SPED Light Sheet Microscopy: Fast Mapping of Biological System Structure and Function.

The goal of understanding living nervous systems has driven interest in high-speed and large field-of-view volumetric imaging at cellular resolution. Light sheet microscopy approaches have emerged for cellular-resolution functional brain imaging in small organisms such as larval zebrafish, but remain fundamentally limited in speed. Here, we have developed SPED light sheet microscopy, which combines large volumetric field-of-view via an extended depth of field with the optical sectioning of light sheet microscopy, thereby eliminating the need to physically scan detection objectives for volumetric imaging. SPED enables scanning of thousands of volumes-per-second, limited only by camera acquisition rate, through the harnessing of optical mechanisms that normally result in unwanted spherical aberrations. We demonstrate capabilities of SPED microscopy by performing fast sub-cellular resolution imaging of CLARITY mouse brains and cellular-resolution volumetric Ca(2+) imaging of entire zebrafish nervous systems. Together, SPED light sheet methods enable high-speed cellular-resolution volumetric mapping of biological system structure and function.

Projections from neocortex mediate top-down control of memory retrieval.

Top-down prefrontal cortex inputs to the hippocampus have been hypothesized to be important in memory consolidation, retrieval, and the pathophysiology of major psychiatric diseases; however, no such direct projections have been identified and functionally described. Here we report the discovery of a monosynaptic prefrontal cortex (predominantly anterior cingulate) to hippocampus (CA3 to CA1 region) projection in mice, and find that optogenetic manipulation of this projection (here termed AC-CA) is capable of eliciting contextual memory retrieval. To explore the network mechanisms of this process, we developed and applied tools to observe cellular-resolution neural activity in the hippocampus while stimulating AC-CA projections during memory retrieval in mice behaving in virtual-reality environments. Using this approach, we found that learning drives the emergence of a sparse class of neurons in CA2/CA3 that are highly correlated with the local network and that lead synchronous population activity events; these neurons are then preferentially recruited by the AC-CA projection during memory retrieval. These findings reveal a sparsely implemented memory retrieval mechanism in the hippocampus that operates via direct top-down prefrontal input, with implications for the patterning and storage of salient memory representations.

Automated estimation of image quality for coronary computed tomographic angiography using machine learning.

OBJECTIVES: Our goal was to evaluate the efficacy of a fully automated method for assessing the image quality (IQ) of coronary computed tomography angiography (CCTA). METHODS: The machine learning method was trained using 75 CCTA studies by mapping features (noise, contrast, misregistration scores, and un-interpretability index) to an IQ score based on manual ground truth data. The automated method was validated on a set of 50 CCTA studies and subsequently tested on a new set of 172 CCTA studies against visual IQ scores on a 5-point Likert scale. RESULTS: The area under the curve in the validation set was 0.96. In the 172 CCTA studies, our method yielded a Cohen's kappa statistic for the agreement between automated and visual IQ assessment of 0.67 (p < 0.01). In the group where good to excellent (n = 163), fair (n = 6), and poor visual IQ scores (n = 3) were graded, 155, 5, and 2 of the patients received an automated IQ score > 50 %, respectively. CONCLUSION: Fully automated assessment of the IQ of CCTA data sets by machine learning was reproducible and provided similar results compared with visual analysis within the limits of inter-operator variability. KEY POINTS: • The proposed method enables automated and reproducible image quality assessment. • Machine learning and visual assessments yielded comparable estimates of image quality. • Automated assessment potentially allows for more standardised image quality. • Image quality assessment enables standardization of clinical trial results across different datasets.

Computational simulation of the adaptive capacity of vein grafts in response to increased pressure.

Vein maladaptation, leading to poor long-term patency, is a serious clinical problem in patients receiving coronary artery bypass grafts (CABGs) or undergoing related clinical procedures that subject veins to elevated blood flow and pressure. We propose a computational model of venous adaptation to altered pressure based on a constrained mixture theory of growth and remodeling (G&R). We identify constitutive parameters that optimally match biaxial data from a mouse vena cava, then numerically subject the vein to altered pressure conditions and quantify the extent of adaptation for a biologically reasonable set of bounds for G&R parameters. We identify conditions under which a vein graft can adapt optimally and explore physiological constraints that lead to maladaptation. Finally, we test the hypothesis that a gradual, rather than a step, change in pressure will reduce maladaptation. Optimization is used to accelerate parameter identification and numerically evaluate hypotheses of vein remodeling.

