7,659 research outputs found
A new approach for improving coronary plaque component analysis based on intravascular ultrasound images
Virtual histology intravascular ultrasound (VH-IVUS) is a clinically available technique for atherosclerosis plaque characterization. It, however, suffers from a poor longitudinal resolution due to electrocardiogram (ECG)-gated acquisition. This article presents an effective algorithm for IVUS image-based histology to overcome this limitation. After plaque area extraction within an input IVUS image, a textural analysis procedure consisting of feature extraction and classification steps is proposed. The pixels of the extracted plaque area excluding the shadow region were classified into one of the three plaque components of fibro-fatty (FF), calcification (CA) or necrotic core (NC) tissues. The average classification accuracy for pixel and region based validations is 75% and 87% respectively. Sensitivities (specificities) were 79% (85%) for CA, 81% (90%) for FF and 52% (82%) for NC. The kappa (kappa) = 0.61 and p value = 0.02 indicate good agreement of the proposed method with VH images. Finally, the enhancement in the longitudinal resolution was evaluated by reconstructing the IVUS images between the two sequential IVUS-VH images
Ultrasonic characterization of the pulmonary venous wall: echographic and histological correlation
Background: Pulmonary vein isolation with radiofrequency catheter ablation techniques is used to prevent recurrences of human atrial fibrillation. Visualization of the architecture at the venoatrial junction could be crucial for these ablative techniques. Our study assesses the potential for intravascular ultrasound to provide this information.
Methods and Results: We retrieved 32 pulmonary veins from 8 patients dying from noncardiac causes. We obtained cross-sectional intravascular ultrasound (IVUS) images with a 3.2F, 30-MHz ultrasound catheter at intervals on each vein. Histological cross-sections at the intervals allowed comparisons with ultrasonic images. The pulmonary venous wall at the venoatrial junction revealed a 3-layered ultrasonic pattern. The inner echogenic layer represents both endothelium and connective tissue of the media (mean maximal thickness, 1.4±0.3 mm). The middle hypoechogenic stratum corresponds to the sleeves of left atrial myocardium surrounding the external aspect of the venous media. This layer was thickest at the venoatrial junction (mean maximal thickness, 2.6±0.8 mm) and decreased toward the lung hilum. The outer echodense layer corresponds to fibro-fatty adventitial tissue (mean maximal thickness, 2.15±0.36 mm). We found a close agreement among the IVUS and histological measurements for maximal luminal diameter (mean difference, -0.12±1.3 mm) and maximal muscular thickness (mean difference, 0.17±0.13 mm) using the Bland and Altman method.
Conclusions: Our experimental study demonstrates for the first time that IVUS images of the pulmonary veins can provide information on the distal limits and thickness of the myocardial sleeves and can be a valuable tool to help accurate targeting during ablative procedures
IVUS-based histology of atherosclerotic plaques: improving longitudinal resolution
Although Virtual Histology (VH) is the in-vivo gold standard for atherosclerosis plaque characterization in IVUS images, it suffers from a poor longitudinal resolution due to ECG-gating. In this paper, we propose an image- based approach to overcome this limitation. Since each tissue have different echogenic characteristics, they show in IVUS images different local frequency components. By using Redundant Wavelet Packet Transform (RWPT), IVUS images are decomposed in multiple sub-band images. To encode the textural statistics of each resulting image, run-length features are extracted from the neighborhood centered on each pixel. To provide the best discrimination power according to these features, relevant sub-bands are selected by using Local Discriminant Bases (LDB) algorithm in combination with Fisher’s criterion. A structure of weighted multi-class SVM permits the classification of the extracted feature vectors into three tissue classes, namely fibro-fatty, necrotic core and dense calcified tissues. Results shows the superiority of our approach with an overall accuracy of 72% in comparison to methods based on Local Binary Pattern and Co-occurrence, which respectively give accuracy rates of 70% and 71%
Stent implant follow-up in intravascular optical coherence tomography images
The objectives of this article are (i) to
utilize computer methods in detection of stent struts
imaged in vivo by optical coherence tomography
(OCT) during percutaneous coronary interventions
(PCI); (ii) to provide measurements for the assessment
and monitoring of in-stent restenosis by OCT post PCI.
