Multi-center trials provide the unique ability to investigate novel techniques across a range of geographical sites with sufficient statistical power, the inclusion of multiple operators determining feasibility under a wider array of clinical environments and work-flows. For this purpose, we introduce a new means of distributing pre-procedural cardiac models for image-guided interventions across a large scale multi-center trial. In this method, a single core facility is responsible for image processing, employing a novel web-based interface for model visualization and distribution. The requirements for such an interface, being WebGL-based, are minimal and well within the realms of accessibility for participating centers. We then demonstrate the accuracy of our approach using a single-center pacemaker lead implantation trial with generic planning models.
Compared to conventional open-heart surgeries, minimally invasive cardiac interventions cause less trauma and sideeffects
to patients. However, the direct view of surgical targets and tools is usually not available in minimally invasive
procedures, which makes image-guided navigation systems essential. The choice of imaging modalities used in the
navigation systems must consider the capability of imaging soft tissues, spatial and temporal resolution, compatibility
and flexibility in the OR, and financial cost. In this paper, we propose a new means of guidance for minimally invasive
cardiac interventions using 3D real-time ultrasound images to show the intra-operative heart motion together with preoperative
CT image(s) employed to demonstrate high-quality 3D anatomical context. We also develop a method to
register intra-operative ultrasound and pre-operative CT images in close to real-time. The registration method has two
stages. In the first, anatomical features are segmented from the first frame of ultrasound images and the CT image(s). A
feature based registration is used to align those features. The result of this is used as an initialization in the second stage,
in which a mutual information based registration is used to register every ultrasound frame to the CT image(s). A GPU
based implementation is used to accelerate the registration.