DYNAMIC MEASUREMENT OF THREE-DIMENSIONAL MOTION FROM SINGLE-PERSPECTIVE TWO-DIMENSIONAL RADIOGRAPHIC PROJECTIONS

The digital evolution of the x-ray imaging modality has spurred the development of numerous clinical and research tools. This work focuses on the design, development, and validation of dynamic radiographic imaging and registration techniques to address two distinct medical applications: tracking during image-guided interventions, and the measurement of musculoskeletal joint kinematics. Fluoroscopy is widely employed to provide intra-procedural image-guidance. However, its planar images provide limited information about the location of surgical tools and targets in three-dimensional space. To address this limitation, registration techniques, which extract three-dimensional tracking and image-guidance information from planar images, were developed and validated in vitro. The ability to accurately measure joint kinematics in vivo is an important tool in studying both normal joint function and pathologies associated with injury and disease, however it still remains a clinical challenge. A technique to measure joint kinematics from single-perspective x-ray projections was developed and validated in vitro, using clinically available radiography equipmen

Vessel target location estimation during the TIPS procedure

Creation of a Transjugular Intrahepatic Portosystemic Shunt (TIPS) requires passage of a needle toward a moving target that is only seen transiently by x-ray prior to needle passage. Intraoperative, 3D target localization would facilitate target access and improve the safety of the procedure. The clinical assumption is that patients undergoing the TIPS procedure possess rigid, cirrhotic livers that undergo only intraoperative translation without significant deformation or rotation. Based upon this assumption, we hypothesize that the position of any unseen, 3D target point within the liver can be determined intraoperatively by precalculation of the relative positions of the target point to a different 3D point that can be tracked intraoperatively. This paper examines this hypothesis using intraoperatively acquired, biplane, x-ray images of 7 patients. In 6, we tracked the effects of cardiac and respiratory motion, and in 3 the effects of needle pressure. Methods involved reconstruction of 3D vessel bifurcation and other trackable intrahepatic points from biplane angiograms, measurement of liver deformation by examining changing distances between these 3D points over time, and comparison of expected to actual displacements of these points with respect to a fixed reference point in the liver. We conclude that, for the rigid livers associated with patients undergoing TIPS, that there is less intraoperative deformation than previously reported by other groups addressing healthy liver deformation, and that the location of an unseen target can be predicted within 3 mm accuracy

Application of engineering methodologies to address patient-specific clinical questions in congenital heart disease

The recent advances in medical imaging and in computer technologies have improved the prediction capabilities of biomechanical models. In order to replicate physiological, pathological or surgically corrected portions of the cardiovascular system, several engineering methodologies and their combinations can be adopted. Specifically, in this thesis, 3D reconstructions of patient-specific implanted devices and cardiovascular anatomies have been realised using both volumetric and biplanar visualisation methods, such as CT, MR, 4D-MR Flow and fluoroscopy. Finite Element techniques have been used to computationally deploy cardiovascular endoprosthesis, such as stents and percutaneous pulmonary valve devices, under patient-specific boundary conditions. To analyse pressure and velocity fields occurring in patient-specific vessel anatomies under patient-specific conditions, Lumped Parameter Networks and Computational Fluid Dynamics simulations have been employed. The above mentioned engineering tools have been here applied to address three clinical topics: 1 - Percutaneous pulmonary valve implantation (PPVI) Nowadays, more than 5,000 patients with pulmonary valve dysfunctions have been treated successfully with a percutaneous device, consisting in a bovine jugular venous valve sewn inside a balloon expandable stent. However, 25% of the treated patients experienced stent fracture. Using a novel methodological patient-specific approach that combines 3D reconstructions of the implanted stent from patients’ biplane fluoroscopy images and FE analyses, I carried out a risk stratification for stent fracture prediction. 2 - Transposition of the Great Arteries (TGA) Patients born with the congenital heart defect TGA need a surgical correction, which however, is associated with long term complications: the enlargement of the aortic root, and the development of a unilateral pulmonary stenosis. These may originate a complex hemodynamics that I tried to investigate by using patient-specific LPN and CFD models. 3 - Aortic Coarctation (CoA) Finally, combinations of FE and CFD-LPN models have been used to plan treatment in a patient with CoA and aberrant right subclavian

