REAL-TIME 4D ULTRASOUND RECONSTRUCTION FOR IMAGE-GUIDED INTRACARDIAC INTERVENTIONS

Image-guided therapy addresses the lack of direct vision associated with minimally- invasive interventions performed on the beating heart, but requires effective intraoperative imaging. Gated 4D ultrasound reconstruction using a tracked 2D probe generates a time-series of 3D images representing the beating heart over the cardiac cycle. These images have a relatively high spatial resolution and wide field of view, and ultrasound is easily integrated into the intraoperative environment. This thesis presents a real-time 4D ultrasound reconstruction system incorporated within an augmented reality environment for surgical guidance, whose incremental visualization reduces common acquisition errors. The resulting 4D ultrasound datasets are intended for visualization or registration to preoperative images. A human factors experiment demonstrates the advantages of real-time ultrasound reconstruction, and accuracy assessments performed both with a dynamic phantom and intraoperatively reveal RMS localization errors of 2.5-2.7 mm, and 0.8 mm, respectively. Finally, clinical applicability is demonstrated by both porcine and patient imaging

Spatial calibration of a 2D/3D ultrasound using a tracked needle

PURPOSE: Spatial calibration between a 2D/3D ultrasound and a pose tracking system requires a complex and time-consuming procedure. Simplifying this procedure without compromising the calibration accuracy is still a challenging problem. METHOD: We propose a new calibration method for both 2D and 3D ultrasound probes that involves scanning an arbitrary region of a tracked needle in different poses. This approach is easier to perform than most alternative methods that require a precise alignment between US scans and a calibration phantom. RESULTS: Our calibration method provides an average accuracy of 2.49 mm for a 2D US probe with 107 mm scanning depth, and an average accuracy of 2.39 mm for a 3D US with 107 mm scanning depth. CONCLUSION: Our method proposes a unified calibration framework for 2D and 3D probes using the same phantom object, work-flow, and algorithm. Our method significantly improves the accuracy of needle-based methods for 2D US probes as well as extends its use for 3D US probes

Foetal echocardiographic segmentation

Congenital heart disease affects just under one percentage of all live births [1]. Those defects that manifest themselves as changes to the cardiac chamber volumes are the motivation for the research presented in this thesis. Blood volume measurements in vivo require delineation of the cardiac chambers and manual tracing of foetal cardiac chambers is very time consuming and operator dependent. This thesis presents a multi region based level set snake deformable model applied in both 2D and 3D which can automatically adapt to some extent towards ultrasound noise such as attenuation, speckle and partial occlusion artefacts. The algorithm presented is named Mumford Shah Sarti Collision Detection (MSSCD). The level set methods presented in this thesis have an optional shape prior term for constraining the segmentation by a template registered to the image in the presence of shadowing and heavy noise. When applied to real data in the absence of the template the MSSCD algorithm is initialised from seed primitives placed at the centre of each cardiac chamber. The voxel statistics inside the chamber is determined before evolution. The MSSCD stops at open boundaries between two chambers as the two approaching level set fronts meet. This has significance when determining volumes for all cardiac compartments since cardiac indices assume that each chamber is treated in isolation. Comparison of the segmentation results from the implemented snakes including a previous level set method in the foetal cardiac literature show that in both 2D and 3D on both real and synthetic data, the MSSCD formulation is better suited to these types of data. All the algorithms tested in this thesis are within 2mm error to manually traced segmentation of the foetal cardiac datasets. This corresponds to less than 10% of the length of a foetal heart. In addition to comparison with manual tracings all the amorphous deformable model segmentations in this thesis are validated using a physical phantom. The volume estimation of the phantom by the MSSCD segmentation is to within 13% of the physically determined volume

