Biology subjects

Parker, G. J. M.

Publications and source records attributed to Parker, G. J. M..

7 recordsLinked to original sources

bioRxiv2025

VASAL: A Vascular Substrate Algorithm to Generate Microvascular Network Phantoms

To date, few methods have been proposed for synthesising realistic microvascular networks, particularly with compatibility for diffusion MRI (dMRI) simulations. This work presents a generative algorithm for building computational brain microvascular network phantoms, together with a Monte Carlo framework for simulating spin flow dynamics and corresponding dMRI signals. Phantom morphology can be user-tuned according to the: number of generations (branches); total number of segments; segment diameter and length; angle between connecting segments, and; maximum extent in space. The geometric fidelity of resulting VASAL phantoms was validated against an exemplar rat hippocampal network. Comparable distributions of segment lengths, diameters and orientations, and branching characteristics were obtained, demonstrating VASAL replicated realistic microvascular structures. Implementation of spin flow dynamics was validated by simulating dMRI signals in the ballistic regime, which has a well-defined analytical solution; agreement was excellent between simulated signals and analytical solutions (average root mean square error of 0.005). Following this validation of network geometry and spin flow dynamics, VASAL phantoms with different morphologies were generated to evaluate key IVIM model assumptions. Phantom geometry substantially affected dMRI signals and caused deviations from the IVIM model: for example, complex branching phantoms displayed non-Gaussian signal attenuation and diffusion time dependence. While the IVIM model was exemplified here, the presented VASAL algorithm holds potential for advancing other dMRI acquisitions and models: by enabling simulation of complex blood flow within physiologically accurate vascular networks, a route to disentangling how vascular structure and function influence dMRI signals is initiated.

biophysics↗

bioRxiv2025

Hierarchical Bayesian Modelling Improves Microstructural Parameter Mapping in Diffusion and Exchange MRI Data

Microstructure modelling quantifies subvoxel tissue features by combining an MRI acquisition with a mathematical model, which is typically fitted voxel-by-voxel with least-squares (LSQ) minimisation to give voxelwise maps of microstructural quantities such as diffusivity and compartmental fractions. Such approaches are susceptible to voxelwise noise, which can lead to erroneous values in parameter maps. Hierarchical Bayesian modelling (HBM) can address this limitation, but has only been demonstrated for simple models. We previously derived an HBM approach for an arbitrary microstructure model with flexible parameter constraints, utilising a Markov chain Monte Carlo algorithm for parameter estimation; here the method is demonstrated and evaluated using simulated and human data for two previously unexplored diffusion MRI techniques, namely diffusion kurtosis imaging and blood-brain barrier filter exchange imaging. When compared with LSQ minimisation, HBM increased the accuracy, precision, contrast-to-noise ratio, and parameter map quality in both simulated and human data. HBM was also able to resolve local parameter variations associated with white matter lesions in a small sample of cerebral small vessel disease subjects, which were obscured by high noise levels in the LSQ-derived parameter maps. Finally, a noise sensitivity assessment in simulations showed that HBM improved the contrast-to-noise ratio and parameter map quality even at low signal-to-noise ratios. This generalised HBM framework can improve parameter estimation for more complex diffusion MRI microstructural models that extend beyond linear combinations of exponentials.

biophysics↗

bioRxiv2023

Quantification of water exchange across the blood-brain barrier using non-contrast MR fingerprinting

