2618

Investigating Relationships Between Brain Magnetic Susceptibility, Transfusion Treatments, and Fine Motor Function in Sickle Cell Disease
Matthew T. Cherukara1, Jamie M Kawadler2, Fenella Kirkham2, and Karin Shmueli1
1Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, 2Developmental Neurosciences Section, Institute of Child Health, University College London, London, United Kingdom

Synopsis

Keywords: Susceptibility/QSM, Genetic Diseases, Haematology

Motivation: Sickle cell disease (SCD) can lead to cognitive difficulties, but transfusion treatment presents a risk of iron overload which may lead to neurodegeneration. Better understanding of the impact of SCD and transfusions is needed.

Goal(s): To use quantitative susceptibility mapping (QSM) to assess iron deposition in the brain in SCD with and without transfusions.

Approach: Brain susceptibility was quantified in 28 SCD patients and 16 healthy controls using QSM and related to fine motor function by a general linear model.

Results: Susceptibilities in deep brain structures were not correlated with transfusions, SCD status (except in substantia nigra), or motor function (except in pulvinar).

Impact: Using an up-to-date QSM reconstruction pipeline reduced noise and artefacts and revealed correlations of susceptibility with age which were not found previously in these data, confirming the importance of correct coil combination for QSM studies.

Introduction

Homozygous sickle cell anaemia (HbSS) is the most common and severe form of sickle cell disease (SCD)1 and may cause pain, anaemia, repeated infections, stroke, and progressive cognitive difficulties.2,3 HbSS patients are often treated with regular blood transfusions; however, this presents a risk of iron overload which can lead to neurodegeneration.4,5 Quantitative susceptibility mapping (QSM)6,7 has been validated as a means to measure tissue iron content,8-11 and has been used in assessing iron overload in SCD patients.5 Previous work has shown susceptibility ($$$\chi$$$) differences in deep-brain regions in HbSS patients relative to healthy controls12-14 and an association between cognitive ability and brain iron content measured by QSM.15 However, little work has been done investigating relationships between $$$\chi$$$ and cognitive function in SCD under different treatment conditions.16 We optimized a QSM reconstruction pipeline and investigated the relationship between $$$\chi$$$ in deep-brain ROIs and SCD status and treatment.

Methods

Data were acquired under local ethics committee approval as part of a previous study.12 The cohort consisted of 29 children and young adults with HbSS (of which 4 were receiving regular transfusions) and 18 sibling and familial controls (HC). One HbSS and two HC subjects were excluded due to severe image artefacts. A T2*-weighted multi-echo 3D GRE and a T1-weighted 3D FLASH were acquired on a 1.5 T Siemens system (Erlangen, Germany), with parameters shown in Figure 1. As a measure of fine motor function, subjects completed the pegboard subtest of the Zurich Neuromotor Assessment,17 with two trials per hand.
QSMs were calculated from 3D-GRE data using the following pipeline. To remove phase singularities18 due to incorrect coil combination evident in a previous study,12 raw k-space data were reconstructed offline for each coil and combined using ASPIRE.19 Phase inconsistencies were corrected.20 and complex data were de-noised using MP-PCA21 before non-linear fitting20 and unwrapping using SEGUE.22 A brain mask was created from the GRE magnitude data using FSL BET23 and eroded by 2 voxels. Background fields were removed using V-SHARP.24 QSMs were calculated using an iterative Tikhonov-regularized algorithm,25 with regularization parameter $$$\alpha=0.05$$$ (determined through L-curve analysis). Within-ROI $$$\chi$$$ standard deviations ($$$SD_{\chi}$$$) were compared with those from the previously reported pipeline12 as a measure of reliability. Mean $$$\chi$$$ values in 8 bilateral ROIs (Figure 2) were extracted from QSMs using MRIcloud,26,27 informed by the T1-weighted images.
A general linear model (GLM) was used to model mean ROI $$$\chi$$$ as a function of fixed covariates {age, SCD status (including transfusions), pegboard test score}. Goodness of fit was evaluated with an F-test, and t-tests were performed on each coefficient to identify those contributing significantly to each model. All analyses were performed in MATLAB (MathWorks, Natick, MA).