Context-aware learning for robot-assisted endovascular catheterization

Endovascular intervention has become a mainstream treatment of cardiovascular diseases. However, multiple challenges remain such as unwanted radiation exposures, limited two-dimensional image guidance, insufficient force perception and haptic cues. Fast evolving robot-assisted platforms improve the stability and accuracy of instrument manipulation. The master-slave system also removes radiation to the operator. However, the integration of robotic systems into the current surgical workflow is still debatable since repetitive, easy tasks have little value to be executed by the robotic teleoperation. Current systems offer very low autonomy, potential autonomous features could bring more benefits such as reduced cognitive workloads and human error, safer and more consistent instrument manipulation, ability to incorporate various medical imaging and sensing modalities. This research proposes frameworks for automated catheterisation with different machine learning-based algorithms, includes Learning-from-Demonstration, Reinforcement Learning, and Imitation Learning. Those frameworks focused on integrating context for tasks in the process of skill learning, hence achieving better adaptation to different situations and safer tool-tissue interactions. Furthermore, the autonomous feature was applied to next-generation, MR-safe robotic catheterisation platform. The results provide important insights into improving catheter navigation in the form of autonomous task planning, self-optimization with clinical relevant factors, and motivate the design of intelligent, intuitive, and collaborative robots under non-ionizing image modalities.Open Acces

Differential geometry methods for biomedical image processing : from segmentation to 2D/3D registration

This thesis establishes a biomedical image analysis framework for the advanced visualization of biological structures. It consists of two important parts: 1) the segmentation of some structures of interest in 3D medical scans, and 2) the registration of patient-specific 3D models with 2D interventional images. Segmenting biological structures results in 3D computational models that are simple to visualize and that can be analyzed quantitatively. Registering a 3D model with interventional images permits to position the 3D model within the physical world. By combining the information from a 3D model and 2D interventional images, the proposed framework can improve the guidance of surgical intervention by reducing the ambiguities inherent to the interpretation of 2D images. Two specific segmentation problems are considered: 1) the segmentation of large structures with low frequency intensity nonuniformity, and 2) the detection of fine curvilinear structures. First, we directed our attention toward the segmentation of relatively large structures with low frequency intensity nonuniformity. Such structures are important in medical imaging since they are commonly encountered in MRI. Also, the nonuniform diffusion of the contrast agent in some other modalities, such as CTA, leads to structures of nonuniform appearance. A level-set method that uses a local-linear region model is defined, and applied to the challenging problem of segmenting brain tissues in MRI. The unique characteristics of the proposed method permit to account for important image nonuniformity implicitly. To the best of our knowledge, this is the first time a region-based level-set model has been used to perform the segmentation of real world MRI brain scans with convincing results. The second segmentation problem considered is the detection of fine curvilinear structures in 3D medical images. Detecting those structures is crucial since they can represent veins, arteries, bronchi or other important tissues. Unfortunately, most currently available curvilinear structure detection filters incur significant signal lost at bifurcations of two structures. This peculiarity limits the performance of all subsequent processes, whether it be understanding an angiography acquisition, computing an accurate tractography, or automatically classifying the image voxels. This thesis presents a new curvilinear structure detection filter that is robust to the presence of X- and Y-junctions. At the same time, it is conceptually simple and deterministic, and allows for an intuitive representation of the structure’s principal directions. Once a 3D computational model is available, it can be used to enhance surgical guidance. A 2D/3D non-rigid method is proposed that brings a 3D centerline model of the coronary arteries into correspondence with bi-plane fluoroscopic angiograms. The registered model is overlaid on top of the interventional angiograms to provide surgical assistance during image-guided chronic total occlusion procedures, which reduces the uncertainty inherent in 2D interventional images. A fully non-rigid registration model is proposed and used to compensate for any local shape discrepancy. This method is based on a variational framework, and uses a simultaneous matching and reconstruction process. With a typical run time of less than 3 seconds, the algorithms are fast enough for interactive applications

