Coronary artery stent geometry and in-stent contrast attenuation with 64-slice computed tomography

We aimed at assessing stent geometry and in-stent contrast attenuation with 64-slice CT in patients with various coronary stents. Twenty-nine patients (mean age 60 +/- 11 years; 24 men) with 50 stents underwent CT within 2 weeks after stent placement. Mean in-stent luminal diameter and reference vessel diameter proximal and distal to the stent were assessed with CT, and compared to quantitative coronary angiography (QCA). Stent length was also compared to the manufacturer's values. Images were reconstructed using a medium-smooth (B30f) and sharp (B46f) kernel. All 50 stents could be visualized with CT. Mean in-stent luminal diameter was systematically underestimated with CT compared to QCA (1.60 +/- 0.39 mm versus 2.49 +/- 0.45 mm; P < 0.0001), resulting in a modest correlation of QCA versus CT (r = 0.49; P < 0.0001). Stent length as given by the manufacturer was 18.2 +/- 6.2 mm, correlating well with CT (18.5 +/- 5.7 mm; r = 0.95; P < 0.0001) and QCA (17.4 +/- 5.6 mm; r = 0.87; P < 0.0001). Proximal and distal reference vessel diameters were similar with CT and QCA (P = 0.06 and P = 0.03). B46f kernel images showed higher image noise (P < 0.05) and lower in-stent CT attenuation values (P < 0.001) than images reconstructed with the B30f kernel. 64-slice CT allows measurement of coronary artery in-stent density, and significantly underestimates the true in-stent diameter compared to QCA.

Rotational integral geometry of tensor valuations

We derive a new rotational Crofton formula for Minkowski tensors. In special cases, this formula gives (1) the rotational average of Minkowski tensors defined on linear subspaces and (2) the functional defined on linear subspaces with rotational average equal to a Minkowski tensor. Earlier results obtained for intrinsic volumes appear now as special cases.

Foundations of stochastic geometry and theory of random sets

The first section of this chapter starts with the Buffon problem, which is one of the oldest in stochastic geometry, and then continues with the definition of measures on the space of lines. The second section defines random closed sets and related measurability issues, explains how to characterize distributions of random closed sets by means of capacity functionals and introduces the concept of a selection. Based on this concept, the third section starts with the definition of the expectation and proves its convexifying effect that is related to the Lyapunov theorem for ranges of vector-valued measures. Finally, the strong law of large numbers for Minkowski sums of random sets is proved and the corresponding limit theorem is formulated. The chapter is concluded by a discussion of the union-scheme for random closed sets and a characterization of the corresponding stable laws.