Simulating Patho-realistic Ultrasound Images using Deep Generative Networks with Adversarial Learning

Ultrasound imaging makes use of backscattering of waves during their interaction with scatterers present in biological tissues. Simulation of synthetic ultrasound images is a challenging problem on account of inability to accurately model various factors of which some include intra-/inter scanline interference, transducer to surface coupling, artifacts on transducer elements, inhomogeneous shadowing and nonlinear attenuation. Current approaches typically solve wave space equations making them computationally expensive and slow to operate. We propose a generative adversarial network (GAN) inspired approach for fast simulation of patho-realistic ultrasound images. We apply the framework to intravascular ultrasound (IVUS) simulation. A stage 0 simulation performed using pseudo B-mode ultrasound image simulator yields speckle mapping of a digitally defined phantom. The stage I GAN subsequently refines them to preserve tissue specific speckle intensities. The stage II GAN further refines them to generate high resolution images with patho-realistic speckle profiles. We evaluate patho-realism of simulated images with a visual Turing test indicating an equivocal confusion in discriminating simulated from real. We also quantify the shift in tissue specific intensity distributions of the real and simulated images to prove their similarity.Comment: To appear in the Proceedings of the 2018 IEEE International Symposium on Biomedical Imaging (ISBI 2018

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

Coronary computed tomography angiography of spontaneous coronary artery dissection: A case report and review of the literature

Patient: Male, 23 Final Diagnosis: Spontaneous coronary artery dissection Symptoms: Chest discomfort • chest pain Medication: — Clinical Procedure: Coronary computed tomography angiography Specialty: Radiology OBJECTIVE: Rare disease BACKGROUND: Multidetector computed tomography (MDCT) has gained wide acceptance in the evaluation of the cardiovascular system. Of particular clinical interest is its ability to non-invasively evaluate coronary arteries in patients presenting to the emergency room. In acute coronary syndromes, myocardial ischemia is most often caused by atherosclerosis. We present a case of a rare cause of acute coronary syndrome, spontaneous coronary artery dissection (SCAD), which was initially evaluated with MDCT and followed by intravascular ultrasound (IVUS) and invasive coronary angiography (ICA). We discuss the findings and role of each modality with particular attention to coronary computed tomographic angiography (CCTA) in the diagnosis and management of SCAD. As the use of CCTA in the emergency department continues to rise, radiologists must become familiar with CT appearance of SCAD. CASE REPORT: We report the multidetector computed tomography (MDCT), intravascular ultrasound (IVUS), and invasive coronary angiography (ICA) findings in a case of spontaneous coronary artery dissection of the left anterior descending artery in a previously healthy 23-year-old man. The role of coronary computed tomographic angiography (CCTA) in diagnosis and management of this potentially life-threatening condition is discussed. CONCLUSIONS: In the clinical setting of acute coronary syndrome, SCAD must be a consideration, particularly in young patients without clear risk factors for coronary artery disease and in women in the peripartum period. CCTA is a very helpful diagnostic tool to diagnose the condition in a non-invasive manner and to follow up after treatment

Shape-driven segmentation of the arterial wall in intravascular ultrasound images

Segmentation of arterial wall boundaries from intravascular images is an important problem for many applications in the study of plaque characteristics, mechanical properties of the arterial wall, its 3D reconstruction, and its measurements such as lumen size, lumen radius, and wall radius. We present a shape-driven approach to segmentation of the arterial wall from intravascular ultrasound images in the rectangular domain. In a properly built shape space using training data, we constrain the lumen and media-adventitia contours to a smooth, closed geometry, which increases the segmentation quality without any tradeoff with a regularizer term. In addition to a shape prior, we utilize an intensity prior through a non-parametric probability density based image energy, with global image measurements rather than pointwise measurements used in previous methods. Furthermore, a detection step is included to address the challenges introduced to the segmentation process by side branches and calcifications. All these features greatly enhance our segmentation method. The tests of our algorithm on a large dataset demonstrate the effectiveness of our approach

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 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

A New Approach in Risk Stratification by Coronary CT Angiography.

For a decade, coronary computed tomographic angiography (CCTA) has been used as a promising noninvasive modality for the assessment of coronary artery disease (CAD) as well as cardiovascular risks. CCTA can provide more information incorporating the presence, extent, and severity of CAD; coronary plaque burden; and characteristics that highly correlate with those on invasive coronary angiography. Moreover, recent techniques of CCTA allow assessing hemodynamic significance of CAD. CCTA may be potentially used as a substitute for other invasive or noninvasive modalities. This review summarizes risk stratification by anatomical and hemodynamic information of CAD, coronary plaque characteristics, and burden observed on CCTA