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

Intracardiac Ultrasound Guided Systems for Transcatheter Cardiac Interventions

Transcatheter cardiac interventions are characterized by their percutaneous nature, increased patient safety, and low hospitalization times. Transcatheter procedures involve two major stages: navigation towards the target site and the positioning of tools to deliver the therapy, during which the interventionalists face the challenge of visualizing the anatomy and the relative position of the tools such as a guidewire. Fluoroscopic and transesophageal ultrasound (TEE) imaging are the most used techniques in cardiac procedures; however, they possess the disadvantage of radiation exposure and suboptimal imaging. This work explores the potential of intracardiac ultrasound (ICE) within an image guidance system (IGS) to facilitate the two stages of cardiac interventions. First, a novel 2.5D side-firing, conical Foresight ICE probe (Conavi Medical Inc., Toronto) is characterized, calibrated, and tracked using an electromagnetic sensor. The results indicate an acceptable tracking accuracy within some limitations. Next, an IGS is developed for navigating the vessels without fluoroscopy. A forward-looking, tracked ICE probe is used to reconstruct the vessel on a phantom which mimics the ultrasound imaging of an animal vena cava. Deep learning methods are employed to segment the complex vessel geometry from ICE imaging for the first time. The ICE-reconstructed vessel showed a clinically acceptable range of accuracy. Finally, a guidance system was developed to facilitate the positioning of tools during a tricuspid valve repair. The designed system potentially facilitates the positioning of the TriClip at the coaptation gap by pre-mapping the corresponding site of regurgitation in 3D tracking space

Functional and Biomechanical Effects of the Edge-to-Edge Repair in the Setting of Mitral Regurgitation: Consolidated Knowledge and Novel Tools to Gain Insight into Its Percutaneous Implementation

Mitral regurgitation is the most prevalent heart valve disease in the western population. When severe, it requires surgical treatment, repair being the preferred option. The edge-to-edge repair technique treats mitral regurgitation by suturing the leaflets together and creating a double-orifice valve. Due to its relative simplicity and versatility, it has become progressively more widespread. Recently, its percutaneous version has become feasible, and has raised interest thanks to the positive results of the Mitraclip(\uae) device. Edge-to-edge features and evolution have stimulated debate and multidisciplinary research by both clinicians and engineers. After providing an overview of representative studies in the field, here we propose a novel computational approach to the most recent percutaneous evolution of the edge-to-edge technique. Image-based structural finite element models of three mitral valves affected by posterior prolapse were derived from cine-cardiac magnetic resonance imaging. The models accounted for the patient-specific 3D geometry of the valve, including leaflet compound curvature pattern, patient-specific motion of annulus and papillary muscles, and hyperelastic and anisotropic mechanical properties of tissues. The biomechanics of the three valves throughout the entire cardiac cycle was simulated before and after Mitraclip(\uae) implantation, assessing the biomechanical impact of the procedure. For all three simulated MVs, Mitraclip(\uae) implantation significantly improved systolic leaflets coaptation, without inducing major alterations in systolic peak stresses. Diastolic orifice area was decreased, by up to 58.9%, and leaflets diastolic stresses became comparable, although lower, to systolic ones. Despite established knowledge on the edge-to-edge surgical repair, latest technological advances make its percutanoues implementation a challenging field of research. The modeling approach herein proposed may be expanded to analyze clinical scenarios that are currently critical for Mitraclip(\uae) implantation, helping the search for possible solutions