Introduction

Quantitative susceptibility mapping (QSM) provides local magnetic susceptibility (χ) estimates using MRI-signal phase^1–5 and has previously found promise in the assessment of cortical abnormalities in paediatric epilepsy at 3T⁶. Importantly, phase-based measurements benefit from increased contrast and resolution at 7T but QSM at ultra-high field has mainly been used to assess deep brain-structures. Cortical normative susceptibility values in children are not well-known, limiting the research or clinical utility of the method. Here we develop a pipeline for assessing childhood normative values of cortical QSM at 7T using surface-mapping based on the Human Connectome Pipeline (HCP)⁷. We investigate the sensitivity of this normative dataset for detecting tissue-differences associated with focal cortical dysplasia (FCD) in children with drug-resistant epilepsy.

Methods

Data acquisition:
9 healthy controls (mean age: 11.6 years, range: 8-14 years; 7 males) and 9 pediatric patients with drug-resistant focal epilepsy (mean age: 12.4 years, range: 8-17 years; 6 males) were imaged at 7T (MAGNETOM Terra, Siemens Healthineers, Ethics Ref:18.LO.1766) with a 3D MP2RAGE (0.65mm isotropic, TE/TR=3.15/4000ms,TIs=650/2280ms), 3D FLAIR (0.8mm isotropic, TE/TR=240/9000ms,TI=2600ms) and a 3D T2*-weighted gradient-echo sequence for QSM (0.7mm isotropic, TR=29ms, flip angle= 15.5°, 6 equidistant TEs between 2.68 and 26.13ms).

Data analysis:
QSM reconstruction: Single-channel data was combined for each TE by the sum of the square of all single-channel magnitude images. The combined phase was calculated for each TE by the sum of conjugate complex multiplication between the previous and current TE⁸. The combined phase image of the first TE was set to zero.

$$Φ_{TE_{1}}=0$$
$$Φ_{TE_{n}}=arg(\sum z_{TE_{n-1}}
⋅\bar{z}_{TE_{n}})\hspace{3mm}∀n>1\hspace{1mm} [1]$$

A brain-mask was computed on the first magnitude image⁹. The frequency-shift was calculated by a non-linear fit of the complex data over TEs¹⁰. The local frequency-shift was calculated using the Projection onto Dipole Fields (PDF) method¹¹ with a brain-mask eroded to 85% to minimise non-local phase contributions. QSM maps were calculated from the local frequency shift using Iterative Tikhonov regularization¹².
Image co-registration and analysis: FLAIR and T1w images were analysed with the HCP⁷ to perform volumetric segmentation and reconstruct white/grey matter and pial surfaces. Bias-corrected magnitude images were rigidly co-registered to their corresponding MP2RAGE volume¹³, and QSM spatial normalisation was achieved through the composition of the above transformations. A study-specific template was created with the MP2RAGE volumes¹³, to which each QSM map was warped.
Lesion masking: Two patients with suspected FCD diagnosis based on radiological and electro-clinical reports were selected for this feasibility study. After surgical resection, the FCD was confirmed at histology for Patient 2 (see Table 1 for clinical information). 3D FLAIR images were used to segment the brain lesion and create 3D binary masks.
Computation of a control distribution: The remaining patients and healthy participants were combined to build a heterogeneous pseudo-control distribution (making the assumption that lesions did not overlap in any two patients), against which individual QSM changes in the two patients were tested. Masks of globus pallidus, putamen, caudate and cortical ribbon were used to calculate normative average and standard deviation values.
Deviation maps of FCDs: Maps of susceptibility deviation within lesions were obtained by computing the voxel-wise z-score. Values beyond the interval [−2, 2] were considered as representing significant differences in patients compared to the control distribution¹⁴.
Surface mapping and FCD profiling: QSM was sampled along surfaces extracted at different depths from the pial surface down to 6mm in 0.5mm steps. The QSM surfaces were smoothed using a 10mm vertex-wise FWHM Gaussian kernel⁶. The smoothed QSM surfaces and the lesion masks were mapped to a standard surface (fsaverage). Average χ values at different cortical depths within the lesion area were extracted, and z-score QSM profiles were computed.

Results

An example susceptibility map is shown in Figure 1. Caudate, putamen and globus pallidus are clearly visible, consistent with previous findings^15–18. Normative susceptibility values for these structures (Figure 2) are roughly consistent with previous paediatric studies at 3T^19–21, and marginally lower than previous adult studies^17,22,23, in line with the positive correlation previously reported between age and χ^24–26. Figure 2 also reports mean and standard deviation of cortical χ values. Results of the voxel-wise MRI lesion-profiling and depth-dependent QSM sampling are shown in Figures 3 & 4. Depth-dependent signal sampling showed the highest sensitivity to changes, revealing increasingly negative z-scores at ~5mm from the pial surface in Patient 1, and increasingly positive z-scores at ~4mm depth in Patient 2.

Discussion

We evaluated the feasibility of 7T QSM to investigate epileptogenic lesions in pediatric focal epilepsy. The obtained QSM normative values were consistent with previous studies^19–21 and the analysis approaches utilised were sensitive to susceptibility changes in epileptogenic lesions. The finding of decreased susceptibility across cortical depths within Patient 1’s FCD is in line with previous evidence⁶, suggested to represent colocalized reductions in iron and myelin. Susceptibility increases observed in Patient 2’s FCD may represent increased iron accumulation owing to tissue-inflammation, abnormal cells and oedema, thus suggesting the presence of an FCD type IIb ^27–29, consistent with the pathology report (Table 1).

Conclusion

These first results indicate the feasibility of ultra-high field QSM for the in-vivo characterisation of FCDs in paediatric epilepsy, however replication in a larger sample is needed.

Acknowledgements

This work was supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society [206675/Z/17/Z], GOSHCC Sparks Grant [V4419], King's Health Partners and by core funding from the Wellcome/EPSRC Centre for Medical Engineering [WT203148/Z/16/Z], as well as by the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London and/or the NIHR Clinical Research Facility. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. We are also thankful to the families who contributed their time to this research, and to our colleagues from the Evelina London Children Hospital, St Thomas’ Hospital and Centre for the Developing Brain.

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