2D‐3D registration of coronary angiograms for cardiac procedure planning and guidance

Abstract

We present a completely automated 2D-3D registration technique that accurately maps a patient-specific heart model, created from preoperative images, to the patient's orientation in the operating room. This mapping is based on the registration of preoperatively acquired 3D vascular data with intraoperatively acquired angiograms. Registration using both single and dual-plane angiograms is explored using simulated but realistic datasets that were created from clinical images. Heart deformations and cardiac phase mismatches are taken into account in our validation using a digital 4D human heart model. In an ideal situation where the pre- and intraoperative images were acquired at identical time points within the cardiac cycle, the single-plane and the dual-plane registrations resulted in 3D root-mean-square (rms) errors of 1.60 +/- 0.21 and 0.53 +/- 0.08 mm, respectively. When a 10% timing offset was added between the pre- and the intraoperative acquisitions, the single-plane registration approach resulted in inaccurate registrations in the out-of-plane axis, whereas the dual-plane registration exhibited a 98% success rate with a 3D rms error of 1.33 +/- 0.28 mm. When all potential sources of error were included, namely, the anatomical background, timing offset, and typical errors in the vascular tree reconstruction, the dual-plane registration performed at 94% with an accuracy of 2.19 +/- 0.77 mm.

Patient Specific 4D Coronary Models from ECG-gated CTA Data for Intra-operative Dynamic Alignment of CTA with X-ray Images

“…Static registration of simulated X-ray images and CTA data has been performed by Turgeon et al, who presented results on both mono-and biplane registration at end-diastole [3]. Deformable 2D/3D registration of vasculature was presented by Zikic et al, who constrained the deformation of the vessels based on a priori information about plausible deformations [4].…”

Segmentation-Driven 2D-3D Registration for Abdominal Catheter Interventions

“…1. Illustration of the segmentation-driven registration: 1(a) original DSA, 1(b) bothat filtered DSA, 1(c) initial segmentation (automatic seed point detection, region growing), 1(d) probability map penalizing non-corresponding but extracted features in 2D and 3D, 1(e) final segmentation after registration -increased feature similarity, 1(f) overlay of 3D vasculature and DSA not subject to hard time constraints 1 The 3D point cloud that spatially describes M , i.e. sampling points on vessel centerlines and bifurcation locations, is denoted by {X j }.…”

“…2D-3D rigid registration in deformable regions can be addressed by a fully intensity-based procedure with gating, as proposed e.g. by Turgeon et al [1] for heart. However, it is difficult to use such an image-based method without gating information.…”

Information theoretic regularization in diffuse optical tomography

“…• The dimensionality of each of the involved images, with cases including 2D/2D [Jacquet et al, 2009], 3D/3D [Rueckert et al, 1999], 2D/3D [Huang et al, 2009;Turgeon et al, 2005], studies involving time series and more.…”