Prediction of stent under-expansion in calcified coronary arteries using machine learning on intravascular optical coherence tomography images.

It can be difficult/impossible to fully expand a coronary artery stent in a heavily calcified coronary artery lesion. Under-expanded stents are linked to later complications. Here we used machine/deep learning to analyze calcifications in pre-stent intravascular optical coherence tomography (IVOCT) images and predicted the success of vessel expansion. Pre- and post-stent IVOCT image data were obtained from 110 coronary lesions. Lumen and calcifications in pre-stent images were segmented using deep learning, and lesion features were extracted. We analyzed stent expansion along the lesion, enabling frame, segmental, and whole-lesion analyses. We trained regression models to predict the post-stent lumen area and then computed the stent expansion index (SEI). Best performance (root-mean-square-error = 0.04 ± 0.02 mm2, r = 0.94 ± 0.04, p < 0.0001) was achieved when we used features from both lumen and calcification to train a Gaussian regression model for segmental analysis of 31 frames in length. Stents with minimum SEI > 80% were classified as "well-expanded;" others were "under-expanded." Under-expansion classification results (e.g., AUC = 0.85 ± 0.02) were significantly improved over a previous, simple calculation, as well as other machine learning solutions. Promising results suggest that such methods can identify lesions at risk of under-expansion that would be candidates for intervention lesion preparation (e.g., atherectomy).

Postural and longitudinal variability in seismocardiographic signals.

Objective. Low frequency cardiovascular vibrations detectable on the chest surface (termed seismocardiography or SCG) may be useful for non-invasive diagnosis and monitoring of various cardiovascular conditions. A potential limitation of using SCG for longitudinal patient monitoring is the existence of intra-subject variability, which can contribute to errors in calculating SCG features. Improved understanding of the contribution of intra-subject variability sources may lead to improved SCG utility. This study aims to quantify postural and longitudinal SCG variability in healthy resting subjects during normal breathing.Approach. SCG and ECG signals were longitudinally acquired in 19 healthy subjects at different postures (supine, 45° head up, and sitting) during five recording sessions over five months. SCG cycles were segmented using the ECG R wave. Unsupervised machine learning was used to reduce SCG variability due to respiration by grouping the SCG signals into two clusters with minimized intra-cluster waveform heterogeneity. Several SCG features were assessed at different postures and longitudinally.Main results. SCG waveform morphological variability was calculated within each cluster (intra-cluster) and between two clusters (inter-cluster) at each posture and data collection session. The variabilities were significantly different between the supine and sitting but not between supine and 45° postures. For the 45° and sitting postures, the intra-cluster variability was not significantly different, while the inter-cluster variability difference was significant. The energy ratio between different frequency bands to total spectral energy in 0.5-50 Hz were calculated and were comparable for all postures. The combined cardiac timing intervals from the two clusters showed significant variation with postural changes. There was significant heart rate difference between the clusters and between postural positions. The SCG features were compared between longitudinal sessions and all features were not significantly different,Significance. Several SCG features significantly varied with posture suggesting that posture needs to be specified when comparing SCG changes over time. Longitudinally comparable SCG feature values suggests that significant longitudinal differences, if observed, may reflect true alternations in the cardiac functioning over time.

Fractional Flow Reserve (FFR) Estimation from OCT-Based CFD Simulations: Role of Side Branches.

The computational fluid dynamic method has been widely used to quantify the hemodynamic alterations in a diseased artery and investigate surgery outcomes. The artery model reconstructed based on optical coherence tomography (OCT) images generally does not include the side branches. However, the side branches may significantly affect the hemodynamic assessment in a clinical setting, i.e., the fractional flow reserve (FFR), defined as the ratio of mean distal coronary pressure to mean aortic pressure. In this work, the effect of the side branches on FFR estimation was inspected with both idealized and optical coherence tomography (OCT)-reconstructed coronary artery models. The electrical analogy of blood flow was further used to understand the impact of the side branches (diameter and location) on FFR estimation. Results have shown that the side branches decrease the total resistance of the vessel tree, resulting in a higher inlet flowrate. The side branches located at the downstream of the stenosis led to a lower FFR value, while the ones at the upstream had a minimal impact on the FFR estimation. Side branches with a diameter larger than one third of the main vessel diameter are suggested to be considered for a proper FFR estimation. The findings in this study could be extended to other coronary artery imaging modalities and facilitate treatment planning.

Upper Airway Flow Dynamics in Obstructive Sleep Apnea Patients with Various Apnea-Hypopnea Index.

BACKGROUND AND AIM: This study evaluates the upper airway flow characteristics, anatomical features and analyzes their correlations with AHI in patients with varied degrees of OSA severity seeking for discernments of the underlying pathophysiological profile. MATERIALS AND METHODS: Patient-specific computational fluid dynamics models were reconstructed from high-resolution cone-beam computed tomography images for 4 OSA patients classified as minimal, mild, moderate, and severe according to AHI. RESULTS: The parameters, minimal cross-sectional area (MCA), and the pharyngeal airway volume did not show clear correlations with the OSA severity defined according to AHI. No correlations were found between the classically defined resistance of the airway in terms of pressure drop and AHI. The flow analysis further showed that the fluid mechanisms likely to cause airway collapse are associated with the degree of narrowing in the pharyngeal airway rather than AHI. Results also suggested that some patients classified as severe OSA according to the AHI can show less susceptibility to airway collapse than patients with relatively lower AHI values and vice versa. CONCLUSIONS: The relative contribution of anatomical and non-anatomical causes to the OSA severity can significantly vary between patients. AHI alone is inadequate to be used as a marker of the pathophysiological profile of OSA. Combining airflow analysis with AHI in diagnosing OSA severity may provide additional details about the underlying pathophysiology, subsequently improving the individualized clinical outcomes.

