  Pulmonary vein isolation (PVI) is a common procedure for the treatment of
atrial fibrillation (AF). A successful isolation produces a continuous lesion
(scar) completely encircling the veins that stops activation waves from
propagating to the atrial body. Unfortunately, the encircling lesion is often
incomplete, becoming a combination of scar and gaps of healthy tissue. These
gaps are potential causes of AF recurrence, which requires a redo of the
isolation procedure. Late-gadolinium enhanced cardiac magnetic resonance
(LGE-CMR) is a non-invasive method that may also be used to detect gaps, but it
is currently a time-consuming process, prone to high inter-observer
variability. In this paper, we present a method to semi-automatically identify
and quantify ablation gaps. Gap quantification is performed through minimum
path search in a graph where every node is a scar patch and the edges are the
geodesic distances between patches. We propose the Relative Gap Measure (RGM)
to estimate the percentage of gap around a vein, which is defined as the ratio
of the overall gap length and the total length of the path that encircles the
vein. Additionally, an advanced version of the RGM has been developed to
integrate gap quantification estimates from different scar segmentation
techniques into a single figure-of-merit. Population-based statistical and
regional analysis of gap distribution was performed using a standardised
parcellation of the left atrium. We have evaluated our method on synthetic and
clinical data from 50 AF patients who underwent PVI with radiofrequency
ablation. The population-based analysis concluded that the left superior PV is
more prone to lesion gaps while the left inferior PV tends to have less gaps
(p<0.05 in both cases), in the processed data. This type of information can be
very useful for the optimization and objective assessment of PVI interventions.
