Estimating Target Vessel Location on Robot-Assisted CABG using Feature-based CT to US Registration

Although robot-assisted coronary artery bypass grafting (RA-CABG) has gained more acceptance worldwide, its success still depends on the surgeon’s experience and expertise, and the conversion rate to full sternotomy is in the order of 15%—25%. One of the reasons for conversion is poor pre-operative planning, which is based solely on pre-operative computed tomography (CT) images. This thesis proposes a technique to estimate the global peri-operative displacement of the heart and to predict the intra-operative target vessel location. The technique has been validated via both an in vitro and a clinical study, and predicted the position of the peri-operative target vessel location with ~ 3.5 mm RMS accuracy in the in vitro study while it yielded ~ 5.0 mm accuracy for the clinical validation. As the desired clinical accuracy imposed by this procedure is on the order of one intercostal space (10 - 15 mm), our technique suits the clinical requirements. It is therefore believed that this technique has the potential to improve the pre-operative planning by updating peri-operative migration patterns of the heart and, consequently, will lead to reduced conversion to conventional open thoracic procedures

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