Non-invasive Assessment of Coronary Microvascular Dysfunction Using Vascular Deformation-Based Flow Estimation.

OBJECTIVE: Non-invasive computation of the index of microcirculatory resistance from coronary computed tomography angiography (CTA), referred to as IMR[Formula: see text], is a promising approach for quantitative assessment of coronary microvascular dysfunction (CMD). However, the computation of IMR[Formula: see text] remains an important unresolved problem due to its high requirement for the accuracy of coronary blood flow. Existing CTA-based methods for estimating coronary blood flow rely on physiological assumption models to indirectly identify, which leads to inadequate personalization of total and vessel-specific flow. METHODS: To overcome this challenge, we propose a vascular deformation-based flow estimation (VDFE) model to directly estimate coronary blood flow for reliable IMR[Formula: see text] computation. Specifically, we extract the vascular deformation of each vascular segment from multi-phase CTA. The concept of inverse problem solving is applied to implicitly derive coronary blood flow based on the physical constraint relationship between blood flow and vascular deformation. The vascular deformation constraints imposed on each segment within the vascular structure ensure sufficient individualization of coronary blood flow. RESULTS: Experimental studies on 106 vessels collected from 89 subjects demonstrate the validity of our VDFE, achieving an IMR[Formula: see text] accuracy of 82.08 %. The coronary blood flow estimated by VDFE has better reliability than the other four existing methods. CONCLUSION: Our proposed VDFE is an effective approach to non-invasively compute IMR[Formula: see text] with excellent diagnostic performance. SIGNIFICANCE: The VDFE has the potential to serve as a safe, effective, and cost-effective clinical tool for guiding CMD clinical treatment and assessing prognosis.

Distraction-aware hierarchical learning for vascular structure segmentation in intravascular ultrasound images.

Vascular structure segmentation in intravascular ultrasound (IVUS) images plays an important role in pre-procedural evaluation of percutaneous coronary intervention (PCI). However, vascular structure segmentation in IVUS images has the challenge of structure-dependent distractions. Structure-dependent distractions are categorized into two cases, structural intrinsic distractions and inter-structural distractions. Traditional machine learning methods often rely solely on low-level features, overlooking high-level features. This way limits the generalization of these methods. The existing semantic segmentation methods integrate low-level and high-level features to enhance generalization performance. But these methods also introduce additional interference, which is harmful to solving structural intrinsic distractions. Distraction cue methods attempt to address structural intrinsic distractions by removing interference from the features through a unique decoder. However, they tend to overlook the problem of inter-structural distractions. In this paper, we propose distraction-aware hierarchical learning (DHL) for vascular structure segmentation in IVUS images. Inspired by distraction cue methods for removing interference in a decoder, the DHL is designed as a hierarchical decoder that gradually removes structure-dependent distractions. The DHL includes global perception process, distraction perception process and structural perception process. The global perception process and distraction perception process remove structural intrinsic distractions then the structural perception process removes inter-structural distractions. In the global perception process, the DHL searches for the coarse structural region of the vascular structures on the slice of IVUS sequence. In the distraction perception process, the DHL progressively refines the coarse structural region of the vascular structures to remove structural distractions. In the structural perception process, the DHL detects regions of inter-structural distractions in fused structure features then separates them. Extensive experiments on 361 subjects show that the DHL is effective (e.g., the average Dice is greater than 0.95), and superior to ten state-of-the-art IVUS vascular structure segmentation methods.

Validation of a new non-hyperemic physiological index: the constant-resistance ratio (cRR).

