Image-based cardiac gating for three-dimensional intravascular ultrasound imaging

Three-dimensional (3-D) intravascular ultrasound (US), or IVUS, provides valuable insight into the tissue characteristics of the coronary wall and plaque composition. However, artefacts due to cardiac motion and vessel wall pulsation limit the accuracy and variability of coronary lumen and plaque volume measurement in 3-D IVUS images. ECG-gated image acquisition can reduce these artefacts but it requires recording the ECG signal and may increase image acquisition time. The goal of our study was to reconstruct a 3-D IVUS image with negligible cardiac motion and vessel pulsation artefacts, by developing an image-based gating method to track 2-D IVUS images over the cardiac cycle. Our approach involved selecting 2-D IVUS images belonging to the same cardiac phase from an asynchronously-acquired series, by tracking the changing lumen contour over the cardiac cycle. The algorithm was tested with IVUS images of a custom-built coronary vessel phantom and with patient images. The artefact reduction achieved using the image-gating approach was > 86% in the in vitro images and > 80% in the in vivo images in our study. Our study shows that image-based gating of IVUS images provides a useful method for accurate reconstruction of 3-D IVUS images with reduced cardiac motion artefact.     

Advanced contour detection for three-dimensional intracoronary ultrasound: a validation--in vitro and in vivo

Intracoronary ultrasound (ICUS) provides high-resolution transmural images of the arterial wall. By performing a pullback of the ICUS transducer and three-dimensional reconstruction of the images, an advanced assessment of the lumen and vessel wall morphology can be obtained. To reduce the analysis time and the subjectivity of boundary tracing, automated segmentation of the image sequence must be performed. The Quantitative Coronary Ultrasound – Clinical Measurement Solutions (QCU-CMS) (semi)automated analytical software package uses a combination of transversal and longitudinal model and knowledge-guided contour detection techniques. On multiple longitudinal sections through the pullback stack, the external vessel contours are detected simultaneously, allowing mutual guidance of the detection in difficult areas. Subsequently, luminal contours are detected on these longitudinal sections. Vessel and luminal contour points are transformed to the individual cross-sections, where they guide the vessel and lumen contour detection on these transversal images. The performance of the software was validated stepwise. A set of phantoms was used to determine the systematic and random errors of the contour detection of external vessel and lumen boundaries. Subsequently, the results of the contour detection as obtained in in vivo image sets were compared with expert manual tracing, and finally the contour detection in in vivo image sequences was compared with results obtained from another previously validated ICUS quantification system. The phantom lumen diameters were underestimated by 0.1 mm, equally by the QCU-CMS software and by manual tracing. Comparison of automatically detected contours and expert manual contours, showed that lumen contours correspond very well (systematic and random radius difference: –0.025 ± 0.067 mm), while automatically detected vessel contours slightly overestimated the expert manual contours (radius difference: 0.061 ± 0.037 mm). The cross-sectional vessel and lumen areas as detected with our system and with the second computerized system showed a high correlation (r = 0.995 and 0.978, respectively). Thus, use of the new QCU-CMS analytical software is feasible and the validation data suggest its application for the analysis of clinical research.     

Image analysis techniques for automated IVUS contour detection

Intravascular ultrasound (IVUS) constitutes a valuable technique for the diagnosis of coronary atherosclerosis. The detection of lumen and media-adventitia borders in IVUS images represents a necessary step towards the reliable quantitative assessment of atherosclerosis. In this work, a fully automated technique for the detection of lumen and media-adventitia borders in IVUS images is presented. This comprises two different steps for contour initialization: one for each corresponding contour of interest and a procedure for the refinement of the detected contours. Intensity information, as well as the result of texture analysis, generated by means of a multilevel discrete wavelet frames decomposition, are used in two different techniques for contour initialization. For subsequently producing smooth contours, three techniques based on low-pass filtering and radial basis functions are introduced. The different combinations of the proposed methods are experimentally evaluated in large datasets of IVUS images derived from human coronary arteries. It is demonstrated that our proposed segmentation approaches can quickly and reliably perform automated segmentation of IVUS images. (E-mail: mpapad@iti.gr).     

