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

3D Reconstruction of Interventional Material from Very Few X-Ray Projections for Interventional Image Guidance

Today, minimally invasive endovascular interventions are usually guided by 2D fluoroscopy, i.e. a live 2D X-ray image. However, 3D fluoroscopy, i.e. a live 3D image reconstructed from a stream of 2D X-ray images, could improve spatial awareness. 3D fluoroscopy is, however, not used today, since no appropriate 3D reconstruction algorithm is known. Existing algorithms for the real-time reconstruction of interventional material (guidewires, stents, catheters, etc.) are either only capable of reconstructing a single guidewire or catheter, or use too many X-ray images and therefore too much dose per 3D reconstruction. The goal of this thesis was to reconstruct complex arrangements of interventional material from as few X-ray images as possible. To this end, a previously proposed algorithm for the reconstruction of interventional material from four X-ray images was adapted. Five key improvements allowed to reduce the number of X-ray images per 3D reconstruction from four to two: a) use of temporal information in a rotating imaging setup, b) separate reconstruction of different types of interventional material enabled by the computation of semantic interventional material extraction images, c) compensation of stent motion by spatial transformer networks, d) per-projection backprojection and e) binarization of the guidewire extraction images. While previously only single curves could be reconstructed from two newly acquired X-ray images, the proposed pipeline can reconstruct stents and even stent-guidewire combinations. Submillimeter reconstruction accuracy was demonstrated on measured X-ray images of interventional material inside an anthropomorphic phantom with simulated respiratory motion. Measurements of the dose area product rate of the proposed 3D reconstruction pipeline indicate a dose burden roughly similar to that of 2D fluoroscopy

Constrained Stochastic State Estimation for 3D Shape Reconstruction of Catheters and Guidewires in Fluoroscopic Images

Minimally invasive fluoroscopy-based procedures are the gold standard for diagnosis and treatment of various pathologies of the cardiovascular system. This kind of procedures imply for the clinicians to infer the 3D shape of the device from 2D images, which is known to be an ill-posed problem. In this paper we present a method to reconstruct the 3D shape of the interventional device, with the aim of improving the navigation. The method combines a physics-based simulation with non-linear Bayesian filter. Whereas the physics-based model provides a prediction of the shape of the device navigating within the blood vessels (taking into account non-linear interactions between the catheter and the surrounding anatomy), an Unscented Kalman Filter is used to correct the navigation model using 2D image features as external observations. The proposed framework has been evaluated on both synthetic and real data, under different model parameterization, filter parameters tuning and external observations data-sets. Comparing the reconstructed 3D shape with a known ground truth, for the synthetic data-set, we obtained an average 3D Hausdorff distance of 0.07 ± 0.37 mm; the 3D distance at the tip equal to 0.021 ± 0.009 mm and the 3D mean distance at the distal segment of the catheter equal to 0.02 ± 0.008 mm. For the real data-set, the obtained average 3D Hausdorff Distance was of 0.95 ± 0.35 mm, the average 3D distance at the tip is equal to 0.7 ± 0.45 mm with an average 3D mean distance at the distal segment of 0.7 ± 0.46 mm. These results show the ability of our method to retrieve the 3D shape of the device, under a variety of filter parameterizations and challenging conditions: errors on the friction coefficient, ambiguous views and non-linear complex phenomena such as stick and slip motions

Constrained Stochastic State Estimation of Deformable 1D Objects: Application to Single-view 3D Reconstruction of Catheters with Radio-opaque Markers