OBJECTIVES: The instantaneous wave-free ratio (iwFR) has limited availability. A new resting index called the constant-resistance ratio (cRR), which dynamically identifies cardiac intervals with constant and minimum resistance, has been developed; however, its diagnostic performance is unknown. The aim of this study was to validate the cRR by retrospectively calculating the cRR values from raw pressure waveforms of 2 publicly available datasets and compare them with those of the iwFR. METHODS: Waveform data from the CONTRAST and VERIFY 2 studies were used. The primary endpoint was Bland-Altman bias between cRR and iwFR. Secondary endpoints included diagnostic agreement, correlation, receiver operating characteristic (ROC) analysis, and success rates of cRR and iwFR. RESULTS: Among the 1036 waveforms, 871 were successful in determining paired cRR and iwFR values, while cRR was 6% more successful than iwFR (P less than .0001). The mean bias between cRR and iwFR was 0.003, with 95% limits of agreement [-0.021,0.028]. These 2 indices were highly correlated (r = 0.991; P less than .0001). Using an iwFR of 0.89 or less as the reference standard, the optimal cRR cutoff was 0.89, with an area under the ROC curve of 0.991 (P less than .001) and a diagnostic accuracy of 96.9% (95% CI [96%, 98%]). CONCLUSIONS: The cRR, a new resting index for identifying dynamic cardiac intervals with constant and minimum resistance, demonstrated high numerical agreement, diagnostic consistency, and a higher success rate than the iwFR based on the 2 publicly available datasets.

A novel de-escalation antiplatelet therapy for patients with acute coronary syndrome undergoing percutaneous coronary intervention.

To investigate the effect of different DAPTs in patients with ACS undergoing PCI, and to identify the most efficient DAPT to reduce the risk of ischemia and bleeding after PCI. Between March 2017 and December 2021, 1598 patients with ACS who underwent PCI were included in the study. The DAPT protocol included the clopidogrel group (aspirin 100 mg + clopidogrel 75 mg), ticagrelor group (aspirin 100 mg + ticagrelor 90 mg), de-escalation Group 1 (reduced dose of ticagrelor [from 90 mg to 60 mg]) after 3 months of oral DAPT [aspirin 100 mg + ticagrelor 90 mg]), and de-escalation Group 2 (switched from ticagrelor to clopidogrel after 3 months of oral DAPT [aspirin 100 mg + ticagrelor 90 mg]). All patients received a 12-month follow-up. The primary endpoint was net adverse clinical events (NACEs) that included the composite endpoints of cardiac death, myocardial infarction, ischemia-driven revascularization, stroke, and bleeding events. There were 2 secondary endpoints, major adverse cardiovascular and cerebrovascular events (MACCEs) and bleeding. No statistically significant difference was found in the incidence of NACEs between the 4 groups at the average 12-month follow-up (15.7% vs 19.2% vs 16.7% vs 20.4%). Cox regression analysis revealed that DAPT ticagrelor group regimen (hazard ratio [HR] 0.547; 95% confidence interval [CI]: 0.334-0.896; P  = .017) were associated with a lower risk of MACCEs. Age (HR 1.024; 95% CI: 1.003-1.046; P  = .022). DAPT de-escalation Group 2 regimen (HR 1.665; 95% CI: 1.001-2.767; P  = .049) were marginally associated with a higher risk of MACCEs. Ticagrelor group regimen (HR 1.856; 95% CI: 1.376-2.504; P  < .001) was associated with higher risk of bleeding events. Ticagrelor group regimen (HR 1.606; 95% CI: 1.179-2.187; P  = .003) were associated with a higher risk of minor bleeding events. For patients with ACS underwent PCI, there were no significant difference in the incidence of NACEs between 3 and 12 months after PCI between de-escalation and non-de-escalation therapies. Compared with ticagrelor-based 12-month DAPT, there was no significant difference in MACCEs and bleeding events in patients receiving de-escalation treatment (ticagrelor reduction from 90 to 60 mg, 3 months after PCI).

Vessel Contour Detection in Intracoronary Images via Bilateral Cross-Domain Adaptation.

Vessel contour detection (VCD) in intravascular images is important for the quantitative assessment of vessels. However, it is still a challenging task due to a high degree of morphology variability. Images from a single modality lack sufficient information on the vessel morphology due to the natural limitation of the imaging capability. Therefore, the single-modality VCD methods have difficulty extracting sufficient morphological information. Cross-modality methods have the potential to overcome morphology variability by extracting more information from different modalities. However, they still face the difficulty of the domain discrepancy, i.e., feature space discrepancy and label space inconsistency. In this paper, we aim to address the domain discrepancy for VCD. To overcome label space inconsistency, our method divides the label space into private label space and shared label space. It constructs subdomains for the private label space and the shared label space, and minimizes the task risk at the subdomain level. To overcome feature space discrepancy, it extracts domain-invariant features via domain adaptation between the subdomains. Finally, it uses the domain-invariant features as auxiliary information for each subdomain. Extensive experiments on 130 IVUS sequences (135663 images) and 124 OCT sequences (39857 images) show that our method is effective (e.g., the Dice index [Formula: see text] 0.949), and superior to the nineteen state-of-the-art VCD methods.