Automated contour detection for high-frequency intravascular ultrasound imaging: a technique with blood noise reduction for edge enhancement

Automated edge detection may standardize measurements among observers, providing for rapid assessment of intravascular ultrasound (IVUS) images. However, with high frequency images, enhanced blood signals make it difficult to define and trace the lumen borders. Accordingly, we evaluated a fully automated contour analysis facilitated with a blood noise reduction algorithm (BNR) for 40-MHz IVUS images in human coronary arteries of 27 patients. This algorithm is based on the principle that blood echo speckles have higher temporal and spatial variations than the arterial wall. A total of 193 paired lumen areas and 78 external elastic membrane (EEM) areas were measured and compared. Automated measurements showed good agreement with manual tracings for lumen and EEM area, with high correlation coefficients (0.945 and 0.950, respectively) and small variability (0.4 +/- 14.4% and 0.6 +/- 9.7%, respectively). This preliminary finding suggests that automated contour detection facilitated with BNR appeared to be a feasible and reliable technique for area measurements in 40-MHz IVUS imaging.     

Assessment of three dimensional quantitative coronary analysis by using rotational angiography for measurement of vessel length and diameter

The aim of the study was to assess the accuracy of the three-dimensional (3D) quantitative coronary analysis (QCA) system by comparing with that of intravascular ultrasound (IVUS) QCA and two-dimensional (2D) QCA. 3D QCA, 2D QCA and IVUS QCA were performed in 45 vessel segments. The obtained values for the branch to branch segment vessel length and the proximal part of the segment vessel's lumen diameter were measured. Inter-technique agreement was analyzed using paired sample t-test and Bland-Altman analysis. No differences were found in vessel lengths taken by 3D QCA and IVUS QCA (mean difference: 0.29?±?1.06?mm, P?=?0.07). When compared with IVUS QCA, 2D QCA underestimated vessel length (mean difference: -1.78?±?2.55, P?相似文献   

Three-dimensional automatic quantitative analysis of intravascular ultrasound images

Intravascular ultrasound (IVUS) has established itself as a useful tool for coronary assessment. The vast amount of data obtained by a single IVUS study renders manual analysis impractical for clinical use. A computerized method is needed to accelerate the process and eliminate user-dependency. In this study, a new algorithm is used to identify the lumen border and the media-adventitia border (the external elastic membrane). Setting an initial surface on the IVUS catheter perimeter and using active contour principles, the surface inflates until virtual force equilibrium defined by the surface geometry and image features is reached. The method extracts these features in three dimensions (3-D). Eight IVUS procedures were performed using an automatic pullback device. Using the ECG signal for synchronization, sets of images covering the entire studied region and corresponding to the same cardiac phase were sampled. Lumen and media-adventitia border contours were traced manually and compared to the automatic results obtained by the suggested method. Linear regression results for vessel area enclosed by the lumen and media-adventitia border indicate high correlation between manual vs. automatic tracings (y = 1.07 x -0.38; r = 0.98; SD = 0.112 mm(2); n = 88). These results indicate that the suggested algorithm may potentially provide a clinical tool for accurate lumen and plaque assessment.     