Spatial calibration of a 2D/3D ultrasound using a tracked needle

PURPOSE: Spatial calibration between a 2D/3D ultrasound and a pose tracking system requires a complex and time-consuming procedure. Simplifying this procedure without compromising the calibration accuracy is still a challenging problem. METHOD: We propose a new calibration method for both 2D and 3D ultrasound probes that involves scanning an arbitrary region of a tracked needle in different poses. This approach is easier to perform than most alternative methods that require a precise alignment between US scans and a calibration phantom. RESULTS: Our calibration method provides an average accuracy of 2.49 mm for a 2D US probe with 107 mm scanning depth, and an average accuracy of 2.39 mm for a 3D US with 107 mm scanning depth. CONCLUSION: Our method proposes a unified calibration framework for 2D and 3D probes using the same phantom object, work-flow, and algorithm. Our method significantly improves the accuracy of needle-based methods for 2D US probes as well as extends its use for 3D US probes

Computer- and robot-assisted Medical Intervention

Medical robotics includes assistive devices used by the physician in order to make his/her diagnostic or therapeutic practices easier and more efficient. This chapter focuses on such systems. It introduces the general field of Computer-Assisted Medical Interventions, its aims, its different components and describes the place of robots in that context. The evolutions in terms of general design and control paradigms in the development of medical robots are presented and issues specific to that application domain are discussed. A view of existing systems, on-going developments and future trends is given. A case-study is detailed. Other types of robotic help in the medical environment (such as for assisting a handicapped person, for rehabilitation of a patient or for replacement of some damaged/suppressed limbs or organs) are out of the scope of this chapter.Comment: Handbook of Automation, Shimon Nof (Ed.) (2009) 000-00

Computational Methods for Segmentation of Multi-Modal Multi-Dimensional Cardiac Images

Segmentation of the heart structures helps compute the cardiac contractile function quantified via the systolic and diastolic volumes, ejection fraction, and myocardial mass, representing a reliable diagnostic value. Similarly, quantification of the myocardial mechanics throughout the cardiac cycle, analysis of the activation patterns in the heart via electrocardiography (ECG) signals, serve as good cardiac diagnosis indicators. Furthermore, high quality anatomical models of the heart can be used in planning and guidance of minimally invasive interventions under the assistance of image guidance. The most crucial step for the above mentioned applications is to segment the ventricles and myocardium from the acquired cardiac image data. Although the manual delineation of the heart structures is deemed as the gold-standard approach, it requires significant time and effort, and is highly susceptible to inter- and intra-observer variability. These limitations suggest a need for fast, robust, and accurate semi- or fully-automatic segmentation algorithms. However, the complex motion and anatomy of the heart, indistinct borders due to blood flow, the presence of trabeculations, intensity inhomogeneity, and various other imaging artifacts, makes the segmentation task challenging. In this work, we present and evaluate segmentation algorithms for multi-modal, multi-dimensional cardiac image datasets. Firstly, we segment the left ventricle (LV) blood-pool from a tri-plane 2D+time trans-esophageal (TEE) ultrasound acquisition using local phase based filtering and graph-cut technique, propagate the segmentation throughout the cardiac cycle using non-rigid registration-based motion extraction, and reconstruct the 3D LV geometry. Secondly, we segment the LV blood-pool and myocardium from an open-source 4D cardiac cine Magnetic Resonance Imaging (MRI) dataset by incorporating average atlas based shape constraint into the graph-cut framework and iterative segmentation refinement. The developed fast and robust framework is further extended to perform right ventricle (RV) blood-pool segmentation from a different open-source 4D cardiac cine MRI dataset. Next, we employ convolutional neural network based multi-task learning framework to segment the myocardium and regress its area, simultaneously, and show that segmentation based computation of the myocardial area is significantly better than that regressed directly from the network, while also being more interpretable. Finally, we impose a weak shape constraint via multi-task learning framework in a fully convolutional network and show improved segmentation performance for LV, RV and myocardium across healthy and pathological cases, as well as, in the challenging apical and basal slices in two open-source 4D cardiac cine MRI datasets. We demonstrate the accuracy and robustness of the proposed segmentation methods by comparing the obtained results against the provided gold-standard manual segmentations, as well as with other competing segmentation methods