Progressive Perception Learning for Main Coronary Segmentation in X-Ray Angiography.

Main coronary segmentation from the X-ray angiography images is important for the computer-aided diagnosis and treatment of coronary disease. However, it confronts the challenge at three different image granularities (the semantic, surrounding, and local levels). The challenge includes the semantic confusion between the main and collateral vessels, low contrast between the foreground vessel and background surroundings, and local ambiguity near the vessel boundaries. The traditional hand-crafted feature-based methods may be insufficient because they may lack the semantic relationship information and may not distinguish the main and collateral vessels. The existing deep learning-based methods seem to have issues due to the deficiency in the long-distance semantic relationship capture, the foreground and background interference adaptability, and the boundary detail information preservation. To solve the main coronary segmentation challenge, we propose the progressive perception learning (PPL) framework to inspect these three different image granularities. Specifically, the PPL contains the context, interference, and boundary perception modules. The context perception is designed to focus on the main coronary vessel based on the semantic dependence capture among different coronary segments. The interference perception is designed to purify the feature maps based on the foreground vessel enhancement and background artifact suppression. The boundary perception is designed to highlight the boundary details based on boundary feature extraction through the intersection between the foreground and background predictions. Extensive experiments on 1085 subjects show that the PPL is effective (e.g., the overall Dice is greater than 95%), and superior to thirteen state-of-the-art coronary segmentation methods.

Scale Mutualized Perception for Vessel Border Detection in Intravascular Ultrasound Images.

Vessel border detection in IVUS images is essential for coronary disease diagnosis. It helps to obtain the clinical indices on the inner vessel morphology to indicate the stenosis. However, the existing methods suffer the challenge of scale-dependent interference. Early methods usually rely on the hand-crafted features, thus not robust to this interference. The existing deep learning methods are also ineffective to solve this challenge, because these methods aggregate multi-scale features in the top-down way. This aggregation may bring in interference from the non-adjacent scale. Besides, they only combine the features in all scales, and thus may weaken their complementary information. We propose the scale mutualized perception to solve this challenge by considering the adjacent scales mutually to preserve their complementary information. First, the adjacent small scales contain certain semantics to locate different vessel tissues. Then, they can also perceive the global context to assist the representation of the local context in the adjacent large scale, and vice versa. It helps to distinguish the objects with similar local features. Second, the adjacent large scales provide detailed information to refine the vessel boundaries. The experiments show the effectiveness of our method in 153 IVUS sequences, and its superiority to ten state-of-the-art methods.

Deep learning-based automated left ventricular ejection fraction assessment using 2-D echocardiography.

Deep learning (DL) has been applied for automatic left ventricle (LV) ejection fraction (EF) measurement, but the diagnostic performance was rarely evaluated for various phenotypes of heart disease. This study aims to evaluate a new DL algorithm for automated LVEF measurement using two-dimensional echocardiography (2DE) images collected from three centers. The impact of three ultrasound machines and three phenotypes of heart diseases on the automatic LVEF measurement was evaluated. Using 36890 frames of 2DE from 340 patients, we developed a DL algorithm based on U-Net (DPS-Net) and the biplane Simpson's method was applied for LVEF calculation. Results showed a high performance in LV segmentation and LVEF measurement across phenotypes and echo systems by using DPS-Net. Good performance was obtained for LV segmentation when DPS-Net was tested on the CAMUS data set (Dice coefficient of 0.932 and 0.928 for ED and ES). Better performance of LV segmentation in study-wise evaluation was observed by comparing the DPS-Net v2 to the EchoNet-dynamic algorithm (P = 0.008). DPS-Net was associated with high correlations and good agreements for the LVEF measurement. High diagnostic performance was obtained that the area under receiver operator characteristic curve was 0.974, 0.948, 0.968, and 0.972 for normal hearts and disease phenotypes including atrial fibrillation, hypertrophic cardiomyopathy, dilated cardiomyopathy, respectively. High performance was obtained by using DPS-Net in LV detection and LVEF measurement for heart failure with several phenotypes. High performance was observed in a large-scale dataset, suggesting that the DPS-Net was highly adaptive across different echocardiographic systems.NEW & NOTEWORTHY A new strategy of feature extraction and fusion could enhance the accuracy of automatic LVEF assessment based on multiview 2-D echocardiographic sequences. High diagnostic performance for the determination of heart failure was obtained by using DPS-Net in cases with different phenotypes of heart diseases. High performance for left ventricle segmentation was obtained by using DPS-Net, suggesting the potential for a wider range of application in the interpretation of 2DE images.