Automatic segmentation of the lumen region in intravascular images of the coronary artery

Image assessment of the arterial system plays an important role in the diagnosis of cardiovascular diseases. The segmentation of the lumen and media-adventitia in intravascular (IVUS) images of the coronary artery is the first step towards the evaluation of the morphology of the vessel under analysis and the identification of possible atherosclerotic lesions. In this study, a fully automatic method for the segmentation of the lumen in IVUS images of the coronary artery is presented. The proposed method relies on the K-means algorithm and the mean roundness to identify the region corresponding to the potential lumen. An approach to identify and eliminate side branches on bifurcations is also proposed to delimit the area with the potential lumen regions. Additionally, an active contour model is applied to refine the contour of the lumen region. In order to evaluate the segmentation accuracy, the results of the proposed method were compared against manual delineations made by two experts in 326 IVUS images of the coronary artery. The average values of the Jaccard measure, Hausdorff distance, percentage of area difference and Dice coefficient were 0.88 ± 0.06, 0.29 ± 0.17  mm, 0.09 ± 0.07 and 0.94 ± 0.04, respectively, in 324 IVUS images successfully segmented. Additionally, a comparison with the studies found in the literature showed that the proposed method is slight better than the majority of the related methods that have been proposed. Hence, the new automatic segmentation method is shown to be effective in detecting the lumen in IVUS images without using complex solutions and user interaction.     

Automated Quantitative Assessment of Coronary Calcification Using Intravascular Ultrasound

Coronary calcification represents a challenge in the treatment of coronary artery disease by stent placement. It negatively affects stent expansion and has been related to future adverse cardiac events. Intravascular ultrasound (IVUS) is known for its high sensitivity in detecting coronary calcification. At present, automated quantification of calcium as detected by IVUS is not available. For this reason, we developed and validated an optimized framework for accurate automated detection and quantification of calcified plaque in coronary atherosclerosis as seen by IVUS. Calcified lesions were detected by training a supported vector classifier per IVUS A-line on manually annotated IVUS images, followed by post-processing using regional information. We applied our framework to 35 IVUS pullbacks from each of the three commonly used IVUS systems. Cross-validation accuracy for each system was >0.9, and the testing accuracy was 0.87, 0.89 and 0.89 for the three systems. Using the detection result, we propose an IVUS calcium score, based on the fraction of calcium-positive A-lines in a pullback segment, to quantify the extent of calcified plaque. The high accuracy of the proposed classifier suggests that it may provide a robust and accurate tool to assess the presence and amount of coronary calcification and, thus, may play a role in image-guided coronary interventions.     

Reproducibility of volumetric quantification in intravascular ultrasound images

The reproducibility of volume measurements in intravascular ultrasound (IVUS) images derived from separate pull-back manoeuvres remains to be elucidated. Patients (n = 23) were imaged with IVUS prior to (first series) and following percutaneous transluminal angioplasty (PTA) (second series). In 15 patients, one matched vascular segment (3-4 cm in length), not subjected to PTA, was used for analysis of lumen, vessel and plaque volume using an automated contour analysis system. Volume measurements assessed by two independent observers and in the two separate series were compared. Interobserver differences in volume measurements were small (< or =0.4%), with low coefficients of variation (< or =1.7%) and high correlation coefficients (r = 1.00). Differences in volume measurements obtained in the two separate series were small (< or =2.6%), with low coefficients of variation (< or = 8.6%) and high correlation coefficients (r = 0.97-0.99). In conclusion, volume measurements derived from IVUS images are highly reproducible. Therefore, IVUS may be used to monitor the progression/regression of atherosclerotic plaque volume in a longitudinal study.     

Validation of an automated system for luminal and medial-adventitial border detection in three-dimensional intravascular ultrasound

