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Theoretical model and experimental evaluation of cellular, BOLD and nonheme Iron contributions to the quantitative Gradient Recalled Echo (qGRE) and quantitative susceptibility mapping (QSM) signals in basal ganglia.
Dmitriy A Yablonskiy1, Jie Wen1, Satya Kothapalli1, and Alexander Sukstanskii1

1Mallinckrodt Institute of Radiology, St. Louis, MO, United States

Synopsis

Nonheme iron is an important element supporting the structure and functioning of biological tissues. Misbalance in nonheme iron can lead to different neurological disorders. Several MRI approaches have been developed for iron quantification relying either on the relaxation or susceptibility features of MRI signal. Specific quantification of the nonheme iron can, however, be tempered by the heme iron in the deoxygenated blood. The goal of this presentation is to introduce theoretical background and experimental method allowing disentangling contributions of heme and nonheme irons simultaneously with evaluation of tissue neuronal density in the basal ganglia.

INTRODUCTION

Iron is an important element supporting the structure and functioning of biological tissues. In the brain, heme iron (Fe2+) in the hemoglobin supports oxygen delivery while most of the brain nonheme ferric iron (Fe3+) is believed to be present as a storage pool consisting of ferritin or hemosiderin1,2. Since misbalance in nonheme iron in the brain can lead to different neurological disorders3, numerous MRI approaches have been developed for iron quantification mostly relying on MRI signal relaxation properties (T1, T2, T2*)2. Recently developed QSM technique (see recent reviews4,5) allows evaluation tissue magnetic susceptibility based on the MRI signal phase. Due to the paramagnetic nature, iron significantly contributes to the signal phase and can be detected by QSM6. In vivo quantification of the nonheme iron can, however, be tempered by the heme iron in the deoxygenated blood. The goal of this presentation is to introduce experimental method allowing disentangling contributions of heme and nonheme irons to the QSM signal in basal ganglia where iron dominates brain tissue magnetic susceptibility and contribution of other cell-building materials (proteins, lipids, etc) can be neglected. Combination of qGRE and QSM approaches also allows evaluation of basal ganglia neuronal content.

METHODS

In this study we use qGRE technique that is based on 3D GRE MRI sequence with multiple gradient echoes, theoretical model7,8 of BOLD effect, and algorithms that we have developed for correcting adverse effects of background field gradients9 and physiological fluctuations10. qGRE data were acquired on Siemens 3T scanner Trio and 32 channel RF coil with FOV 256x192x144 mm, repetition time TR=50 ms, flip angle 35°, 10 gradient echoes with first echo time TE1 = 4 ms, echo spacing ∆TE = 4ms, voxel size (1x1x2 mm3). Phase stabilization echo was collected for each line in k-space to correct for image artefacts due to the physiological fluctuations. Raw k-space data are analyzed in MATLAB. The multi-channel data are combined using the previously published algorithm11. 3D spatial Hanning filter is applied to reduce Gibbs ringing artefacts and increase signal-to-noise ratio. Data are analyzed using theoretical model12 allowing disentanglement of tissue-cellular-specific (R2t*) and BOLD-related contributions to the total GRE MRI signal decay:

$$S(TE)=S_0\cdot exp(-R2t^*\cdot TE-i\omega\cdot TE)\cdot F_{BOLD}(\delta\omega,dCBV,TE)\cdot F_{macro}(TE)$$

where S(0) is the GRE signal intensity amplitude, TE is the gradient echo time, the function Fmacro(TE) accounts for the adverse effects of macroscopic magnetic field inhomogeneities9, and function FBOLD(TE) accounts for the BOLD contributions, δω is characteristic frequency and dCBV is deoxygenated blood volume7,12. Detailed description of the qGRE data analysis is available in12. Parameters δω and dCBV allow calculating a part of magnetic susceptibility related to the presence of heme iron in deoxyhemoglobin:

$$\chi_{deoxyhemoglobin}=\frac{3\cdot dCBV\cdot\delta\omega}{2\pi f_0}$$

where f0 is the scanner resonance frequency. $$$\chi_{deoxyhemoglobin}$$$ can be converted into concentration of deoxyhemoglobin $$$C_{deoxyhemoglobin}[mol/mL]=1.62\cdot \chi_{deoxyhemoglobin}$$$ 12. qGRE signal frequency ω is used to generate QSM maps using the algorithm developed in reference4. The nonheme iron-related magnetic susceptibility can then be calculated as follows:

$$\chi_{nonheme iron}=QSM-\chi_{deoxyhemoglobin}$$

Data were obtained from 22 healthy control subjects (ages 26-63 years) with local IRB approval.

