provide precise force control and compensate for joint hysteresis. A handheld controller is developed and demonstrated in this thesis. To allow the clinician to estimate the shape of the steerable tools within the anatomy for both micro-scale and meso-scale tools, a miniature tendon force sensor and a high deflection shape sensor are proposed and demonstrated. The force sensor features a compact design consisting of a single LED, dual-phototransistor, and a dual-screen arrangement to increase the linear range of sensor output and compensate for external disturbances, thereby allowing force measurement of up to 21 N with 99.58 % accuracy. The shape sensor uses fiber Bragg grating based optical cable mounted on a micromachined tube and is capable of measuring curvatures as high as 145 /m. These sensors were incorporated and tested in the guidewire and the neuroendoscope tool robots and can provide robust feedback for closed-loop control of these devices in the future.Ph.D

    Cardiac Activation Mapping using Ultrasound Current Source Density Imaging.

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    Intracardiac ablation procedures to correct drug-resistant arrhythmias require accurate maps of cardiac activation. Conventional methods are time-consuming and have poor spatial resolution (5- 10 mm). The goal of this dissertation was to develop a new method, Ultrasound Current Source Density Imaging (UCSDI), to map biological currents. UCSDI is based on the acousto-electric (AE) effect, a modulation of the electric resistivity by acoustic pressure. If a current passes through the focal region of an ultrasound transducer, a voltage modulated at the ultrasonic frequency can be measured with a pair of electrodes located distal to the focal zone. By sweeping the focal zone, UCSDI can map a distributed current field. UCSDI has several potential advantages as a technique for mapping cardiac activation currents: high spatial resolution determined by the typically sub-mm focal characteristics of the ultrasound beam, short mapping time using electronically steered ultrasonic beams, and automatic registration with B-mode ultrasound images without sophisticated mathematical algorithms. UCSDI was first validated by mapping an artificially generated 2D current distribution. It was compared to sequential electrode mapping, computer simulation as well as to an inverse algorithm. In this study it was possible to use UCSDI to locate monopolar current sources to within 1-mm of their true locations without making any prior assumptions about the source geometry. UCSDI was then used to detect and map biological currents in an isolated rabbit heart. Both UCSDI and normal low frequency electrocardiograms (ECG) were measured simultaneously by tungsten electrodes embedded in the left ventricle. The motion of the heart was significantly reduced by perfusing it with an excitation contraction de-coupler. Measured UCSDI maps showed temporal and spatial patterns consistent with a spreading activation wave and timing consistent with normal ECG signals. UCSDI was then combined with ultrasonic strain imaging in a new method for electromechanical imaging. This combined method was used to make localized measurements of electromechanical delay. This method could be useful in cardiac resynchronization therapy for placing pacemaker leads.Ph.D.Biomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60687/1/rolafsso_1.pd

    A Platform for Robot-Assisted Intracardiac Catheter Navigation

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    Steerable catheters are routinely deployed in the treatment of cardiac arrhythmias. During invasive electrophysiology studies, the catheter handle is manipulated by an interventionalist to guide the catheter's distal section toward endocardium for pacing and ablation. Catheter manipulation requires dexterity and experience, and exposes the interventionalist to ionizing radiation. Through the course of this research, a platform was developed to assist and enhance the navigation of the catheter inside the cardiac chambers. This robotic platform replaces the interventionalist's hand in catheter manipulation and provides the option to force the catheter tip in arbitrary directions using a 3D input device or to automatically navigate the catheter to desired positions within a cardiac chamber by commanding the software to do so. To accomplish catheter navigation, the catheter was modeled as a continuum manipulator, and utilizing robot kinematics, catheter tip position control was designed and implemented. An electromagnetic tracking system was utilized to measure the position and orientation of two key points in catheter model, for position feedback to the control system. A software platform was developed to implement the navigation and control strategies and to interface with the robot, the 3D input device and the tracking system. The catheter modeling was validated through in-vitro experiments with a static phantom, and in-vivo experiments on three live swines. The feasibility of automatic navigation was also veri ed by navigating to three landmarks in the beating heart of swine subjects, and comparing their performance with that of an experienced interventionalist using quasi biplane fluoroscopy. The platform realizes automatic, assisted, and motorized navigation under the interventionalist's control, thus reducing the dependence of successful navigation on the dexterity and manipulation skills of the interventionalist, and providing a means to reduce the exposure to X-ray radiation. Upon further development, the platform could be adopted for human deployment