An efficient framework for optimization and parameter sensitivity analysis in arterial growth and remodeling computations.

Computational models for vascular growth and remodeling (G&R) are used to predict the long-term response of vessels to changes in pressure, flow, and other mechanical loading conditions. Accurate predictions of these responses are essential for understanding numerous disease processes. Such models require reliable inputs of numerous parameters, including material properties and growth rates, which are often experimentally derived, and inherently uncertain. While earlier methods have used a brute force approach, systematic uncertainty quantification in G&R models promises to provide much better information. In this work, we introduce an efficient framework for uncertainty quantification and optimal parameter selection, and illustrate it via several examples. First, an adaptive sparse grid stochastic collocation scheme is implemented in an established G&R solver to quantify parameter sensitivities, and near-linear scaling with the number of parameters is demonstrated. This non-intrusive and parallelizable algorithm is compared with standard sampling algorithms such as Monte-Carlo. Second, we determine optimal arterial wall material properties by applying robust optimization. We couple the G&R simulator with an adaptive sparse grid collocation approach and a derivative-free optimization algorithm. We show that an artery can achieve optimal homeostatic conditions over a range of alterations in pressure and flow; robustness of the solution is enforced by including uncertainty in loading conditions in the objective function. We then show that homeostatic intramural and wall shear stress is maintained for a wide range of material properties, though the time it takes to achieve this state varies. We also show that the intramural stress is robust and lies within 5% of its mean value for realistic variability of the material parameters. We observe that prestretch of elastin and collagen are most critical to maintaining homeostasis, while values of the material properties are most critical in determining response time. Finally, we outline several challenges to the G&R community for future work. We suggest that these tools provide the first systematic and efficient framework to quantify uncertainties and optimally identify G&R model parameters.

A stochastic collocation method for uncertainty quantification and propagation in cardiovascular simulations.

Simulations of blood flow in both healthy and diseased vascular models can be used to compute a range of hemodynamic parameters including velocities, time varying wall shear stress, pressure drops, and energy losses. The confidence in the data output from cardiovascular simulations depends directly on our level of certainty in simulation input parameters. In this work, we develop a general set of tools to evaluate the sensitivity of output parameters to input uncertainties in cardiovascular simulations. Uncertainties can arise from boundary conditions, geometrical parameters, or clinical data. These uncertainties result in a range of possible outputs which are quantified using probability density functions (PDFs). The objective is to systemically model the input uncertainties and quantify the confidence in the output of hemodynamic simulations. Input uncertainties are quantified and mapped to the stochastic space using the stochastic collocation technique. We develop an adaptive collocation algorithm for Gauss-Lobatto-Chebyshev grid points that significantly reduces computational cost. This analysis is performed on two idealized problems--an abdominal aortic aneurysm and a carotid artery bifurcation, and one patient specific problem--a Fontan procedure for congenital heart defects. In each case, relevant hemodynamic features are extracted and their uncertainty is quantified. Uncertainty quantification of the hemodynamic simulations is done using (a) stochastic space representations, (b) PDFs, and (c) the confidence intervals for a specified level of confidence in each problem.

Predicting the Physiological Effect of Revascularization in Serially Diseased Coronary Arteries.