Multicenter clinical evaluation of a piezoresistive-MEMS-sensor rapid-exchange pressure microcatheter system for fractional flow reserve measurement.

OBJECTIVES: This multicenter, prospective clinical study investigates whether the microelectromechanical-systems-(MEMS)-sensor pressure microcatheter (MEMS-PMC) is comparable to a conventional pressure wire in fractional flow reserve (FFR) measurement. BACKGROUND: As a conventional tool for FFR measurement, pressure wires (PWs) still have some limitations such as suboptimal handling characteristics and unable to maintain the wire position during pullback assessment. Recently, a MEMS-PMC compatible with any 0.014″ guidewire is developed. Compared with the existing optical-sensor PMC, this MEMS-PMC has smaller profiles at both the lesion crossing and sensor packaging areas. METHODS: Two hundred and forty-two patients with visually 30-70% coronary stenosis were enrolled at four centers. FFR was measured first with the MEMS-PMC, and then with the PW. The primary endpoint was the Bland-Altman mean bias between the MEMS-PMC and PW FFR. RESULTS: From the 224-patient per-protocol data, quantitative coronary angiography showed 17.9% and 55.9% vessels had diameter < 2.5 mm and stenosis >50%, respectively. The two systems' mean bias was -0.01 with [-0.08, 0.06] 95% limits-of-agreement. Using PW FFR≤0.80 as cutoff, the MEMS-PMC per-vessel diagnostic accuracy was 93.4% [95% confidence interval: 89.4-96.3%]. The MEMS-PMC's success rate was similar to that of PW (97.5 vs. 96.3%, p = .43) with no serious adverse event, and its clinically-significant (>0.03) drift rate was 43% less (9.5 vs. 16.7%, p = .014). CONCLUSIONS: Our study showed the MEMS-PMC is safe to use and has a minimal bias equal to the resolution of current FFR systems. Given the MEMS-PMC's high measurement accuracy and rapid-exchange nature, it may become an attractive new tool facilitating routine coronary physiology assessment.

Privileged Modality Distillation for Vessel Border Detection in Intracoronary Imaging.

Intracoronary imaging is a crucial imaging technology in coronary disease diagnosis as it visualizes the internal tissue morphologies of coronary arteries. Vessel border detection in intracoronary images (VBDI) is desired because it can help the succeeding procedures of computer-aided disease diagnosis. However, existing VDBI methods suffer from the challenge of vessel-environment variability (i.e. high intra- and inter-subject diversity of vessels and their surrounding tissues appeared in images). This challenge leads to the ineffectiveness in the vessel region representation for hand-crafted features, in the receptive field extraction for deeply-represented features, as well as performance suppression derived from clinical data limitation. To solve this challenge, we propose a novel privileged modality distillation (PMD) framework for VBDI. PMD transforms the single-input-single-task (SIST) learning problem in the single-mode VBDI to a multiple-input-multiple-task (MIMT) problem by using the privileged image modality to help the learning model in the target modality. This learns the enriched high-level knowledge with similar semantics and generalizes PMD on diversity-increased low-level image features for improving the model adaptation to diverse vessel environments. Moreover, PMD refines MIMT to SIST by distilling the learned knowledge from multiple to one modality. This eliminates the reliance on privileged modality in the test phase, and thus enables the applicability to each of different intracoronary modalities. A structure-deformable neural network is proposed as an elaborately-designed implementation of PMD. It expands a conventional SIST network structure to the MIMT structure, and then recovers it to the final SIST structure. The PMD is validated on intravascular ultrasound imaging and optical coherence tomography imaging. One modality is the target, and the other one can be considered as the privileged modality owing to their semantic relatedness. The experiments show that our PMD is effective in VBDI (e.g. the Dice index is larger than 0.95), as well as superior to six state-of-the-art VBDI methods.

