Biology subjects

Coveney, S.

Publications and source records attributed to Coveney, S..

3 recordsLinked to original sources

bioRxiv2025

ZOnally magnified Oblique Multi-slice (ZOOM) for cardiac diffusion weighted imaging in vivo

PurposeCardiac diffusion weighted imaging (cDWI) commonly utilizes 2D-selective RF-pulses (such as ZOOMit) to avoid aliasing artifacts when reducing the field of view (FOV). These RF-pulses, which may require parallel transmit capability, typically take several 10s of milliseconds, prolonging the echo time (TE) of the imaging sequence. Conversely, slice selection using ZOnally-magnified Oblique Multi-slice (ZOOM) (i.e. tilting the excitation relative to the refocusing RF-pulse) is an alternative for reducing FOV while shortening TE. We hypothesized that ZOOM, with appropriate choices for the RF-pulse tilt angle and thickness, can be reliably used as an alternative to ZOOMit for cardiac Diffusion Tensor Imaging (cDTI). MethodsWe scanned phantoms using full FOV and ZOOM with various parameters chosen from a 2-dimensional grid of angles and thicknesses. We identified a subset of 5 ZOOM parameters for in vivo cDTI experiments. We performed cDTI on healthy volunteers on a Siemens Prisma (6 subjects) and a Connectom (6 subjects) MR system using the 5 identified ZOOM settings, as well as ZOOMit (Prisma only) and no ZOOM (Connectom only). We compared mean diffusivity (MD), fractional anisotropy (FA), and secondary eigenvector angle (|E2A|) between scans. ResultsAverage MD, and standard deviation of MD and FA, were significantly reduced by ZOOM compared to no ZOOM on Connectom. We found no significant differences in any averaged diffusion measures between ZOOMit and ZOOM on Prisma. ConclusionCombining ZOOM with cardiac diffusion weighted imaging allows for TE to be considerably shortened (from 79ms to 67ms) while reducing FOV to avoid aliasing artifacts.

biophysics↗

bioRxiv2025

Robust constrained weighted least squares for in vivo human cardiac diffusion kurtosis imaging

SummaryRobust estimation with convexity constraints significantly improves signal fitting for in vivo human cardiac diffusion kurtosis imaging. PurposeCardiac diffusion tensor imaging (cDTI) is an emerging technique to investigate the microstructure of heart tissue. At sufficiently high b-values, additional information on microstructure can be observed, but the data require a representation beyond DTI such as diffusion kurtosis imaging (DKI). cDTI is highly prone to image corruption, which researchers usually attempt to handle with shot-rejection. However, this can be handled more generally with robust estimation techniques. Recent work has also demonstrated the need to perform constrained fitting for DKI, as fitted parameters can otherwise violate necessary constraints on the signal behaviour, causing significant errors in estimated measures. MethodsWe developed robust constrained weighted least squares (RCWLS) by combining robust estimation with convexity constraints specifically for DKI. Using in vivo cardiac DKI data from 11 healthy volunteers collected with a Connectom scanner, we tested various combinations of fitting techniques, with/without robustness and with/without constraints. ResultsRCWLS was the only tested technique that convincingly showed radial kurtosis to be larger than axial kurtosis for all subjects, something that is expected in myocardium due to increased restrictions to diffusion in the plane perpendicular to the primary myocyte direction. RCWLS also showed the best correction of corrupted regions in diffusion parameter maps for individual subjects. ConclusionFitting techniques utilizing both robust estimation and constraints are essential to facilitate applicability of in vivo cardiac DKI.

physiology↗

bioRxiv2019

Sensitivity and uncertainty analysis of two human atrial cardiac cell models using Gaussian process emulators

Cardiac cell models reconstruct the action potential and calcium dynamics of cardiac myocytes, and are becoming widely used research tools. These models are highly detailed, with many parameters in the equations that describe current flow through ion channels, pumps, and exchangers in the cell membrane, and so it is difficult to link changes in model inputs to model behaviours. The aim of the present study was to undertake sensitivity and uncertainty analysis of two models of the human atrial action potential. We used Gaussian processes to emulate the way that 11 features of the action potential and calcium transient produced by each model depended on a set of. The emulators were trained by maximising likelihood conditional on a set of design data, obtained from 300 model evaluations. For each model evaluation, the set of inputs was obtained from uniform distributions centred on the default values for each parameter, using latin-hypercube sampling. First order and total effect sensitivity indices were calculated for each combination of input and output. First order indices were well correlated with the square root of sensitivity indices obtained by partial least squares regression of the design data. The sensitivity indices highlighted a difference in the balance of inward and outward currents during the plateau phase of the action potential in each model, with the consequence that changes to one parameter can have opposite effects in the two models. Overall the interactions among inputs were not as important as the first order effects, indicating that model parameters tend to have independent effects on the model outputs. This study has shown that Gaussian process emulators are an effective tool for sensitivity and uncertainty analysis of cardiac cell models.\n\nAuthor summaryThe time course of the cardiac action potential is determined by the balance of inward and outward currents across the cell membrane, and these in turn depend on dynamic behaviour of ion channels, pumps and exchangers in the cell membrane. Cardiac cell models reconstruct the action potential by representing transmembrane current as a set of stiff and nonlinear ordinary differential equations. These models capture biophysical detail, but are complex and have large numbers of parameters, so cause and effect relationships are difficult to identify. In recent years there has been an increasing interest in uncertainty and variability in computational models, and a number of tools have been developed. In this study we have used one of these tools, Gaussian process emulators, to compare and contrast two models of the human atrial action potential. We obtained sensitivity indices based on the proportion of variance in a model output that is accounted for by variance in each of the model parameters. These sensitivity indices highlighted the model parameters that had the most influence on the model outputs, and provided a means to make a quantitative comparison between the models.

cell biology↗