Results

Deep brain structures had an average $$$SD_{\chi}=0.032\pm0.012$$$ppm compared to $$$0.058\pm0.010$$$ppm using the previously reported pipeline.12 Figure 3 shows example QSMs from both pipelines from a single subject.
Figure 4 shows the distribution of uncorrected mean $$$\chi$$$ values in 8 ROIs across HC, HbSS, and HbSS with transfusions groups. The GLM was statistically significant (p<0.01) in every ROI but one (thalamus). In the remaining 7 ROIs, age was the most significant covariate, and only in the substantia nigra was SCD status also a positive significant predictor of $$$\chi$$$ (p<0.05). Sex was a significant predictor of $$$\chi$$$ in the caudate, and a significant negative correlation between pegboard score and $$$\chi$$$ was found in the pulvinar. In all other cases, SCD status, transfusions, and pegboard score were not significant covariates.

Discussion & Conclusion

An optimized QSM reconstruction pipeline was applied to data from HbSS patients and healthy controls, resulting in QSMs with significantly (p<0.001) lower variance of $$$\chi$$$ within deep-brain ROIs than the previously reported pipeline.12 The new QSMs are less noisy and contain fewer artefacts, suggesting that they provide more reliable results.
GLM analyses showed that SCD status was not a significant predictor of deep-brain $$$\chi$$$ values, except in the substantia nigra, nor was treatment by transfusion. This is different from previous studies which found that, in SCD patients, $$$\chi$$$ was significantly different in the caudate nucleus,5,14 globus pallidus,12-14 putamen,13 and red nucleus.14 There was a significant relationship between fine motor skill and susceptibility in the pulvinar, consistent with other work.15 Further work is needed to validate the utility of QSM in assessing the impact of SCD and its treatments on the brain.
As expected,28-30 age was found to be a statistically significant covariate of $$$\chi$$$ in every ROI. This relationship was not detected in the same data reconstructed using an older pipeline,12 highlighting the impact of QSM reconstruction artefacts on later analyses.

Acknowledgements

MTC is funded by CRUK multidisciplinary award 24348. KS is funded by ERC consolidator grant DiSCo MRI SFN 770939.

References

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Figures

Figure 1. Scan parameters used to acquire data for QSM (T2*-weighted multi-echo 3D GRE sequence) and segmentation of deep brain ROIs (T1-weighted 3D FLASH sequence).

Figure 2. Bilateral deep-brain grey matter regions of interest, obtained using MRIcloud,26,27 overlaid onto an example susceptibility map from a healthy control subject.

Figure 3. Quantitative susceptibility maps from the same subject as Figure 2 reconstructed using (A) a previously reported pipeline12 and (B) the new, optimized pipeline including ASPIRE coil combination,19 MP-PCA denoising21 and iterative Tikhonov susceptibility calculation.25 (C) Difference image. The orange arrow indicates the location of a singularity in the original reconstruction which was corrected by the present pipeline.

Figure 4. Box chart showing average $$$\chi$$$ values in 8 bilateral deep brain regions of interest, for three groups of subjects: Healthy controls (HC – green), HbSS patients (purple) and HbSS patients receiving regular transfusions (HbSS+Trans – orange). GLM analysis showed that there were significant differences in $$$\chi$$$ in the substantia nigra (boxed) between the HC and HbSS groups.

Figure 5. Table showing the results of fitting a general linear model of susceptibility in each deep-brain ROI, with predictors {Sex, Age, SCD Status, Transfusions, and Pegboard test score}. All models were statistically significant (p<0.05) except in the thalamus. In all statistically significant models, age was a significant covariate with $$$\chi$$$ (p<0.01). In only one ROI (substantia nigra) was SCD status a significant covariate with $$$\chi$$$ (p<0.05), and in one ROI (pulvinar) pegboard score was significant (p<0.05). Transfusion status was not significant in any ROI.

Proc. Intl. Soc. Mag. Reson. Med. 32 (2024)
2618
DOI: https://doi.org/10.58530/2024/2618