Evaluation of fractional flow reserve in patients with stable angina: can CT compete with angiography?

BACKGROUND: We aimed to compare the performance of FFRCT and FFRQCA in assessing the functional significance of coronary artery stenosis in patients suffering from coronary artery disease with stable angina. METHOD: A total of 101 stable coronary heart disease (CAD) patients with 181 lesions were recruited. FFRCT and FFRQCA were compared using invasive fractional flow reserve (FFR) as a reference standard. Comparisons between FFRCT and FFRQCA were conducted based on strategies of the geometric reconstruction, boundary conditions, and geometric characteristics. The performance of FFRCT and FFRQCA in detecting hemodynamic significance was also investigated. RESULTS: The performance of FFRCT and FFRQCA in discriminating hemodynamically significant lesions was compared. Good correlation and agreement with invasive FFR was found using FFRCT and FFRQCA (r = 0.809, p < 0.001 and r = 0.755, p < 0.001). A significant difference was observed in the complex coronary artery tree, in which relatively better prediction was observed using FFRCT than FFRQCA when analyzing the stenosis distributed in the middle segment of a stenotic branch (p = 0.036). Moreover, FFRCT was found to be better at predicting hemodynamically insignificant stenosis than FFRQCA (p = 0.007), while the performance of the two parameters was similar in discriminating functional significant lesions using an FFR threshold of ≤ 0.8 as a reference standard. CONCLUSION: FFRCT and FFRQCA could both accurately rule out functional insignificant lesions in stable CAD patients. FFRCT was found to be better for the noninvasive screening of CAD patients with stable angina than FFRQCA. KEY POINTS: • FFR CT and FFR QCA were both in good correlation and agreement with invasive FFR measurements. • FFR CT is superior in accuracy and consistency compared to FFR QCA in patients with stenoses distributed in left coronary artery. • The noninvasive nature of FFR CT could provide potential benefit for stable CAD patients on disease management.

Analysis of beat-to-beat blood pressure variability response to the cold pressor test in the offspring of hypertensive and normotensive parents.

Abnormal autonomic nervous regulation has an important role in the development of hypertension. As to whether blood pressure (BP) or BP variability represents the proper characteristics for predisposition to hypertension in Chinese young adults remains controversial. We studied the properties of the indices extracted from beat-to-beat BP during a 13 min cold pressor test (CPT). In this study, 69 Chinese young adults including 34 offspring of hypertensive parents (OHPs; 25.6±2.5 years) and 35 offspring of normotensive parents (ONPs; 25.3±2.3 years) were analyzed. We assessed the differences between the two groups regarding mean beat-to-beat BP and variability indices. Beat-to-beat BP variability indices included time-domain indices and frequency-domain indices. Our results showed that the differences in beat-to-beat systolic BP and mean BP levels between the OHPs and the ONPs were statistically significant (P<0.05). Furthermore, more BP variability indices in the frequency domain were significantly different between the two groups. We concluded that BP variability was superior to BP as an index to evaluate the cardiovascular and sympathetic reactivity to the CPT. Moreover, compared with time-domain BP variability, we found more differences in frequency-domain BP variability between the two groups, thus indicating that frequency-domain BP variability may be a potential index of predisposition to hypertension.

An artificial neural network method for lumen and media-adventitia border detection in IVUS.

Intravascular ultrasound (IVUS) has been well recognized as one powerful imaging technique to evaluate the stenosis inside the coronary arteries. The detection of lumen border and media-adventitia (MA) border in IVUS images is the key procedure to determine the plaque burden inside the coronary arteries, but this detection could be burdensome to the doctor because of large volume of the IVUS images. In this paper, we use the artificial neural network (ANN) method as the feature learning algorithm for the detection of the lumen and MA borders in IVUS images. Two types of imaging information including spatial, neighboring features were used as the input data to the ANN method, and then the different vascular layers were distinguished accordingly through two sparse auto-encoders and one softmax classifier. Another ANN was used to optimize the result of the first network. In the end, the active contour model was applied to smooth the lumen and MA borders detected by the ANN method. The performance of our approach was compared with the manual drawing method performed by two IVUS experts on 461 IVUS images from four subjects. Results showed that our approach had a high correlation and good agreement with the manual drawing results. The detection error of the ANN method close to the error between two groups of manual drawing result. All these results indicated that our proposed approach could efficiently and accurately handle the detection of lumen and MA borders in the IVUS images.