Hemodynamic alternations following stent deployment and post-dilation in a heavily calcified coronary artery: In silico and ex-vivo approaches.

In this work, hemodynamic alterations in a patient-specific, heavily calcified coronary artery following stent deployment and post-dilations are quantified using in silico and ex-vivo approaches. Three-dimensional artery models were reconstructed from OCT images. Stent deployment and post-dilation with various inflation pressures were performed through both the finite element method (FEM) and ex vivo experiments. Results from FEM agreed very well with the ex-vivo measurements, interms of lumen areas, stent underexpansion, and strut malapposition. In addition, computational fluid dynamics (CFD) simulations were performed to delineate the hemodynamic alterations after stent deployment and post-dilations. A pressure time history at the inlet and a lumped parameter model (LPM) at the outlet were adopted to mimic the aortic pressure and the distal arterial tree, respectively. The pressure drop across the lesion, pertaining to the clinical measure of instantaneous wave-free flow ratio (iFR), was investigated. Results have shown that post-dilations are necessary for the lumen gain as well as the hemodynamic restoration towards hemostasis. Malapposed struts induced much higher shear rate, flow disturbances and lower time-averaged wall shear stress (TAWSS) around struts. Post-dilations mitigated the strut malapposition, and thus the shear rate. Moreover, stenting induced larger area of low TAWSS (<0.4 Pa) and lager volume of high shear rate (>2000 s-1), indicating higher risks of in-stent restenosis (ISR) and stent thrombosis (ST), respectively. Oscillatory shear index (OSI) and relative residence time (RRT) indicated the wall regions more prone to ISR are located near the malapposed stent struts.

Spectral Decomposition of the Flow and Characterization of the Sound Signals through Stenoses with Different Levels of Severity.

Treatments of atherosclerosis depend on the severity of the disease at the diagnosis time. Non-invasive diagnosis techniques, capable of detecting stenosis at early stages, are essential to reduce associated costs and mortality rates. We used computational fluid dynamics and acoustics analysis to extensively investigate the sound sources arising from high-turbulent fluctuating flow through stenosis. The frequency spectral analysis and proper orthogonal decomposition unveiled the frequency contents of the fluctuations for different severities and decomposed the flow into several frequency bandwidths. Results showed that high-intensity turbulent pressure fluctuations appeared inside the stenosis for severities above 70%, concentrated at plaque surface, and immediately in the post-stenotic region. Analysis of these fluctuations with the progression of the stenosis indicated that (a) there was a distinct break frequency for each severity level, ranging from 40 to 230 Hz, (b) acoustic spatial-frequency maps demonstrated the variation of the frequency content with respect to the distance from the stenosis, and (c) high-energy, high-frequency fluctuations existed inside the stenosis only for severe cases. This information can be essential for predicting the severity level of progressive stenosis, comprehending the nature of the sound sources, and determining the location of the stenosis with respect to the point of measurements.

Spectral Decomposition and Sound Source Localization of Highly Disturbed Flow through a Severe Arterial Stenosis.

For the early detection of atherosclerosis, it is imperative to explore the capabilities of new, effective noninvasive diagnosis techniques to significantly reduce the associated treatment costs and mortality rates. In this study, a multifaceted comprehensive approach involving advanced computational fluid dynamics combined with signal processing techniques was exploited to investigate the highly turbulent fluctuating flow through arterial stenosis. The focus was on localizing high-energy mechano-acoustic source potential to transmit to the epidermal surface. The flow analysis results showed the existence of turbulent pressure fluctuations inside the stenosis and in the post-stenotic region. After analyzing the turbulent kinetic energy and pressure fluctuations on the flow centerline and the vessel wall, the point of maximum excitation in the flow was observed around two diameters downstream of the stenosis within the fluctuating zone. It was also found that the concentration of pressure fluctuation closer to the wall was higher inside the stenosis compared to the post-stenotic region. Additionally, the visualization of the most energetic proper orthogonal decomposition (POD) mode and spectral decomposition of the flow indicated that the break frequencies ranged from 80 to 220 Hz and were correlated to the eddies generated within these regions.

Geometric features of pig airways using computed tomography.

Accurate knowledge of the airway geometry is needed when constructing physical models of the airway tree and for numerical modeling of flow or sound propagation in the airways. Human and animal experiments are conducted to validate these models. Many studies documented the geometric details of the human airways. However, information about the geometry of pig airways is scarcer. Earlier studies suggested that the morphology of animal airways can be significantly different from that of humans. The objective of this study is to measure the airway diameter, length and bifurcation angles in domestic pigs using computed tomography. In this study, lungs of six pigs were imaged, then segmentation software tools were used to extract the geometry of the airway lumen. The airway dimensions were measured from the resulting 3-D models for the first 24 airway generations. Results showed that the size and morphology of the airways of the six pigs were similar. The trachea diameters were found to be comparable to the typical human adult, but the diameter, length and branching angles of other airways were noticeably different from that of humans. For example, pig airways consistently had an early branching from the trachea that feeds the top right lung lobe and precedes the main carina. This branch is absent in the human airways. The results suggested that the pig airways geometry may not be accurately approximated by human airways and this approximation may contribute to increasing the errors in computational models of the pig chest.