Cryptogenic ischemic stroke and silent atrial fibrillation: What is the relationship?

Atrial fibrillation (AF) is responsible for up to one-third of ischemic strokes and associated with silent cerebral infarctions and transient ischemic attacks. Any method that predicts the stroke or unmasks the silent PAF would contribute to the treatment of ischemic stroke. Intraatrial conduction time (ICT) has been shown to be associated with intermittent AF. In this study, we evaluated the value of ICT detected by transthoracic echocardiography in normal population and in patients with cryptogenic stroke (CS) as a risk factor for stroke. The patients with CS and with normal left ventricular function without valvular disease are included in group 1. Patients with atypical symptoms admitted to cardiology clinics without any risk factor for cardiac disease and found to be normal constituted group 2. Age, gender, weight, height, echocardiographic parameters and ICT were compared between groups. 63 and 64 subjects were included in group 1 and 2, respectively. Two groups were similar according to age and gender. Among the parameters studied, left atrial diameter and height of the patients were significantly higher in group 1 (40 ± 2 vs 37 ± 4 mm, p < 0.001 and 167 ± 9 vs 163 ± 9 cm p = 0.027, respectively). ICT was significantly higher in group 1 (131 ± 15 vs 118 ± 13 ms, respectively, p < 0.000). According to ROC analysis, a cut point of 124 ms for ICT with a sensitivity of 74 % and specificity of 73 % in patients with CS (p < 0.001). This study show us, the measurements the ICT determined by means of echocardiography is longer in patients with CS. This simple and noninvasive technique can be applied widely and lead the clinicians to adopt the use of diagnostic and the treatment procedures.

Detecting tubular structures via direct vector field singularity characterization.

The initial step of vessel segmentation in 3D is the detection of vessel centerlines. The proposed methods in literature are either dependent on vessel radius and/or have low response at vessel bifurcations. In this paper we propose a 3D tubular structure detection method that removes these two drawbacks. The proposed method exploits the observations on the eigenvalues of the Hessian matrix as is done in literature, yet it employs a direct 3D vector field singularity characterization. The Gradient Vector Flow vector field is used and the eigenvalues of its Jacobian are exploited in computing a parameter free vesselness map. Results on phantom and real patient data exhibit robustness to scale, high response at vessel bifurcations, and good noise/non-vessel structure suppression.