Efficacy of an early home-based cardiac rehabilitation program for patients after acute myocardial infarction: A three-dimensional speckle tracking echocardiography randomized trial.

BACKGROUND: The effect of an early short-term home-based cardiac rehabilitation (CR) program on ventricular function in acute myocardial infarction (AMI) patients is not yet clear. The purpose of this study was to evaluate the efficacy of our CR program on the improvement of myocardial function using three-dimensional speckle tracking echocardiography (3D-STE) in AMI patients. METHODS: Fifty-two AMI patients were randomly assigned to 2 groups after discharge: the rehabilitation group, which participated in an early, home-based CR program, and the control group, which received only usual care. All subjects in both groups underwent 3D-STE examinations of the left ventricle within 48 hours of percutaneous coronary intervention and again 4 weeks after discharge. Global longitudinal strain (GLS), global radial strain (GRS), global area strain (GAS), global circumferential strain (GCS), left ventricular ejection fraction (LVEF), and segmental strains were computed. The CR program was initially conducted with supervised inpatient training, followed by an unsupervised home-based training program during a 4-week follow-up. RESULTS: We obtained segmental strains from 832 segments, of which 319 were defined as interventional segments, 179 as ischemic segments, and the remaining segments as normal segments. At the 4-week follow-up, when controlling for baseline values, the rehabilitation group showed significant improvements in GLS, GRS, GCS, GAS, LVEF, and in all of the segmental strains of the 3 subgroups compared with the control group (P <0.05). CONCLUSION: Our study suggests that an early, home-based CR program can greatly improve the ventricular function of AMI patients in a short period of time.

Three-dimensional hemodynamics analysis of the circle of Willis in the patient-specific nonintegral arterial structures.

The hemodynamic alteration in the cerebral circulation caused by the geometric variations in the cerebral circulation arterial network of the circle of Wills (CoW) can lead to fatal ischemic attacks in the brain. The geometric variations due to impairment in the arterial network result in incomplete cerebral arterial structure of CoW and inadequate blood supply to the brain. Therefore, it is of great importance to understand the hemodynamics of the CoW, for efficiently and precisely evaluating the status of blood supply to the brain. In this paper, three-dimensional computational fluid dynamics of the main CoW vasculature coupled with zero-dimensional lumped parameter model boundary condition for the CoW outflow boundaries is developed for analysis of the blood flow distribution in the incomplete CoW cerebral arterial structures. The geometric models in our study cover the arterial segments from the aorta to the cerebral arteries, which can allow us to take into account the innate patient-specific resistance of the arterial trees. Numerical simulations of the governing fluid mechanics are performed to determine the CoW arterial structural hemodynamics, for illustrating the redistribution of the blood flow in CoW due to the structural variations. We have evaluated our coupling methodology in five patient-specific cases that were diagnosed with the absence of efferent vessels or impairment in the connective arteries in their CoWs. The velocity profiles calculated by our approach in the segments of the patient-specific arterial structures are found to be very close to the Doppler ultrasound measurements. The accuracy and consistency of our hemodynamic results have been improved (to [Formula: see text] %) compared to that of the pure-resistance boundary conditions (of 43.5 [Formula: see text] 28 %). Based on our grouping of the five cases according to the occurrence of unilateral occlusion in vertebral arteries, the inter-comparison has shown that (i) the flow reduction in posterior cerebral arteries is the consequence of the unilateral vertebral arterial occlusion, and (ii) the flow rate in the anterior cerebral arteries is correlated with the posterior structural variations. This study shows that our coupling approach is capable of providing comprehensive information of the hemodynamic alterations in the pathological CoW arterial structures. The information generated by our methodology can enable evaluation of both the functional and structural status of the clinically significant symptoms, for assisting the treatment decision-making.

Automated Framework for Detecting Lumen and Media-Adventitia Borders in Intravascular Ultrasound Images.