    Multiscale Cohort Modeling of Atrial Electrophysiology : Risk Stratification for Atrial Fibrillation through Machine Learning on Electrocardiograms

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    Patienten mit Vorhofflimmern sind einem fünffach erhöhten Risiko für einen ischämischen Schlaganfall ausgesetzt. Eine frühzeitige Erkennung und Diagnose der Arrhythmie würde ein rechtzeitiges Eingreifen ermöglichen, um möglicherweise auftretende Begleiterkrankungen zu verhindern. Eine Vergrößerung des linken Vorhofs sowie fibrotisches Vorhofgewebe sind Risikomarker für Vorhofflimmern, da sie die notwendigen Voraussetzungen für die Aufrechterhaltung der chaotischen elektrischen Depolarisation im Vorhof erfüllen. Mithilfe von Techniken des maschinellen Lernens könnten Fibrose und eine Vergrößerung des linken Vorhofs basierend auf P Wellen des 12-Kanal Elektrokardiogramms im Sinusrhythmus automatisiert identifiziert werden. Dies könnte die Basis für eine nicht-invasive Risikostrat- ifizierung neu auftretender Vorhofflimmerepisoden bilden, um anfällige Patienten für ein präventives Screening auszuwählen. Zu diesem Zweck wurde untersucht, ob simulierte Vorhof-Elektrokardiogrammdaten, die dem klinischen Trainingssatz eines maschinellen Lernmodells hinzugefügt wurden, zu einer verbesserten Klassifizierung der oben genannten Krankheiten bei klinischen Daten beitra- gen könnten. Zwei virtuelle Kohorten, die durch anatomische und funktionelle Variabilität gekennzeichnet sind, wurden generiert und dienten als Grundlage für die Simulation großer P Wellen-Datensätze mit genau bestimmbaren Annotationen der zugrunde liegenden Patholo- gie. Auf diese Weise erfüllen die simulierten Daten die notwendigen Voraussetzungen für die Entwicklung eines Algorithmus für maschinelles Lernen, was sie von klinischen Daten unterscheidet, die normalerweise nicht in großer Zahl und in gleichmäßig verteilten Klassen vorliegen und deren Annotationen möglicherweise durch unzureichende Expertenannotierung beeinträchtigt sind. Für die Schätzung des Volumenanteils von linksatrialem fibrotischen Gewebe wurde ein merkmalsbasiertes neuronales Netz entwickelt. Im Vergleich zum Training des Modells mit nur klinischen Daten, führte das Training mit einem hybriden Datensatz zu einer Reduzierung des Fehlers von durchschnittlich 17,5 % fibrotischem Volumen auf 16,5 %, ausgewertet auf einem rein klinischen Testsatz. Ein Long Short-Term Memory Netzwerk, das für die Unterscheidung zwischen gesunden und P Wellen von vergrößerten linken Vorhöfen entwickelt wurde, lieferte eine Genauigkeit von 0,95 wenn es auf einem hybriden Datensatz trainiert wurde, von 0,91 wenn es nur auf klinischen Daten trainiert wurde, die alle mit 100 % Sicherheit annotiert wurden, und von 0,83 wenn es auf einem klinischen Datensatz trainiert wurde, der alle Signale unabhängig von der Sicherheit der Expertenannotation enthielt. In Anbetracht der Ergebnisse dieser Arbeit können Elektrokardiogrammdaten, die aus elektrophysiologischer Modellierung und Simulationen an virtuellen Patientenkohorten resul- tieren und relevante Variabilitätsaspekte abdecken, die mit realen Beobachtungen übereinstim- men, eine wertvolle Datenquelle zur Verbesserung der automatisierten Risikostratifizierung von Vorhofflimmern sein. Auf diese Weise kann den Nachteilen klinischer Datensätze für die Entwicklung von Modellen des maschinellen Lernens entgegengewirkt werden. Dies trägt letztendlich zu einer frühzeitigen Erkennung der Arrhythmie bei, was eine rechtzeitige Auswahl geeigneter Behandlungsstrategien ermöglicht und somit das Schlaganfallrisiko der betroffenen Patienten verringert
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