BACKGROUND: Fractional flow reserve (FFR) is commonly used to assess the functional significance of coronary artery disease but is theoretically limited in evaluating individual stenoses in serially diseased vessels. We sought to characterize the accuracy of assessing individual stenoses in serial disease using invasive FFR pullback and the noninvasive equivalent, fractional flow reserve by computed tomography (FFRCT). We subsequently describe and test the accuracy of a novel noninvasive FFRCT-derived percutaneous coronary intervention (PCI) planning tool (FFRCT-P) in predicting the true significance of individual stenoses. METHODS AND RESULTS: Patients with angiographic serial coronary artery disease scheduled for PCI were enrolled and underwent prospective coronary CT angiography with conventional FFRCT-derived post hoc for each vessel and stenosis (FFRCT). Before PCI, the invasive hyperemic pressure-wire pullback was performed to derive the apparent FFR contribution of each stenosis (FFRpullback). The true FFR attributable to individual lesions (FFRtrue) was then measured following PCI of one of the lesions. The predictive accuracy of FFRpullback, FFRCT, and the novel technique (FFRCT-P) was then assessed against FFRtrue. From the 24 patients undergoing the protocol, 19 vessels had post hoc FFRCT and FFRCT-P calculation. When assessing the distal effect of all lesions, FFRCT correlated moderately well with invasive FFR ( R=0.71; P<0.001). For lesion-specific assessment, there was significant underestimation of FFRtrue using FFRpullback (mean discrepancy, 0.06±0.05; P<0.001, representing a 42% error) and conventional trans-lesional FFRCT (0.05±0.06; P<0.001, 37% error). Using FFRCT-P, stenosis underestimation was significantly reduced to a 7% error (0.01±0.05; P<0.001). CONCLUSIONS: FFR pullback and conventional FFRCT significantly underestimate true stenosis contribution in serial coronary artery disease. A novel noninvasive FFRCT-based PCI planner tool more accurately predicts the true FFR contribution of each stenosis in serial coronary artery disease.

Uncertainty quantification in coronary blood flow simulations: Impact of geometry, boundary conditions and blood viscosity.

Computational fluid dynamic methods are currently being used clinically to simulate blood flow and pressure and predict the functional significance of atherosclerotic lesions in patient-specific models of the coronary arteries extracted from noninvasive coronary computed tomography angiography (cCTA) data. One such technology, FFRCT, or noninvasive fractional flow reserve derived from CT data, has demonstrated high diagnostic accuracy as compared to invasively measured fractional flow reserve (FFR) obtained with a pressure wire inserted in the coronary arteries during diagnostic cardiac catheterization. However, uncertainties in modeling as well as measurement results in differences between these predicted and measured hemodynamic indices. Uncertainty in modeling can manifest in two forms - anatomic uncertainty resulting in error of the reconstructed 3D model and physiologic uncertainty resulting in errors in boundary conditions or blood viscosity. We present a data-driven framework for modeling these uncertainties and study their impact on blood flow simulations. The incompressible Navier-Stokes equations are used to model blood flow and an adaptive stochastic collocation method is used to model uncertainty propagation in the Navier-Stokes equations. We perform uncertainty quantification in two geometries, an idealized stenosis model and a patient specific model. We show that uncertainty in minimum lumen diameter (MLD) has the largest impact on hemodynamic simulations, followed by boundary resistance, viscosity and lesion length. We show that near the diagnostic cutoff (FFRCT=0.8), the uncertainty due to the latter three variables are lower than measurement uncertainty, while the uncertainty due to MLD is only slightly higher than measurement uncertainty. We also show that uncertainties are not additive but only slightly higher than the highest single parameter uncertainty. The method presented here can be used to output interval estimates of hemodynamic indices and visualize patient-specific maps of sensitivities.

Fast Computation of Hemodynamic Sensitivity to Lumen Segmentation Uncertainty.

Patient-specific blood flow modeling combining imaging data and computational fluid dynamics can aid in the assessment of coronary artery disease. Accurate coronary segmentation and realistic physiologic modeling of boundary conditions are important steps to ensure a high diagnostic performance. Segmentation of the coronary arteries can be constructed by a combination of automated algorithms with human review and editing. However, blood pressure and flow are not impacted equally by different local sections of the coronary artery tree. Focusing human review and editing towards regions that will most affect the subsequent simulations can significantly accelerate the review process. We define geometric sensitivity as the standard deviation in hemodynamics-derived metrics due to uncertainty in lumen segmentation. We develop a machine learning framework for estimating the geometric sensitivity in real time. Features used include geometric and clinical variables, and reduced-order models. We develop an anisotropic kernel regression method for assessment of lumen narrowing score, which is used as a feature in the machine learning algorithm. A multi-resolution sensitivity algorithm is introduced to hierarchically refine regions of high sensitivity so that we can quantify sensitivities to a desired spatial resolution. We show that the mean absolute error of the machine learning algorithm compared to 3D simulations is less than 0.01. We further demonstrate that sensitivity is not predicted simply by anatomic reduction but also encodes information about hemodynamics which in turn depends on downstream boundary conditions. This sensitivity approach can be extended to other systems such as cerebral flow, electro-mechanical simulations, etc.