An automated framework for detecting lumen and media-adventitia borders in intravascular ultrasound images was developed on the basis of an adaptive region-growing method and an unsupervised clustering method. To demonstrate the capability of the framework, linear regression, Bland-Altman analysis and distance analysis were used to quantitatively investigate the correlation, agreement and spatial distance, respectively, between our detected borders and manually traced borders in 337 intravascular ultrasound images in vivo acquired from six patients. The results of these investigations revealed good correlation (r = 0.99), good agreement (>96.82% of results within the 95% confidence interval) and small average distance errors (lumen border: 0.08 mm, media-adventitia border: 0.10 mm) between the borders generated by the automated framework and the manual tracing method. The proposed framework was found to be effective in detecting lumen and media-adventitia borders in intravascular ultrasound images, indicating its potential for use in routine studies of vascular disease.

Automated detection framework of the calcified plaque with acoustic shadowing in IVUS images.

Intravascular Ultrasound (IVUS) is one ultrasonic imaging technology to acquire vascular cross-sectional images for the visualization of the inner vessel structure. This technique has been widely used for the diagnosis and treatment of coronary artery diseases. The detection of the calcified plaque with acoustic shadowing in IVUS images plays a vital role in the quantitative analysis of atheromatous plaques. The conventional method of the calcium detection is manual drawing by the doctors. However, it is very time-consuming, and with high inter-observer and intra-observer variability between different doctors. Therefore, the computer-aided detection of the calcified plaque is highly desired. In this paper, an automated method is proposed to detect the calcified plaque with acoustic shadowing in IVUS images by the Rayleigh mixture model, the Markov random field, the graph searching method and the prior knowledge about the calcified plaque. The performance of our method was evaluated over 996 in-vivo IVUS images acquired from eight patients, and the detected calcified plaques are compared with manually detected calcified plaques by one cardiology doctor. The experimental results are quantitatively analyzed separately by three evaluation methods, the test of the sensitivity and specificity, the linear regression and the Bland-Altman analysis. The first method is used to evaluate the ability to distinguish between IVUS images with and without the calcified plaque, and the latter two methods can respectively measure the correlation and the agreement between our results and manual drawing results for locating the calcified plaque in the IVUS image. High sensitivity (94.68%) and specificity (95.82%), good correlation and agreement (>96.82% results fall within the 95% confidence interval in the Student t-test) demonstrate the effectiveness of the proposed method in the detection of the calcified plaque with acoustic shadowing in IVUS images.

Virtual histology by intravascular ultrasound study on degenerative aortocoronary saphenous vein grafts.

Data of virtual histology (VH) acquired by intravascular ultrasound (IVUS) on saphenous vein graft (SVG) lesions is lacking. This study sought to report the VH IVUS findings in degenerative aortocoronary SVG lesions and correlate various types of plaque compositions (fibrous, fibro-fatty, dense calcium, and necrotic core) with different clinical and lesion characteristics. Virtual histology IVUS was performed on SVG in 38 symptomatic patients with a history of coronary artery bypass grafting, who underwent percutaneous coronary intervention on either native vessels or SVG. Measurements were made at the image slice with the smallest lumen. A total of 54 SVG lesions were analyzed; the mean graft age was 13.7 +/- 4.0 years. The mean vessel size was 5.0 +/- 1.0 mm; plaque area was 13.4 +/- 7.3 mm(2), and plaque burden was 63.0% +/- 15.0%. Fibrous tissue represented the major plaque component (62.1% +/- 17.1%). Lesions with a plaque burden of >or=70% were associated with positive remodeling, larger vessel size, higher percentage of fibro-fatty tissue, but lower percentage of dense calcium. Plaque burden was found to be positively correlated with remodeling index (r = 0.37, P = 0.01) and % fibro-fatty tissue (r = 0.49, P < 0.001) but negatively correlated with % dense calcium (r= -0.31, P = 0.03). The severity of SVG atherosclerosis paralleled with a proportional increase in fibro-fatty tissue. Unstable plaques in SVG were associated with positive remodeling, lipid-rich atheroma, and less calcium deposition, similar to the VH IVUS findings in native coronary arteries.

Fractional flow reserve and complex coronary intervention.

This paper aims to provide a short overview of the theoretical background for coronary pressure-derived fractional flow reserve (FFR) index and to focus on its clinical applicability in complex coronary intervention.