Quantitative analysis of intracoronary ultrasound (ICUS) studies is performed on a series of tomographic cross-sectional ICUS images acquired during a motorized 0.5 mm/sec catheter pullback. Catheter displacement in the vascular lumen during the cardiac cycle causes an anatomically shuffled ICUS study, which results in a sawtooth-shaped appearance of the coronary segment in longitudinal reconstructed views in quantitative coronary ultrasound software packages. This hampers contour detection and leads to a laborious time-consuming semiquantitative analysis process that may produce inaccurate results. To solve these problems, in the past, online ECG-gated acquisition hardware has been applied. This article describes a novel image-based gating method called Intelligate, which features automatic retrospective selection of end-diastolic frames from videotaped or digitally stored ICUS studies. Our evaluation shows that there are no quantitative differences between analysis results of hardware ECG-gated and Intelligated ICUS studies.
Intracoronary ultrasound (ICUS) provides highresolution tomographic images of selected segments of coronary arteries. Series of cross-sectional images are acquired with motorized pullback imaging catheters and used for quantitative analysis in intracoronary ultrasound studies (ICUS). IntroductionIntracoronary ultrasound (ICUS) gives insight into the composition and extent of atherosclerotic plaque [1]. Previous studies have shown that ICUS may visualize atherosclerotic plaques in angiographically normal coronary arteries. ICUS is used in clinical trials to evaluate the results of novel catheter-based interventional techniques as well as pharmaceutical treatments.Cross-sectional ICUS images are acquired during a continuous speed pullback of the catheter in the coronary artery. In quantitative analysis procedures vessel wall borders are traced in a consecutive number of images. To assess vessel wall morphology and to facilitate quantitative analysis, three-dimensional (3D) reconstruction is performed using quantitative coronary ultrasound (QCU) software that visualizes segments by longitudinal cut-planes (L-views). This procedure avoids the time-consuming manual tracing of a series of individual cross-sections. However, cyclic systolicdiastolic changes of vascular dimensions and catheter motion result in saw-tooth shaped image artifacts in the L-views that significantly hamper the quantitative analysis. (Semi-)automatic contour detection is interfered, the analysis process becomes time-consuming and the procedure may produce inaccurate results.Most QCU analysis software packages acquire ICUS images stored on videotape at a rate of 1 frame per two seconds, randomly within the cardiac cycle, resulting in 1 mm intervals between the frames (assuming the catheter is pulled back with 0.5 mm/s), for use in area measurement and subsequent calculation of volumetric quantitative parameters. However, it has been reported that longitudinal catheter motion of more than 5 mm may occur during the cardiac cycle.Previous studies have shown that an on-line ECGgated pullback procedure overcomes this motion problem and allows more accurate and reproducible measurements [2]. However, the technology requires expensive hardware, long setup times and considerably prolongs the acquisition procedure. Most laboratories still use non-gated acquisition and most existing image databases lack ECG-gated-data. Therefore, we developed the fully automated retrospective image-based gating method "Intelligate", that can select end-diastolic ICUS frames enabling fast and accurate analysis of ICUS studies. In this paper we describe this new method and its validation. The Intellgate methodThe Intelligate method selects ICUS images recorded in the end-diastolic phase. The rationale for selecting this phase is the mutual comparability of the images as the heart is relatively motionless here and blood flow has ceased. This means that forces originating from cardiac motion and the blood flow are no longer acting on the catheter. In the end-diastoli...
IntroductionIntracoronary ultrasound (ICUS) is used as a tomographic imaging technique to visualize the vessel wall morphology and to identify atherosclerotic plaque [1]. To examine a selected coronary vessel segment an ICUS imaging catheter is pulled back through the coronary artery, meanwhile acquiring a series of cross sectional images. Three-dimensional (3D) reconstruction of the segment and contour tracing in the ICUS images facilitate subsequent quantitative coronary ultrasound (QCU) analysis and qualifies ICUS to be used in clinical trials to evaluate the results of novel catheter-based interventional techniques as well as pharmaceutical treatments [2].Knowledge of plaque composition is a valuable clinical parameter to assess information on plaque formation, progression and rupture [3] and plays an important role in gaining insight in the processes underlying events of sudden cardiac death [4]. Studies that compared histology to intravascular ultrasound learned that different structures correspond to different video-densities in the images; structures that appear white indicating high echogenicity, mainly consist of dense fibrous or calcific tissue. The darker, echolucent or hypoechogenic areas contain larger amounts of loose fibrous or smooth, muscle-rich tissue and thrombotic or necrotic elements [5]. Based on this idea, video-densitometry can be used for plaque characterization [7].Manual classification of tissue in the ICUS images for an entire 3D segment is very time-consuming, and potentially error prone. The analyst has to judge the high number of ICUS images (typically 1500 images per minute pullback acquired at a speed of 0.5 mm/s) in a consistent way, which may result in large inter-and intraobserver variability. Moreover, the human eye can discriminate only about 32 levels of grey, while the ultrasound images can contain 256 levels. Computerassisted grey level interpretation (labelling) and measurement in an entire 3D segment can overcome these problems. Therefore we developed a method, based on video-densitometry, that can automatically label plaque tissue and quantify each labelled type in an existing ICUS pullback. This paper describes this method and shows how the results are presented and visualized in the new research tool. MethodsICUS pullbacks can be stored either on S-VHS videotape, or digitally on CD-ROM. Images originating from standard PAL S-VHS tapes are first de-interlaced to remove the effect of having duplicate images in one image frame. A Gaussian low-pass filter is applied to each cross section to smooth the effect of natural intensity fluctuations caused by the texture of that specific tissue. This ensures that echogenicity measurements focus on the identification of regions instead of individual pixels.ICUS studies that are not acquired ECG-gated [8], are retrospectively image-based gated with the IntelliGate ® [13,14] method before QCU analysis. This allows accurate QCU results in anatomically correctly displayed segments. ECG -gating synchronizes the acquisition o...
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