Super-resolution Left Ventricular Flow and Pressure Mapping by Navier-Stokes-Informed Neural Networks.

Intraventricular vector flow mapping (VFM) is a growingly adopted echocardiographic modality that derives time-resolved two-dimensional flow maps in the left ventricle (LV) from color-Doppler sequences. Current VFM models rely on kinematic constraints arising from planar flow incompressibility. However, these models are not informed by crucial information about flow physics; most notably the pressure and shear forces within the fluid and the resulting accelerations. This limitation has rendered VFM unable to combine information from different time frames in an acquisition sequence or derive fluctuating pressure maps. In this study, we leveraged recent advances in artificial intelligence (AI) to develop AI-VFM, a vector flow mapping modality that uses physics-informed neural networks (PINNs) encoding mass conservation and momentum balance inside the LV, and no-slip boundary conditions at the LV endocardium. AI-VFM recovers the flow and pressure fields in the LV from standard echocardiographic scans. It performs phase unwrapping and recovers flow data in areas without input color-Doppler data. AI-VFM also recovers complete flow maps at time points without color-Doppler input data, producing super-resolution flow maps. We show that informing the PINNs with momentum balance is essential to achieving temporal super-resolution and significantly increases the accuracy of AI-VFM compared to informing the PINNs only with mass conservation. AI-VFM is solely informed by each patient's flow physics; it does not utilize explicit smoothness constraints or incorporate data from other patients or flow models. AI-VFM takes 15 minutes to run in off-the-shelf graphics processing units and its underlying PINN framework could be extended to map other flow-associated metrics like blood residence time or the concentration of coagulation species.

Deriving Explainable Metrics of Left Ventricular Flow by Reduced-Order Modeling and Classification.

Background: Extracting explainable flow metrics is a bottleneck to the clinical translation of advanced cardiac flow imaging modalities. We hypothesized that reduced-order models (ROMs) of intraventricular flow are a suitable strategy for deriving simple and interpretable clinical metrics suitable for further assessments. Combined with machine learning (ML) flow-based ROMs could provide new insight to help diagnose and risk-stratify patients. Methods: We analyzed 2D color-Doppler echocardiograms of 81 non-ischemic dilated cardiomyopathy (DCM) patients, 51 hypertrophic cardiomyopathy (HCM) patients, and 77 normal volunteers (Control). We applied proper orthogonal decomposition (POD) to build patient-specific and cohort-specific ROMs of LV flow. Each ROM aggregates a low number of components representing a spatially dependent velocity map modulated along the cardiac cycle by a time-dependent coefficient. We tested three classifiers using deliberately simple ML analyses of these ROMs with varying supervision levels. In supervised models, hyperparameter gridsearch was used to derive the ROMs that maximize classification power. The classifiers were blinded to LV chamber geometry and function. We ran vector flow mapping on the color-Doppler sequences to help visualize flow patterns and interpret the ML results. Results: POD-based ROMs stably represented each cohort through 10-fold cross-validation. The principal POD mode captured >80% of the flow kinetic energy (KE) in all cohorts and represented the LV filling/emptying jets. Mode 2 represented the diastolic vortex and its KE contribution ranged from <1% (HCM) to 13% (DCM). Semi-unsupervised classification using patient-specific ROMs revealed that the KE ratio of these two principal modes, the vortex-to-jet (V2J) energy ratio, is a simple, interpretable metric that discriminates DCM, HCM, and Control patients. Receiver operating characteristic curves using V2J as classifier had areas under the curve of 0.81, 0.91, and 0.95 for distinguishing HCM vs. Control, DCM vs. Control, and DCM vs. HCM, respectively. Conclusions: Modal decomposition of cardiac flow can be used to create ROMs of normal and pathological flow patterns, uncovering simple interpretable flow metrics with power to discriminate disease states, and particularly suitable for further processing using ML.

Intraventricular Flow Patterns in Patients Treated with Left Ventricular Assist Devices.

