Introduction

Neuro-inflammatory and neuro-degenerative diseases affect the central nervous system causing focal and diffuse damage, which can be quantified using magnetic resonance imaging (MRI). MRI contrasts that are sensitive and specific to pathological mechanisms in neuroinflammation and degeneration are invaluable assets to perform disease diagnosis and monitoring. Recent imaging techniques such as relaxometry or magnetization transfer imaging (MTI) showed high sensitivity to subtle brain changes in a number of neurological disease^1-3. Nevertheless, to date, personalized advanced MRI metrics of brain degeneration and inflammation are not available. We propose here a new methodology, which is based on the quantification of region-specific brain tissue-properties to provide personalize maps of brain damage in a single patient. To achive this aim, we used T1, T2* and MTI maps and applied the method to detect brain abnormalities in multiple sclerosis patients.

Method

We acquired Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE)⁴ at 3T for T1 relaxometry, as well as T2⁵ and T2* relaxometry and MTI using prototype sequences in 19 healthy controls and 5 relapsing-remitting MS (RRMS) patients at early phase of the disease (Table 1). Brain segmentation into lobes and concentration estimation of brain tissues (white matter, grey matter and cerebrospinal fluid) were performed on T1 maps using a partial volume estimation algorithm⁶. Lesions were manually segmented by two raters in consensus on MP2RAGE images. For each contrast (T1, T2, T2* and magnetization transfer ratio-MTR), we estimated a healthy distribution of each brain tissue in each lobe⁷ and performed a voxel-wise comparison of each patient to the healthy distribution of each contrast using a T-score. Finally, we qualitatively analyzed the T-score maps in each subject and contrast and measured the mean T-score in the RRMS patient lesions.

Results

The deviation maps computed from T1 maps of the 4 healthy controls showed no with high T-score value (Figure 2.A). The deviation map from RRMS patients (Figure 2.B), showed high mean T-score inside lesions (Mean Lesions T-score=3.7±1.8), but also revealed diffuse changes of T1 in the NA tissue surrounding lesions (Figure 2.C). The figure 2 shows the T-score maps of a RRMS patient computed from T2, MTR and T2* maps with strong changes inside lesions characterized by high T2 T-score (Mean T2 Lesions T-score = 1.6 ± 0.7), but a low MTR T-score (Mean MTR Lesions T-score = -0.9 ± 0.6) and T2* T-score (Mean T2* Lesions T-score = 0.7 ± 0.3) (Figure 3). We also observe diffuse changes in T2 T-score map around the lesions, also present but less advanced in the T2* T-score map.

Discussion and Conclusion

We propose a novel approach to provide personalized quantification of brain damage using a comparison between quantitative parameters of tissue integrity in a patient and the distribution of the same parameters in a population of healthy controls. Based on the identification of brain tissue concentrations and the measure of a deviation score, we obtain a voxel-wise deviation map, which highlights subtle MR signal variations due to diffuse and focal damage in 5 exemplary MS patients. The T1 contrast appeared to be the most sensitive to both focal and diffuse pathological changes; the lower sensitivity of the other maps could be dependent on the different nature of the signal they provide but also on their lower spatial resolution (Table 1). One advantage of the proposed method compared to conventional voxel-based morphometry⁸ is that the maps are computed in patient space. The obtained T-score maps evidence both focal and diffuse damage, as it is expected in MS patients^9,10. A current challenge of this approach is represented by the cortical ribbon, whose MRI properties appear to be highly variable within lobes: future developments will aim to integrate a more detailed parcellation of the cerebral cortex to overcome this problem. In addition, it can be challenging to estimate WM, GM and CSF concentrations from contrasts with low structural detail (e.g. T2, T2* and MTR). In summary, we have presented a novel approach to perform personalized quantification of brain damage using multiple advanced MRI contrasts, which may permit to better stratify and follow-up the impact of neuroinflammatory and neurodegenerative brain pathologies.

Acknowledgements

No acknowledgement found.

References

1. Deoni SC. Quantitative relaxometry of the brain, Top Magn Reson Imaging. 2010; 2. Seiler S. et al., Magnetization transfer imaging for in vivo detection of microstructural tissue changes in aging and dementia: a short literature review. J Alzheimers Dis. 2014; 3. Enzinger C. et al., Nonconventional MRI and microstructural cerebral changes in multiple sclerosis. Nat Rev Neurol. 2015; 4. Marques et al., MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. NeuroImage 2010;5. Sumpf et al., Fast T2 Mapping with Improved Accuracy Using Undersampled Spin-echo MRI and Model-based Reconstructions with a Generating Function.; 6. Roche et al. Partial Volume Estimation in Brain MRI Revisited. Med Image Comput Comput Assist Interv. 2014 ; 7. Schmitter D. et al., An Evaluation of Volume-Based Morphometry for Prediction of Mild Cognitive Impairment and Alzheimer's Disease. NeuroImage: Clinical, vol. 7, pp. 7-17, 2015. 8. Ashburner et al. Voxel-based morphometry--the methods, Neuroimage. 2000; 9. Seewann A, Vrenken H, van der Valk P, et al. Diffusely abnormal white matter in chronic multiple sclerosis: imaging and histopathologic analysis. Arch Neurol 2009; 10. Parry, A., Clare, S., Jenkinson, M. et al. White matter and lesion T1 relaxation times increase in parallel and correlate with disability in multiple sclerosis. J Neurol (2002) 249: 1279. doi:10.1007/s00415-002-0837-7;