Thirty-nine OCT cross-sections from seven pullbacks
from seven patients presenting varying degrees of
neointimal hyperplasia (NIH) are selected, and stent
struts are detected. Stent and lumen boundaries are
reconstructed and one experienced observer analyzed
the strut detection, the lumen and stent area measurements,
as well as the NIH thickness in comparison to
manual tracing using the reviewing software provided
by the OCT manufacturer (LightLab Imaging, MA,
USA). Very good agreements were found between
the computer methods and the expert evaluations
for lumen cross-section area (mean difference =
0.11 ± 0.70 mm2; r2 = 0.98, P\ 0.0001) and the
stent cross-section area (mean difference = 0.10 ±
1.28 mm2; r2 = 0.85, P value\ 0.0001). The average
number of detected struts was 10.4 ± 2.9 per crosssection
when the expert identified 10.5 ± 2.8
(r2 = 0.78, P value\0.0001). For the given patient
dataset: lumen cross-sectional area was on the average
(6.05 ± 1.87 mm2), stent cross-sectional area was
(6.26 ± 1.63 mm2), maximum angle between struts
was on the average (85.96 ± 54.23), maximum,
average, and minimum distance between the stent
and the lumen were (0.18 ± 0.13 mm), (0.08 ±
0.06 mm), and (0.01 ± 0.02 mm), respectively, and
stent eccentricity was (0.80 ± 0.08). Low variability
between the expert and automatic method was
observed in the computations of the most important
parameters assessing the degree of neointimal tissue
growth in stents imaged by OCT pullbacks. After
further extensive validation, the presented methods
might offer a robust automated tool that will improve
the evaluation and follow-up monitoring of in-stent
restenosis in patients
A New 3-D automated computational method to evaluate in-stent neointimal hyperplasia in in-vivo intravascular optical coherence tomography pullbacks
Abstract. Detection of stent struts imaged in vivo by optical coherence
tomography (OCT) after percutaneous coronary interventions (PCI) and
quantification of in-stent neointimal hyperplasia (NIH) are important.
In this paper, we present a new computational method to facilitate the
physician in this endeavor to assess and compare new (drug-eluting)
stents. We developed a new algorithm for stent strut detection and utilized
splines to reconstruct the lumen and stent boundaries which provide
automatic measurements of NIH thickness, lumen and stent area. Our
original approach is based on the detection of stent struts unique characteristics:
bright reflection and shadow behind. Furthermore, we present
for the first time to our knowledge a rotation correction method applied
across OCT cross-section images for 3D reconstruction and visualization
of reconstructed lumen and stent boundaries for further analysis in
the longitudinal dimension of the coronary artery. Our experiments over
OCT cross-sections taken from 7 patients presenting varying degrees of
NIH after PCI illustrate a good agreement between the computer method
and expert evaluations: Bland-Altmann analysis revealed a mean difference
for lumen cross-section area of 0.11 ± 0.70mm2 and for the stent
cross-section area of 0.10 ± 1.28mm2
What have we learned from in vitro intravascular ultrasound?
In vitro studies have established that intravascular ultrasound is a reliable technique for accurate assessment of vascular anatomic structure and disease conditions before and after intervention. In addition, quantitative data from intravascular ultrasound studies correspond well with histologic findings, which serve as the gold standard. These in vitro studies permit the understanding and interpretation of ultrasound images obtained in vivo, although differences between the two settings should be taken into account. New ultrasound modalities currently being developed may enhance the diagnostic differentiation of plaque morphologic characteristics and facilitate on-line quantitative assessment of vessel structure
- …