The precise tomographic assessment of coronary artery disease by intravascular ultrasound (IVUS) is useful in quantitative studies. Such studies require identification of luminal and medial-adventitial (MA) borders in a sequence of IVUS images. We have developed a three-dimensional (3D) active-surface system for border detection that facilitates the analysis of many images with minimal user interaction. To assess the validity of the technique, luminal and MA borders in 529 end-diastolic images from nine coronary arterial segments (58.8 ± 14.2 images per patient) were traced manually by four experienced observers. The computer-detected borders were compared with borders determined by the four observers using a modified Williams' index (WI), the ratio of inter-observer variability to computer-observer variability. While manual tracing required 49.2 ± 12.1 min for analysis, the analysis system identified luminal (R ² = 0.92) and MA borders (R ² = 0.97) in 13.8 ± 4.0 min, a decrease of 35.4 min (p < 0.000001). The computer minus observer differences in lumen area and MA area were –0.88 ± 0.90 and –0.07 ± 0.63 mm². Therefore, the computer system underestimated both lumen and MA area, but this effect was very small in MA area. The WI values and 95% confidence intervals were 0.98 (0.89,1.06) for luminal border detection and 0.99 (0.95,1.04) for MA border detection. Plaque volume measurements, a common endpoint of clinical trials, also verified the accuracy of the technique (R ² = 0.98). The proposed 3D active-surface border detection system provides a faster and less-tedious alternative to manual tracing for assessment of coronary artery anatomy in vivo.     

Interstudy reproducibility of the second generation, Fourier domain optical coherence tomography in patients with coronary artery disease and comparison with intravascular ultrasound: a study applying automated contour detection

Recently, Fourier domain OCT (FD-OCT) has been introduced for clinical use. This approach allows in vivo, high resolution (15 micron) imaging with very fast data acquisition, however, it requires brief flushing of the lumen during imaging. The reproducibility of such fast data acquisition under intracoronary flush application is poorly understood. To assess the inter-study variability of FD-OCT and to compare lumen morphometry to the established invasive imaging method, IVUS. 18 consecutive patients with coronary artery disease scheduled for PCI were included. In each target vessel a FD-OCT pullback (MGH system, light source 1,310 nm, 105 fps, pullback speed 20 mm/s) was acquired during brief (3 s) injection of X-ray contrast (flow 3 ml/s) through the guiding catheter. A second pullback was repeated under the same conditions after re-introduction of the FD OCT catheter into the coronary artery. IVUS and OCT imaging was performed in random order. FD-OCT and IVUS pullback data were analyzed using a recently developed software employing semi automated lumen contour and stent strut detection algorithms. Corresponding ROI were matched based on anatomical landmarks such as side branches and/or stent edges. Inter-study variability is presented as the absolute difference between the two pullbacks. FD-OCT showed remarkably good reproducibility. Inter-study variability in native vessels (cohort A) was very low for mean and minimal luminal area (0.10 ± 0.38, 0.19 ± 0.57 mm², respectively). Likewise inter-study variability was very low in stented coronary segments (cohort B) for mean lumen, mean stent, minimal luminal and minimal stent area (0.06 ± 0.08, 0.07 ± 0.10, 0.04 ± 0.09, 0.04 ± 0.10 mm², respectively). Comparison to IVUS morphometry revealed no significant differences. The differences between both imaging methods, OCT and IVUS, were very low for mean lumen, mean stent, minimal luminal and minimal stent area (0.10 ± 0.45, 0.10 ± 0.36, 0.26 ± 0.54, 0.05 ± 0.47 mm², respectively). FD-OCT shows excellent reproducibility and very low inter-study variability in both, native and stented coronary segments. No significant differences in quantitative lumen morphometry were observed between FD-OCT and IVUS. Evaluating these results suggest that FD-OCT is a reliable imaging tool to apply in longitudinal coronary artery disease studies.     

冠状动脉弹性参数测量的系统与方法

本文提出一种利用血管内超声技术测量血管弹性参数的方法.这种方法通过血管内超声成像(IVUS)得到冠状动脉的B型超声视频图像,然后通过图像处理技术提取血管壁轮廓,从而计算出截面积.获得一个心动周期以上的冠状动脉截面积曲线后,计算出相应的冠状动脉直径曲线,结合基于血管内压力测量技术检测到的血压曲线,计算出与冠状动脉弹性有关的参数.文中给出了利用该方法建立的系统所测量的实例及其相应结果.     