Coronary Artery Axial Plaque Stress and its Relationship With Lesion Geometry: Application of Computational Fluid Dynamics to Coronary CT Angiography.

OBJECTIVES: The purpose of this study was to characterize the hemodynamic force acting on plaque and to investigate its relationship with lesion geometry. BACKGROUND: Coronary plaque rupture occurs when plaque stress exceeds plaque strength. METHODS: Computational fluid dynamics was applied to 114 lesions (81 patients) from coronary computed tomography angiography. The axial plaque stress (APS) was computed by extracting the axial component of hemodynamic stress acting on stenotic lesions, and the axial lesion asymmetry was assessed by the luminal radius change over length (radius gradient [RG]). Lesions were divided into upstream-dominant (upstream RG > downstream RG) and downstream-dominant lesions (upstream RG < downstream RG) according to the RG. RESULTS: Thirty-three lesions (28.9%) showed net retrograde axial plaque force. Upstream APS linearly increased as lesion severity increased, whereas downstream APS exhibited a concave function for lesion severity. There was a negative correlation (r = -0.274, p = 0.003) between APS and lesion length. The pressure gradient, computed tomography-derived fractional flow reserve (FFRCT), and wall shear stress were consistently higher in upstream segments, regardless of the lesion asymmetry. However, APS was higher in the upstream segment of upstream-dominant lesions (11,371.96 ± 5,575.14 dyne/cm(2) vs. 6,878.14 ± 4,319.51 dyne/cm(2), p < 0.001), and in the downstream segment of downstream-dominant lesions (7,681.12 ± 4,556.99 dyne/cm(2) vs. 11,990.55 ± 5,556.64 dyne/cm(2), p < 0.001). Although there were no differences in FFRCT, % diameter stenosis, and wall shear stress pattern, the distribution of APS was different between upstream- and downstream-dominant lesions. CONCLUSIONS: APS uniquely characterizes the stenotic segment and has a strong relationship with lesion geometry. Clinical application of these hemodynamic and geometric indices may be helpful to assess the future risk of plaque rupture and to determine treatment strategy for patients with coronary artery disease. (Evaluation of FFR, WSS, and TPF Using CCTA; NCT01857687).

Real-time sensitivity analysis of blood flow simulations to lumen segmentation uncertainty.

Patient-specific modeling of blood flow combining CT image data and computational fluid dynamics has significant potential for assessing the functional significance of coronary artery disease. An accurate segmentation of the coronary arteries, an essential ingredient for blood flow modeling methods, is currently attained by a combination of automated algorithms with human review and editing. However, not all portions of the coronary artery tree affect blood flow and pressure equally, and it is of significant importance to direct human review and editing towards regions that will most affect the subsequent simulations. We present a data-driven approach for real-time estimation of sensitivity of blood-flow simulations to uncertainty in lumen segmentation. A machine learning method is used to map patient-specific features to a sensitivity value, using a large database of patients with precomputed sensitivities. We validate the results of the machine learning algorithm using direct 3D blood flow simulations and demonstrate that the algorithm can predict sensitivities in real time with only a small reduction in accuracy as compared to the 3D solutions. This approach can also be applied to other medical applications where physiologic simulations are performed using patient-specific models created from image data.

Quantitative assessment of collagen fibre orientations from two-dimensional images of soft biological tissues.