The success of left ventricular assist device (LVAD) therapy is hampered by complications such as thrombosis and bleeding. Understanding blood flow interactions between the heart and the LVAD might help optimize treatment and decrease complication rates. We hypothesized that LVADs modify shear stresses and blood transit in the left ventricle (LV) by changing flow patterns and that these changes can be characterized using 2D echo color Doppler velocimetry (echo-CDV). We used echo-CDV and custom postprocessing methods to map blood flow inside the LV in patients with ongoing LVAD support (Heartmate II, N = 7). We compared it to healthy controls (N = 20) and patients with dilated cardiomyopathy (DCM, N = 20). We also analyzed intraventricular flow changes during LVAD ramp tests (baseline ± 400 rpm). LVAD support reversed the increase in blood stasis associated with DCM, but it did not reduce intraventricular shear exposure. Within the narrow range studied, the ventricular flow was mostly insensitive to changes in pump speed. Patients with significant aortic insufficiency showed abnormalities in blood stasis and shear indices. Overall, this study suggests that noninvasive flow imaging could potentially be used in combination with standard clinical methods for adjusting LVAD settings to optimize flow transport and minimize stasis on an individual basis.

Hemodynamic-mediated endocardial signaling controls in vivo myocardial reprogramming.

Lower vertebrate and neonatal mammalian hearts exhibit the remarkable capacity to regenerate through the reprogramming of pre-existing cardiomyocytes. However, how cardiac injury initiates signaling pathways controlling this regenerative reprogramming remains to be defined. Here, we utilize in vivo biophysical and genetic fate mapping zebrafish studies to reveal that altered hemodynamic forces due to cardiac injury activate a sequential endocardial-myocardial signaling cascade to direct cardiomyocyte reprogramming and heart regeneration. Specifically, these altered forces are sensed by the endocardium through the mechanosensitive channel Trpv4 to control Klf2a transcription factor expression. Consequently, Klf2a then activates endocardial Notch signaling which results in the non-cell autonomous initiation of myocardial Erbb2 and BMP signaling to promote cardiomyocyte reprogramming and heart regeneration. Overall, these findings not only reveal how the heart senses and adaptively responds to environmental changes due to cardiac injury, but also provide insight into how flow-mediated mechanisms may regulate cardiomyocyte reprogramming and heart regeneration.

Prevalence of Cryptosporidium spp., Enterocytozoon bieneusi, Encephalitozoon spp. and Giardia intestinalis in Wild, Semi-Wild and Captive Orangutans (Pongo abelii and Pongo pygmaeus) on Sumatra and Borneo, Indonesia.

BACKGROUND: Orangutans are critically endangered primarily due to loss and fragmentation of their natural habitat. This could bring them into closer contact with humans and increase the risk of zoonotic pathogen transmission. AIMS: To describe the prevalence and diversity of Cryptosporidium spp., microsporidia and Giardia intestinalis in orangutans at seven sites on Sumatra and Kalimantan, and to evaluate the impact of orangutans' habituation and location on the occurrence of these zoonotic protists. RESULT: The overall prevalence of parasites in 298 examined animals was 11.1%. The most prevalent microsporidia was Encephalitozoon cuniculi genotype II, found in 21 animals (7.0%). Enterocytozoon bieneusi genotype D (n = 5) and novel genotype Pongo 2 were detected only in six individuals (2.0%). To the best of our knowledge, this is the first report of these parasites in orangutans. Eight animals were positive for Cryptosporidium spp. (2.7%), including C. parvum (n = 2) and C. muris (n = 6). Giardia intestinalis assemblage B, subtype MB6, was identified in a single individual. While no significant differences between the different human contact level groups (p = 0.479-0.670) or between the different islands (p = 0.992) were reported in case of E. bieneusi or E. cuniculi, Cryptosporidium spp. was significantly less frequently detected in wild individuals (p < 2×10-16) and was significantly more prevalent in orangutans on Kalimantan than on Sumatra (p < 2×10-16). CONCLUSION: Our results revealed that wild orangutans are significantly less frequently infected by Cryptosporidium spp. than captive and semi-wild animals. In addition, this parasite was more frequently detected at localities on Kalimantan. In contrast, we did not detect any significant difference in the prevalence of microsporidia between the studied groups of animals. The sources and transmission modes of infections were not determined, as this would require repeated sampling of individuals, examination of water sources, and sampling of humans and animals sharing the habitat with orangutans.