Background and purpose: Abnormal electrical conduction and excitability associated with fibrosis in the left atrium (LA) may serve as a substrate for atrial fibrillation (AF). Electroanatomical voltage mapping systems (EAMs) have become a dominant facilitator to treat AF with catheter ablation assisted by additional diagnostic imaging modalities. Importantly, AF has been associated with structural changes to the extracellular matrix of the myocardium, including increased collagen deposition—a process known as fibrosis. Late gadolinium enhancement-magnetic resonance imaging (LGE-MRI) may aid in guiding AF cardiac ablation therapy by determination of location of fibrosis in the LA. To locate fibrosis for cardiac ablation, however, accurate registration between EAMs and LGE-MRI data is crucial. The purpose of this work was to develop a method for registering EAMs with late gadolinium enhancement-magnetic resonance (LGE-MR) images of fibrosis. Methods: Twenty patients with persistent AF, who underwent magnetic resonance imaging scanning and EAMs prior to first-time catheter ablation, participated in the study. In our registration pipeline, LGE-MR images were registered to the left atrial surface on EAMs using manual alignment followed by iterative closest point (ICP), and non-rigid ICP (NICP) algorithm. Results and conclusions: The results demonstrate that NICP provided a substantial reduction in registration error when compared to the use of affine ICP alone. Regions of fibrosis on LGE-MR images identified using the signal threshold to reference mean threshold demonstrated the most regional overlap with low bipolar voltage points on EAMs. Successful co-registration of LGE-MR images to EAMs may assist electro-physiologists in selecting candidate targets for ablation and ultimately, reduce the rate of AF recurrence for patients.

Atrial fibrillation, Electroanatomical voltage mapping system, Late gadolinium enhancement magnetic resonance imaging, Non-rigid iterative closest point, Point cloud registration
Computers in Biology and Medicine
Department of Systems and Computer Engineering

Lee, J. (J.), Thornhill, R.E. (Rebecca E.), Nery, P. (P.), Robert deKemp, (), Peña, E. (E.), Birnie, D. (D.), … Ukwatta, E.M. (2019). Left atrial imaging and registration of fibrosis with conduction voltages using LGE-MRI and electroanatomical mapping. Computers in Biology and Medicine, 111. doi:10.1016/j.compbiomed.2019.103341