International audienceMinimally invasive fluoroscopy-based procedures are the gold standard for diagnosis and treatment of various pathologies of the cardiovascular system. This kind of procedures imply for the clinicians to infer the 3D shape of the device from 2D images, which is known to be an ill-posed 10 problem. In this paper we present a method to reconstruct the 3D shape of the interventional device, with the aim of improving the navigation. The method combines a physics-based simulation with non-linear Bayesian filter. Whereas the physics-based model provides a prediction of the shape of the device navigating within the blood vessels (taking into account non-linear interactions be-15 tween the catheter and the surrounding anatomy), an Unscented Kalman Filter is used to correct the navigation model using 2D image features as external observations. The proposed framework has been evaluated on both synthetic and real data, under different model parameterizations, filter parameters tuning and external observations data-sets. Comparing the reconstructed 3D shape with a known ground truth, for the synthetic data-set, we obtained average values for 3D Hausdorff Distance of $0.81 ± 0.53 mm$, for the 3D mean distance at the segment of $0.37 ± 0.17$ mm and an average 3D tip error of $0.24 ± 0.13 mm$. For the real data-set,we obtained an average 3D Hausdorff distance of $1.74 ± 0.77 mm$, a average 3D mean distance at the distal segment of 0.91 ± 0.14 mm, an average 3D error on the tip of $0.53 ± 0.09 mm$. These results show the ability of our method to retrieve the 3D shape of the device, under a variety of filter parameterizations and challenging conditions: uncertainties on model parameterization, ambiguous views and non-linear complex phenomena such as stick and slip motions

Catheter Localization Utilizing a Sensor-Enabled Guidewire: Design of a Proof-of-Concept System

The purpose of this thesis project was to develop a proof-of-concept system for tracking the tip of a catheter without an embedded electromagnetic sensor by utilizing a sensor enabled guidewire. The motivation for the project was a reduction fluoroscopy radiation dose for clinicians in the interventional cardiology lab and the extension of navigation technology to be used with a wider variety of interventional devices through the implementation of expanded capabilities of the Abbott MediGuide system. The focus of the project was on the development of a proof-of-concept system capable of using an external device to track relative guidewire and catheter motion and apply that to a calculated position in the vasculature. The research conducted covered multiple disciplines from mechanical design to software algorithms. A prototype system was developed that functions alongside the MediGuide system to provide a three dimensional depiction of catheter location and a measurement of the relative linear displacement separating the distal tip of the guidewire and the distal tip of the catheter. The system consists of an electromechanical device to measure relative motion and software to communicate with the device, interpret recorded guidewire position data into a representative trajectory, and display the results to the user. The hardware and software components of the project were evaluated to determine accuracy and precision. The prototype device was determined to be accurate to 0.7±0.03% of total displacement. In a simulated use procedure the device was determined to be accurate to 1.4±0.53mm. The software algorithms to generate a simulated guidewire path were evaluated and tuned to generate the best response to the data sets available. In summary, the work performed here shows the possibility of implementing a device and software system that can provide localization information to the operator about the catheters used in an interventional procedure without the need for a sensor in the catheter

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

Constrained Stochastic State Estimation of Deformable 1D Objects: Application to Single-view 3D Reconstruction of Catheters with Radio-opaque Markers

International audienceMinimally invasive fluoroscopy-based procedures are the gold standard for diagnosis and treatment of various pathologies of the cardiovascular system. This kind of procedures imply for the clinicians to infer the 3D shape of the device from 2D images, which is known to be an ill-posed 10 problem. In this paper we present a method to reconstruct the 3D shape of the interventional device, with the aim of improving the navigation. The method combines a physics-based simulation with non-linear Bayesian filter. Whereas the physics-based model provides a prediction of the shape of the device navigating within the blood vessels (taking into account non-linear interactions be-15 tween the catheter and the surrounding anatomy), an Unscented Kalman Filter is used to correct the navigation model using 2D image features as external observations. The proposed framework has been evaluated on both synthetic and real data, under different model parameterizations, filter parameters tuning and external observations data-sets. Comparing the reconstructed 3D shape with a known ground truth, for the synthetic data-set, we obtained average values for 3D Hausdorff Distance of $0.81 ± 0.53 mm$, for the 3D mean distance at the segment of $0.37 ± 0.17$ mm and an average 3D tip error of $0.24 ± 0.13 mm$. For the real data-set,we obtained an average 3D Hausdorff distance of $1.74 ± 0.77 mm$, a average 3D mean distance at the distal segment of 0.91 ± 0.14 mm, an average 3D error on the tip of $0.53 ± 0.09 mm$. These results show the ability of our method to retrieve the 3D shape of the device, under a variety of filter parameterizations and challenging conditions: uncertainties on model parameterization, ambiguous views and non-linear complex phenomena such as stick and slip motions

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