Biology subjects

Raynaud, Q.

Publications and source records attributed to Raynaud, Q..

3 recordsLinked to original sources

bioRxiv2023

Non-exponential transverse relaxation decay in subcortical grey matter

According to theoretical studies, the MRI signal decay due to transverse relaxation, in brain tissue with magnetic inclusions (e.g. blood vessels, myelin, iron-rich cells), exhibits a transition from a Gaussian behaviour at short echo times to exponential at long echo times. Combined, the Gaussian and exponential decay parameters carry information about the inclusions (e.g., size, volume fraction) and provide a unique insight into brain tissue microstructure. However, gradient echo decays obtained experimentally typically only capture the long-time exponential behaviour. Here, we provide experimental evidence of non-exponential transverse relaxation signal decay at short times in human subcortical grey matter, from MRI data acquired in vivo at 3T. The detection of the non-exponential behaviour of the signal decay allows the subsequent characterization of the magnetic inclusions in the subcortex. The gradient-echo data was collected with short inter-echo spacings, a minimal echo time of 1.25 ms and novel acquisition strategies tailored to mitigate the effect of motion and cardiac pulsation. The data was fitted using both a standard exponential model and non-exponential theoretical models describing the impact of magnetic inclusions on the MRI signal. The non-exponential models provided superior fits to the data, indicative of a better representation of the observed signal. The strongest deviations from exponential behaviour were detected in the substantia nigra and globus pallidus. Numerical simulations of the signal decay, conducted from histological maps of iron concentration in the substantia nigra, closely replicated the experimental data - highlighting that non-heme iron can be at the source of the non-exponential signal decay. To investigate the potential of the non-exponential signal decay as a tool to characterize brain microstructure, we attempted to estimate the properties of the inclusions at the source of this decay behaviour using two available analytical models of transverse relaxation. Under the assumption of the static dephasing regime, the magnetic susceptibility and volume fractions of the inclusions was estimated to range from 1.8 to 4 ppm and from 0.02 to 0.04 respectively. Alternatively, under the assumption of the diffusion narrowing regime, the typical inclusion size was estimated to be [~]2.4 m. Both simulations and experimental data point towards an intermediate regime with a non-negligible effect of water diffusion to signal decay. Non-exponential transverse relaxation decay provides new means to characterize the spatial distribution of magnetic material within subcortical grey matter tissue with increased specificity, with potential applications to Parkinsons disease and other pathologies.

neuroscience↗

bioRxiv2023

Statistical analyses of motion-corrupted MRI relaxometry data

Consistent noise variance across data points (i.e. homoscedasticity) is required to ensure the validity of statistical analyses of MRI data conducted using linear regression methods. However, head motion leads to degradation of image quality, introducing noise heteroscedasticity into ordinary-least square analyses. The recently introduced QUIQI method restores noise homoscedasticity by means of weighted least square analyses in which the weights, specific for each dataset of an analysis, are computed from an index of motion-induced image quality degradation. QUIQI was first demonstrated in the context of brain maps of the MRI parameter R2*, which were computed from a single set of images with variable echo time. Here, we extend this framework to quantitative maps of the MRI parameters R1, R2*, and MTsat, which are computed from multiple sets of images. QUIQI allows for optimization of the noise model by using metrics quantifying heteroscedasticity and free energy. QUIQI restores homoscedasticity more effectively than insertion of an image quality index in the analysis design and yields higher sensitivity than simply removing the datasets most corrupted by head motion from the analysis. In sum, QUIQI provides an optimal approach to group-wise analyses of a range of quantitative MRI parameter maps that is robust to inherent homoscedasticity.

neuroscience↗

bioRxiv2022

Characterization of cardiac-induced noise in R2* maps of the brain

PurposeCardiac pulsation increases the noise level in brain maps of the transverse relaxation rate R2*. Cardiac-induced noise is challenging to mitigate during the acquisition of R2* mapping data because its characteristics are unknown. In this work, we therefore aim to characterize cardiac-induced noise in brain maps of the MRI parameter R2*. MethodsWe designed a sampling strategy to acquire multi-echo 3D data in 12 intervals of the cardiac cycle, monitored with a fingertip pulse-oximeter. We measured the amplitude of cardiac-induced noise in this data and assessed the effect of cardiac pulsation on R2* maps computed across echoes. The area of k-space that contains most of the cardiac-induced noise in R2* maps was then identified. Based on these characteristics, we introduced a tentative sampling strategy that aims to mitigate cardiac-induced noise in R2* maps of the brain. ResultsIn inferior brain regions, cardiac pulsation accounts for R2* variations of up to 3s-1 across the cardiac cycle, i.e. [~]35% of the overall variability. Cardiac-induced fluctuations occur throughout the cardiac cycle, with a reduced intensity during the first quarter of the cycle. 50-60% of the overall cardiac-induced noise is localized near the k-space centre (k < 0.074 mm-1). The tentative cardiac noise mitigation strategy reduced the variability of R2* maps across repetitions by 11% in the brainstem and 6% across the whole brain. ConclusionWe provide a characterisation of cardiac-induced noise in brain R2* maps that can be used as a basis for the design of mitigation strategies during data acquisition.

neuroscience↗