Invasive diagnostic imaging of coronary atherosclerosis

Invasive diagnostic imaging technique of coronary atherosclerosis has rapidly developed. For example, intravascular ultrasound(IVUS) is recognized as an essential device for percutaneous coronary intervention to evaluate the vessel wall, vascular lumen and coronary plaque morphologies because of its accuracy for quantitative analysis capability. Recently new imaging modalities such as radio-frequency signal analysis, elastography and contrast harmonic echography have been developed for the evaluation of histological characteristics. Also, optical coherence tomography(OCT), which provides approximately ten-times higher-resolutional cross-section images of the coronary arterial wall in comparison with IVUS, became available in clinical setting. In this article, we review the latest progress of the invasive diagnostic imaging of coronary atherosclerosis.     

血管内超声测量血流动力学参数的研究进展

血管内超声显像可在活体中观察血管壁、管腔及斑块形态、性质,甚至斑块成分和管壁功能状态,由此获得的冠状动脉的血流动力学参数(包括管壁的弹性模量和血管壁应力-应变分布等)对于正确诊断冠心病、制定最佳诊疗方案具有重要意义。本文就近年来血管内超声在冠状动脉血流动力学参数测量中的应用进行综述。     

Towards a geometrically correct 3-D reconstruction of tortuous coronary arteries based on biplane angiography and intravascular ultrasound

At present, 3-D reconstructions of coronary vessels are generated from intravascular ultrasound (IVUS) by stacking up ECG-gated segmented IVUS frames of a pullback sequence. This simplified approach always results in straight vessel reconstructions and, therefore, gives an incorrect representation of tortuous coronary arteries. A more realistic reconstruction of tortuous vessels may be obtained by data fusion with biplane angiography. The 3-D course of the vessel is first derived from the angiograms and then combined with the segmented IVUS images. In this paper, we focus on two problems associated with the data fusion method: The definition of the pullback path and the estimation of the IVUS catheter twist during pullback. A robust algorithm for calculation of tortuosity-induced catheter twist is reported that is based on sequential triangulation of the 3-D pullback path. The method is analyzed with computer simulations and validated in helical vessel phantoms. A largely automated data fusion approach is proposed and applied to tortuous coronary arteries in cadaveric pig hearts.     

Automatic segmentation of calcified plaques and vessel borders in IVUS images

Objective

Intravascular ultrasound (IVUS) is a diagnostic imaging technique for tomographic visualization of coronary arteries. Automatic analysis of IVUS images is difficult due to speckle noise, artifacts of the catheter, and shadows generated by calcifications. We designed and implemented a system for automated segmentation of coronary artery IVUS images.

Methods

Two methods for automatic detection of the intima and the media-adventitia borders in IVUS coronary artery images were developed and compared. The first method uses the parametric deformable models, while the second method is based on the geometric deformable models. The initial locations of the borders are approximated using two different edge detection methods. The final borders are then defined using the two deformable models. Finally, the calcified regions between the extracted borders are identified using a Bayesian classifier. The performance of the proposed methods was evaluated using 60 different IVUS images obtained from 7 patients.

Results

Segmented images were compared with manually outlined contours. We compared the performance of calcified region characterization methods using ROC analysis and by computing the sensitivity and specificity of the Bayesian classifier, thresholding, adaptive thresholding, and textural features. The Bayesian method performed best.

Conclusion

The results shows that the geometric deformable model outperforms the parametric deformable model for automated segmentation of IVUS coronary artery images.     

血管内超声/光声联合成像研究进展

血管内超声/光声联合成像技术将血管内超声成像(IVUS)和血管内光声成像(IVPA)相结合、生成血管的组合图像。这种新的成像技术可获得高对比度和高分辨率的血管壁及管腔图像,可快速定位风险斑块并辨别其成分。本文就IVUS/IVPA联合成像的可行性及研究现状进行综述。     

Automatic coronary wall segmentation in intravascular ultrasound images using binary morphological reconstruction