PurposeA method is proposed to quantify cerebral blood volume (vb) and intravascular water residence time ({tau}b) using magnetic resonance fingerprinting (MRF), applied using a spoiled gradient echo sequence, without the need for contrast agent. MethodsAn in silico study optimised an acquisition protocol to maximise the sensitivity of the measurement to vb and{tau} b changes. Its accuracy in the presence of variations in T1,t, T1,b, and B1 was evaluated. The optimised protocol (scan time of 19 minutes) was then tested in a exploratory healthy volunteer study (10 volunteers, mean age 24 {+/-} 3, 6 male) at 3 T with a repeat scan taken after repositioning to allow estimation of repeatability. ResultsSimulations show that assuming literature values for T1,b and T1,t, no variation in B1, while fitting only vb and{tau} b, leads to large errors in quantification of vb and{tau} b, regardless of noise levels. However, simulations also show that matching [Formula] and{tau} b, simultaneously is feasible at clinically achievable noise levels. Across the healthy volunteers, all parameter quantifications fell within the expected literature range. In addition, the maps show good agreement between hemispheres suggesting physiologically relevant information is being extracted. Expected differences between white and grey matter T1,t (p<0.0001) and vb (p<0.0001) are observed, T1,b and{tau} b show no significant differences, p=0.4 and p=0.6 repectively. Good repeatability was seen between repeat scans: mean ICC of T1,t : 0.91, T1,b : 0.58, vb : 0.90, and v{tau}b : 0.96. ConclusionWe demonstrate that regional simultaneous quantification of vb,{tau} b, T1,b,T1,t, and B1 using MRF is feasible in vivo.

neuroscience↗

bioRxiv2023

Sensitivity analysis of models of gas exchange for lung hyperpolarised 129-Xe MRS and MRI.

PurposeSensitivity analysis enables the identification of influential parameters and the optimisation of model composition. Such methods have not previously been applied systematically to models describing hyperpolarised 129Xe gas exchange in the lung. Here, we evaluate current 129Xe gas exchange models to assess their precision for identifying alterations in pulmonary-vascular function and lung microstructure. MethodsWe assess sensitivity using established univariate methods and scatter plots for parameter interactions. We apply them to the model described by Patz and MOXE et al., examining their ability to measure: i) importance (rank), ii) temporal dependence, and iii) interaction effects of each parameter across healthy and diseased ranges. ResultsThe univariate methods and scatter plot analyses demonstrate consistently similar results for the importance of parameters common to both models evaluated. Alveolar surface area to volume ratio is identified as the parameter to which model signals are most sensitive. The alveolar-capillary barrier thickness is identified as a low-sensitivity parameter for the MOXE model. An acquisition window of at least 200 ms effectively demonstrates model sensitivity to most parameters. Scatter plots reveal interaction effects in both models, impacting output variability and sensitivity. ConclusionOur sensitivity analysis ranks the parameters within the model described by Patz et al and within the MOXE model. The MOXE model shows low sensitivity to alveolar-capillary barrier thickness, highlighting the need for designing acquisition protocols optimised for the measurement of this parameter. The presence of parameter interaction effects highlights the requirement for care in interpreting model outputs.

bioengineering↗

bioRxiv2023

Independent component analysis (ICA) applied to dynamic oxygen-enhanced MRI (OE-MRI) for robust functional lung imaging at 3 T

PurposeDynamic lung oxygen-enhanced MRI (OE-MRI) is challenging due to the presence of confounding signals and poor signal-to-noise ratio, particularly at 3 T. We have created a robust pipeline utilizing independent component analysis (ICA) to automatically extract the oxygen-induced signal change from confounding factors to improve the accuracy and sensitivity of lung OE-MRI. MethodsDynamic OE-MRI was performed on healthy participants using a dual-echo multi- slice spoiled gradient echo sequence at 3 T and cyclical gas delivery. ICA was applied to each echo within a thoracic mask. The ICA component relating to the oxygen-enhancement signal was automatically identified using correlation analysis. The oxygen-enhancement component was reconstructed, and the percentage signal enhancement (PSE) was calculated. The lung PSE of current smokers was compared with non-smokers; scan-rescan repeatability, ICA pipeline repeatability, and reproducibility between two vendors, were assessed. ResultsICA successfully extracted a consistent oxygen-enhancement component for all participants. Lung tissue and oxygenated blood displayed opposite oxygen-induced signal enhancements. A significant difference in PSE was observed between the lungs of current smokers and non-smokers. The scan-rescan repeatability, and the ICA pipeline repeatability, were good. ConclusionThe developed pipeline demonstrated sensitivity to the signal enhancements of the lung tissue and oxygenated blood at 3 T. The difference in lung PSE between current smokers and non-smokers indicates a likely sensitivity to lung function alterations that may be seen in mild pathology, supporting future use of our methods in patient studies.

