Biology subjects

Finney, S. M.

Publications and source records attributed to Finney, S. M..

4 recordsLinked to original sources

bioRxiv2026

A Framework for SPICE MRSI Reconstruction Posterior Uncertainty Quantification

Purpose: Low-rank reconstruction methods such as SPICE substantially improve Magnetic Resonance Spectroscopic Imaging (MRSI) reconstruction quality. However, SPICE does not provide an estimate of reconstruction uncertainty. A framework for posterior uncertainty estimation in SPICE reconstruction is proposed, hereafter referred to as the framework. Theory and Methods: A Laplace approximation is used to estimate the covariance of the reconstructed spatial coefficients. This covariance is propagated through the spectral basis and spectral fitting to obtain metabolite concentration uncertainty. Matrix-free Hessian--vector products and voxel-wise conjugate-gradient estimation are used to avoid explicit construction or inversion of the full Hessian, making the method tractable. Results: Validation using repeated in vivo 7 T MRSI acquisitions showed that the estimated posterior uncertainty exhibited spatial and spectral patterns similar to those of empirical repeated-scan uncertainty. Experiments further demonstrated how spatial regularization and k-space undersampling affect the estimated uncertainty. Conclusion: The framework provides a practical framework for uncertainty quantification when using SPICE reconstruction of MRSI. It could be adapted to other regularised low-rank reconstruction methods.

neuroscience↗

bioRxiv2026

Open-Source Framework for Measurement of the Gradient Impulse Response Function

The Gradient Impulse Response Function (GIRF) is widely used to model and correct gradient system imperfections in MRI, but scanner-specific GIRF measurement remains inaccessible to many research groups because existing approaches rely on specialised field monitoring hardware or fragmented and non-reproducible software workflows. To address this limitation, an open-source, end-to-end framework for phantom-based GIRF measurement is presented, providing a reproducible workflow requiring only standard MRI hardware and a spherical water phantom. The framework integrates vendor-independent pulse sequence generation, phantom-based data acquisition, automated data processing, and GIRF estimation. The framework was validated by comparing GIRF-predicted non-Cartesian k-space trajectories with independent measurements acquired using NMR field probes, which served as the gold-standard for trajectory characterization. Accurate prediction of rosette and spiral trajectories was demonstrated across multiple imaging orientations, with substantially lower trajectory error than the corresponding nominal trajectories. By providing the first openly available end-to-end implementation for phantom-based GIRF measurement, the barrier to routine scanner-specific GIRF characterisation is reduced, facilitating broader adoption of GIRF-based methods across the MRI community.

bioengineering↗

bioRxiv2026

The Impact of BOLD Induced Linewidth Modulation on Functional 1H MRS Analysis

Functional MRS can measure the neurometabolic response to neuronal activation, therapeutic interventions and changes in physiology. Substantial technical challenges currently present a barrier to reproducible findings and broader adoption by the neuroscientific community. One such challenge is the conflation between genuine metabolic changes and bias caused by subtle spectral lineshape changes associated with the BOLD response. Previous studies have demonstrated an approximately 1% bias for glutamate estimates at 7T based on experimentally acquired data and a single conventional fitting algorithm. In this study, we use synthetic MRS data to estimate the bias for two conventional fitting methods (LCModel and ABfit-reg) at 3T and 7T and evaluate the efficacy of dynamic lineshape adjustment, during preprocessing and fitting analysis stages, to reduce bias. Using the same dataset, we also explore the potential bias in 2D fitting approaches, comparing several fitting models implemented in FSL-MRS. Bias between two conventional fitting methods without explicit linewidth correction was similar ([~]1% for glutamate) and in good agreement with previous experimental studies at 7T. Lineshape changes from the BOLD response cause similar bias in conventional and 2D fitting packages for 3T and 7T data, resulting in an overestimation of metabolic changes associated with neuronal activation. This bias may be significantly reduced (<0.2%) by incorporating a BOLD linewidth matching step for conventional analysis or by direct modelling for 2D analysis. We therefore recommend explicit BOLD lineshape correction or modelling for future task-based fMRS studies at 3T and above.

neuroscience↗

bioRxiv2023

Utilising an in silico model to predict outcomes in senescence-driven acute liver injury

Currently liver transplantation is the only treatment option for liver disease, but organ availability cannot meet demand and transplant recipients require lifelong immunosuppression. The identification of alternative treatments, e.g. cell therapies, able to tip resolution of injury from inflammation to regeneration requires an understanding of the host response to the degree of injury. We adopt a combined in vivo-in silico approach and develop a mathematical model of acute liver disease able to predict the host response to injury. We utilise the Mdm2fl/fl mouse model together with a single Cre induction through intravenous injection of the hepatotropic Adeno-associated Virus Serotype 8 Cre (AAV8.Cre) to model acute liver injury. We derive a complementary ordinary differential equation model to capture the dynamics of the key cell players in the injury response together with the extracellular matrix. We show that the mathematical model is able to predict the host response to moderate injury via qualitative comparison of the model predictions with the experimental data. We then use the model to predict the host response to mild and severe injury, and test these predictions in vivo, obtaining good qualitative agreement.

cell biology↗