In this work, we outline an automated method for the extraction and quantification of material parameters characterizing collagen fibre orientations from two-dimensional images. Morphological collagen data among different length scales were obtained by combining the established methods of Fourier power spectrum analysis, wedge filtering and progressive regions of interest splitting. Our proposed method yields data from which we can determine parameters for computational modelling of soft biological tissues using fibre-reinforced constitutive models and gauge the length scales most appropriate for obtaining a physically meaningful measure of fibre orientations, which is representative of the true tissue morphology of the two-dimensional image. Specifically, we focus on three parameters quantifying different aspects of the collagen morphology: first, using maximum-likelihood estimation, we extract location parameters that accurately determine the angle of the principal directions of the fibre reinforcement (i.e. the preferred fibre directions); second, using a dispersion model, we obtain dispersion parameters quantifying the collagen fibre dispersion about these principal directions; third, we calculate the weighted error entropy as a measure of changes in the entire fibre distributions at different length scales, as opposed to their average behaviour. With fully automated imaging techniques (such as multiphoton microscopy) becoming increasingly popular (which often yield large numbers of images to analyse), our method provides an ideal tool for quickly extracting mechanically relevant tissue parameters which have implications for computational modelling (e.g. on the mesh density) and can also be used for the inhomogeneous modelling of tissues.

Image-based modeling of hemodynamics in coronary artery aneurysms caused by Kawasaki disease.

Kawasaki Disease (KD) is the leading cause of acquired pediatric heart disease. A subset of KD patients develops aneurysms in the coronary arteries, leading to increased risk of thrombosis and myocardial infarction. Currently, there are limited clinical data to guide the management of these patients, and the hemodynamic effects of these aneurysms are unknown. We applied patient-specific modeling to systematically quantify hemodynamics and wall shear stress in coronary arteries with aneurysms caused by KD. We modeled the hemodynamics in the aneurysms using anatomic data obtained by multi-detector computed tomography (CT) in a 10-year-old male subject who suffered KD at age 3 years. The altered hemodynamics were compared to that of a reconstructed normal coronary anatomy using our subject as the model. Computer simulations using a robust finite element framework were used to quantify time-varying shear stresses and particle trajectories in the coronary arteries. We accounted for the cardiac contractility and the microcirculation using physiologic downstream boundary conditions. The presence of aneurysms in the proximal coronary artery leads to flow recirculation, reduced wall shear stress within the aneurysm, and high wall shear stress gradients at the neck of the aneurysm. The wall shear stress in the KD subject (2.95-3.81 dynes/sq cm) was an order of magnitude lower than the normal control model (17.10-27.15 dynes/sq cm). Particle residence times were significantly higher, taking 5 cardiac cycles to fully clear from the aneurysmal regions in the KD subject compared to only 1.3 cardiac cycles from the corresponding regions of the normal model. In this novel quantitative study of hemodynamics in coronary aneurysms caused by KD, we documented markedly abnormal flow patterns that are associated with increased risk of thrombosis. This methodology has the potential to provide further insights into the effects of aneurysms in KD and to help risk stratify patients for appropriate medical and surgical interventions.

Patient-specific multiscale modeling of blood flow for coronary artery bypass graft surgery.

We present a computational framework for multiscale modeling and simulation of blood flow in coronary artery bypass graft (CABG) patients. Using this framework, only CT and non-invasive clinical measurements are required without the need to assume pressure and/or flow waveforms in the coronaries and we can capture global circulatory dynamics. We demonstrate this methodology in a case study of a patient with multiple CABGs. A patient-specific model of the blood vessels is constructed from CT image data to include the aorta, aortic branch vessels (brachiocephalic artery and carotids), the coronary arteries and multiple bypass grafts. The rest of the circulatory system is modeled using a lumped parameter network (LPN) 0 dimensional (0D) system comprised of resistances, capacitors (compliance), inductors (inertance), elastance and diodes (valves) that are tuned to match patient-specific clinical data. A finite element solver is used to compute blood flow and pressure in the 3D (3 dimensional) model, and this solver is implicitly coupled to the 0D LPN code at all inlets and outlets. By systematically parameterizing the graft geometry, we evaluate the influence of graft shape on the local hemodynamics, and global circulatory dynamics. Virtual manipulation of graft geometry is automated using Bezier splines and control points along the pathlines. Using this framework, we quantify wall shear stress, wall shear stress gradients and oscillatory shear index for different surgical geometries. We also compare pressures, flow rates and ventricular pressure-volume loops pre- and post-bypass graft surgery. We observe that PV loops do not change significantly after CABG but that both coronary perfusion and local hemodynamic parameters near the anastomosis region change substantially. Implications for future patient-specific optimization of CABG are discussed.