Novel feature-based visualization of the unsteady blood flow in intracranial aneurysms with the help of proper orthogonal decomposition (POD)

4D Flow Patterns and Relative Pressure Distribution in a Left Ventricle Model by Shake-the-Box and Proper Orthogonal Decomposition Analysis

PURPOSE

Intraventricular blood flow dynamics are associated with cardiac function. Accurate, noninvasive, and easy assessments of hemodynamic quantities (such as velocity, vortex, and pressure) could be an important addition to the clinical diagnosis and treatment of heart diseases. However, the complex time-varying flow brings many challenges to the existing noninvasive image-based hemodynamic assessments. The development of reliable techniques and analysis tools is essential for the application of hemodynamic biomarkers in clinical practice.

METHODS

In this study, a time-resolved particle tracking method, Shake-the-Box, was applied to reconstruct the flow in a realistic left ventricle (LV) silicone model with biological valves. Based on the obtained velocity, 4D pressure field was calculated using a Poisson equation-based pressure solver. Furthermore, flow analysis by proper orthogonal decomposition (POD) of the 4D velocity field has been performed.

RESULTS

As a result of the Shake-the-Box algorithm, we have extracted: (i) particle positions, (ii) particle tracks, and finally, (iii) 4D velocity fields. From the latter, the temporal evolution of the 3D pressure field during the full cardiac cycle was obtained. The obtained maximal pressure difference extracted along the base-to-apex was about 2.7 mmHg, which is in good agreement with those reported in vivo. The POD analysis results showed a clear picture of different scale of vortices in the pulsatile LV flow, together with their time-varying information and corresponding kinetic energy content. To reconstruct 95% of the kinetic energy of the LV flow, only the first six POD modes would be required, leading to significant data reduction.

CONCLUSIONS

This work demonstrated Shake-the-Box is a promising technique to accurately reconstruct the left ventricle flow field in vitro. The good spatial and temporal resolutions of the velocity measurements enabled a 4D reconstruction of the pressure field in the left ventricle. The application of POD analysis showed its potential in reducing the complexity of the high-resolution left ventricle flow measurements. For future work, image analysis, multi-modality flow assessments, and the development of new flow-derived biomarkers can benefit from fast and data-reducing POD analysis.

Body Acoustics for the Non-Invasive Diagnosis of Medical Conditions

In the past few decades, many non-invasive monitoring methods have been developed based on body acoustics to investigate a wide range of medical conditions, including cardiovascular diseases, respiratory problems, nervous system disorders, and gastrointestinal tract diseases. Recent advances in sensing technologies and computational resources have given a further boost to the interest in the development of acoustic-based diagnostic solutions. In these methods, the acoustic signals are usually recorded by acoustic sensors, such as microphones and accelerometers, and are analyzed using various signal processing, machine learning, and computational methods. This paper reviews the advances in these areas to shed light on the state-of-the-art, evaluate the major challenges, and discuss future directions. This review suggests that rigorous data analysis and physiological understandings can eventually convert these acoustic-based research investigations into novel health monitoring and point-of-care solutions.

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.

Proper Orthogonal Decomposition Analysis of the Flow Downstream of a Dysfunctional Bileaflet Mechanical Aortic Valve

PURPOSE

Aortic valve replacement remains the only viable solution for symptomatic patients with severe aortic valve stenosis. Despite their improved design and long history of successful operation, bileaflet mechanical heart valves are still associated with post-operative complications leading to valve dysfunction. Thus, the flow dynamics can be highly disturbed downstream of the dysfunctional valve.

METHODS

In this in vitro study, the flow dynamics downstream of healthy and dysfunctional bileaflet mechanical heart valves have been investigated using particle image velocimetry measurements. Proper orthogonal decomposition of the velocity field has been performed in order to explore the coherent flow features in the ascending aorta in the presence of a dysfunctional bileaflet mechanical heart valve.

RESULTS

The ability of proper orthogonal decomposition derived metrics to differentiate between heathy and dysfunctional cases is reported. Moreover, reduced-order modeling using proper orthogonal decomposition is thoroughly investigated not only for the velocity field but also for higher order flow characteristics such as time average wall shear stress, oscillatory shear index and viscous energy dissipation.

CONCLUSION

Considering these results, proper orthogonal decomposition can provide a rapid binary classifier to evaluate if the bileaflet mechanical valve deviates from its normal operating conditions. Moreover, the study shows that the size of the reduced-order model depends on which flow parameter is required to be reconstructed.

Dynamic modes of inflow jet in brain aneurysms

The transition of the inflow jet to turbulence is crucial in understanding the pathology of brain aneurysms. Previous works Le et al. (2010, 2013) have shown evidence for a highly dynamic inflow jet in the ostium of brain aneurysms. While it is highly desired to investigate this inflow jet dynamics in clinical practice, the constraints on spatial and temporal resolutions of in vivo data do not allow a detailed analysis of this transition. In this work, Dynamic Mode Decomposition (DMD) is used to identify the most energetic modes of the inflow jet in patient-specific models of internal carotid aneurysms via the utilization of high-resolution simulation data. It is hypothesized that dynamic modes are not solely controlled by the blood flow waveform at the parent artery. They are also dependent on jet-wall interaction phenomena. DMD analysis shows that the spatial extent of low- frequency modes corresponds well to the most energetic areas of the inflow jet. The high-frequency modes are short-lived and correspond to the flow separation at the proximal neck and the jet's impingement onto the aneurysmal wall. Low-frequency modes can be reconstructed at relatively low spatial and temporal resolutions comparable to ones of in vivo data. The current results suggest that DMD can be practically useful in analyzing blood flow patterns of brain aneurysms with in vivo data.