physiology↗

bioRxiv2023

Dynamic oxygen-enhanced MRI of the lung at 3 T: feasibility, repeatability and reproducibility

PurposeDynamic T1-weighted lung oxygen enhanced MRI (OE-MRI) is challenging at 3 T due to decreased longitudinal relaxivity of oxygen and increased magnetic susceptibility difference between air and tissue interfaces relative to 1.5 T, leading to poor signal quality. In this work, we evaluate the robustness of an alternative T2*-sensitised lung dynamic OE-MRI protocol in humans at 3 T. MethodsSimulations were performed to predict OE contrast behaviour and optimise the MRI protocol. Sixteen healthy subjects underwent dynamic free-breathing OE-MRI acquisitions using a dual echo RF-spoiled gradient echo acquisition at 3 T on two MRI scanners at different institutions. Non-linear registration and tissue density variation correction were applied. Percent signal enhancement (PSE) maps and {Delta}R2* were derived. Intra-class correlation coefficient (ICC) and Bland-Altman analyses were used to evaluate reproducibility of the OE indices across two sites and vendors as well as scan-rescan repeatability. ResultsSimulations and experimental data show negative contrast on oxygen inhalation due to substantial dominance of {Delta}R2* at TE longer than 0.2 ms when using our chosen flip angle and TR. Mean PSE values were TE dependent and mean {Delta}R2* was 0.14 ms-1 {+/-} 0.03 ms-1, ICC values for intra-scanner (ICCintra) and inter-scanner (ICCinter) variability for OE indices were high (ICCintra > 0.74; ICCinter = 0.70) and 95% limits of agreement showed strong agreement of repeated measures. ConclusionOur results demonstrate excellent scan-rescan repeatability for the PSE indices and good reproducibility for {Delta}R2* across two sites and vendors, suggesting potential utility in multi-centre clinical studies.

biophysics↗

bioRxiv2021

Validating Pore Size Estimates in a Complex Microfibre Environment on a Human MRI System

PurposeRecent advances in diffusion-weighted MRI provide restricted diffusion signal fraction and restricting pore size estimates. Materials based on co-electrospun oriented hollow cylinders have been introduced to provide validation for such methods. This study extends this work, exploring accuracy and repeatability using an extended acquisition on a 300 mT/m gradient human MRI scanner, in substrates closely mimicking tissue, i.e., non-circular cross-sections, intra-voxel fibre crossing, intra-voxel distributions of pore-sizes and smaller pore-sizes overall. MethodsIn a single-blind experiment, diffusion-weighted data were collected from a biomimetic phantom on a 3T Connectom system using multiple gradient directions/diffusion times. Repeated scans established short-term and long-term repeatability. The total scan time (54 minutes) matched similar protocols used in human studies. The number of distinct fibre populations was estimated using spherical deconvolution, and median pore size estimated through the combination of CHARMED and AxCaliber3D framework. Diffusion-based estimates were compared with measurements derived from scanning electron microscopy. ResultsThe phantom contained substrates with different orientations, fibre configurations and pore size distributions. Irrespective of one or two populations within the voxel, the pore-size estimates (~5m) and orientation-estimates showed excellent agreement with the median values of pore-size derived from scanning electron microscope and phantom configuration. Measurement repeatability depended on substrate complexity, with lower values seen in samples containing crossing-fibres. Sample-level repeatability was found to be good. ConclusionWhile no phantom mimics tissue completely, this study takes a step closer to validating diffusion microstructure measurements for use in vivo by demonstrating the ability to quantify microgeometry in relatively complex configurations.

biophysics↗