Intravascular ultrasound (IVUS) image segmentation can provide more detailed vessel and plaque information, resulting in better diagnostics, evaluation and therapy planning. A novel automatic segmentation proposal is described herein; the method relies on a binary morphological object reconstruction to segment the coronary wall in IVUS images. First, a preprocessing followed by a feature extraction block are performed, allowing for the desired information to be extracted. Afterward, binary versions of the desired objects are reconstructed, and their contours are extracted to segment the image. The effectiveness is demonstrated by segmenting 1300 images, in which the outcomes had a strong correlation to their corresponding gold standard. Moreover, the results were also corroborated statistically by having as high as 92.72% and 91.9% of true positive area fraction for the lumen and media adventitia border, respectively. In addition, this approach can be adapted easily and applied to other related modalities, such as intravascular optical coherence tomography and intravascular magnetic resonance imaging.     

Towards quantitative analysis of coronary CTA

The current high spatial and temporal resolution, multi-slice imaging capability, and ECG-gated reconstruction of multi-slice computed tomography (MSCT) allows the non-invasive 3D imaging of opacified coronary arteries. MSCT coronary angiography studies are currently carried out by the visual inspection of the degree of stenosis and it has been shown that the assessment with sensitivities and specificities of 90% and higher can be achieved. To increase the reproducibility of the analysis, we present a method that performs the quantitative analysis of coronary artery diseases with limited user interaction: only the positioning of one or two seed points is required. The method allows the segmentation of the entire left or right coronary tree by the positioning of a single seed point, and an extensive evaluation of a particular vessel segment by placing a proximal and distal seed point. The presented method consists of: (1) the segmentation of the coronary vessels, (2) the extraction of the vessel centerline, (3) the reformatting of the image volume, (4) a combination of longitudinal and transversal contour detection, and (5) the quantification of vessel morphological parameters. The method is illustrated in this paper by the segmentation of the left and right coronary trees and by the analysis of a coronary artery segment. The sensitivity of the positioning of the seed points is studied by varying the position of the proximal and distal seed points with a standard deviation of 6 and 8 mm (along the vessels course) respectively. It is shown that only close to the individual seed points the vessel centerlines deviate and that for more than 80% of the centerlines the paths coincide. Since the quantification depends on the determination of the centerline, no user variability is expected as long as the seed points are positioned reasonably far away from the vessel lesion. The major bottleneck of MSCT imaging of the coronary arteries is the potential lack of image quality due to limitations in the spatial and temporal resolution, irregular or high heart beat, respiratory effects, and variations of the distribution of the contrast agent: the number of rejected vessel segments in diagnostic studies is currently still too high for implementation in routine clinical practice. Also for the automated quantitative analysis of the coronary arteries high image quality is required. However, based upon the trend in technological development of MSCT scanners, there is no doubt that the quantitative analysis of MSCT coronary angiography will benefit from these technological advances in the near future.     

Modelling of image-catheter motion for 3-D IVUS

Three-dimensional intravascular ultrasound (IVUS) allows to visualize and obtain volumetric measurements of coronary lesions through an exploration of the cross sections and longitudinal views of arteries. However, the visualization and subsequent morpho-geometric measurements in IVUS longitudinal cuts are subject to distortion caused by periodic image/vessel motion around the IVUS catheter. Usually, to overcome the image motion artifact ECG-gating and image-gated approaches are proposed, leading to slowing the pullback acquisition or disregarding part of IVUS data. In this paper, we argue that the image motion is due to 3-D vessel geometry as well as cardiac dynamics, and propose a dynamic model based on the tracking of an elliptical vessel approximation to recover the rigid transformation and align IVUS images without loosing any IVUS data. We report an extensive validation with synthetic simulated data and in vivo IVUS sequences of 30 patients achieving an average reduction of the image artifact of 97% in synthetic data and 79% in real-data. Our study shows that IVUS alignment improves longitudinal analysis of the IVUS data and is a necessary step towards accurate reconstruction and volumetric measurements of 3-D IVUS.