Reproducibility of calcified lesion quantification: a longitudinal intravascular ultrasound study

Small coronary calcifications are not detectable by 64-slice contrast enhanced computed tomography

Recently, small calcifications have been associated with unstable plaques. Plaque calcifications are both in intravascular ultrasound (IVUS) and multi-slice computed tomography (MSCT) easily recognized. However, smaller calcifications might be missed on MSCT due to its lower resolution. Because it is unknown to which extent calcifications can be detected with MSCT, we compared calcification detection on contrast enhanced MSCT with IVUS. The coronary arteries of patients with myocardial infarction or unstable angina were imaged by 64-slice MSCT angiography and IVUS. The IVUS and MSCT images were registered and the arteries were inspected on the presence of calcifications on both modalities independently. We measured the length and the maximum circumferential angle of each calcification on IVUS. In 31 arteries, we found 99 calcifications on IVUS, of which only 47 were also detected on MSCT. The calcifications missed on MSCT (n = 52) were significantly smaller in angle (27° ± 16° vs. 59° ± 31°) and length (1.4 ± 0.8 vs. 3.7 ± 2.2 mm) than those detected on MSCT. Calcifications could only be detected reliably on MSCT if they were larger than 2.1 mm in length or 36° in angle. Half of the calcifications seen on the IVUS images cannot be detected on contrast enhanced 64-slice MSCT angiography images because of their size. The limited resolution of MSCT is the main reason for missing small calcifications.

An Automated Method for Lumen and Media–Adventitia Border Detection in a Sequence of IVUS Frames

In this paper, we present a method for the automated detection of lumen and media-adventitia border in sequential intravascular ultrasound (IVUS) frames. The method is based on the use of deformable models. The energy function is appropriately modified and minimized using a Hopfield neural network. Proper modifications in the definition of the bias of the neurons have been introduced to incorporate image characteristics. A simulated annealing scheme is included to ensure convergence at a global minimum. The method overcomes distortions in the expected image pattern, due to the presence of calcium, employing a specialized structure of the neural network and boundary correction schemas which are based on a priori knowledge about the vessel geometry. The proposed method is evaluated using sequences of IVUS frames from 18 arterial segments, some of them indicating calcified regions. The obtained results demonstrate that our method is statistically accurate, reproducible, and capable to identify the regions of interest in sequences of IVUS frames.