RESULTS AND DISCUSSION

In13, using a multi-modality approach that included qGRE and Allen Human Brain Atlas gene expression data, we demonstrated that the R2t* metric of qGRE signal provides a unique genetic perspective of brain cortical structure and allows in vivo quantitative evaluation of brain neuronal, synaptic and glial cells densities. However, in regions of the brain with high iron accumulation R2t* is also affected by the relaxation processes induced by the water-iron interaction. To account for this phenomenon, three ROIs (caudate, putamen and pallidum) were segmented in each of 22 participants, combined in a single dataset and analyzed using the above-described methods. The regression analysis of R2t* vs. $$$\chi_{nonheme iron}$$$ and age resulted in:

$$R2t^*=14.3+123.2\chi_{nonheme iron}$$

with R2=0.83 and no significant age dependence (P=0.68). The neuronal index (NI) (parameter proportional to volume fraction of neurons) can be calculated from R2t*13 and is NI=0.42. This result suggests that the concentration of neurons in the basal ganglia remains constant across ages. As presented in Figs.2-4, the magnetic susceptibility of heme iron in deoxyhemoglobin only slightly increases with age, while concentration of nonheme iron increases with age significantly, especially in the pallidum. Since magnetic behavior of nonheme iron is not purely paramagnetic14 it would require additional assumptions to convert $$$\chi_{nonheme iron}$$$ into iron molar concentration. One of the important conclusions from our result is a significant contribution of heme iron to QSM measurements.

CONCLUSION

Results show that the qGRE approach provides quantitative information on cellular neuronal and iron (heme and nonheme) content in the basal ganglia.

Acknowledgements

Supported by NIH/NIA grant R01AG054513

References

1. Hallgren, B. & Sourander, P. THE EFFECT OF AGE ON THE NON-HAEMIN IRON IN THE HUMAN BRAIN. Journal of Neurochemistry. 1958;3:41-51.

2. Schenck, J. F. & Zimmerman, E. A. High-field magnetic resonance imaging of brain iron: birth of a biomarker? NMR in Biomedicine. 2004;17:433-445.

3. Stankiewicz, J. et al. Iron in chronic brain disorders: imaging and neurotherapeutic implications. Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics. 2007;4:371-386.

4. Wang, Y. & Liu, T. Quantitative Ssusceptibility Mapping (QSM): Decoding MRI Data for a Tissue Magnetic Biomarker. Magn Reson Med. 2015;73:82-101.

5. Reichenbach, J. R., Schweser, F., Serres, B. & Deistung, A. Quantitative Susceptibility Mapping: Concepts and Applications. Clin Neuroradiol. 2015;25 Suppl 2:225-230.

6. Langkammer, C. et al. Quantitative susceptibility mapping (QSM) as a means to measure brain iron? A post mortem validation study. NeuroImage. 2012;62:1593-1599.

7. Yablonskiy, D. A. & Haacke, E. M. Theory of NMR signal behavior in magnetically inhomogeneous tissues: the static dephasing regime. Magn Reson Med. 1994;32:749-763.

8. Yablonskiy, D. A. Quantitation of intrinsic magnetic susceptibility-related effects in a tissue matrix. Phantom study. Magnetic Resonance in Medicine. 1998;39:417-428.

9. Yablonskiy, D. A., Sukstanskii, A. L., Luo, J. & Wang, X. Voxel spread function method for correction of magnetic field inhomogeneity effects in quantitative gradient-echo-based MRI. Magn Reson Med. 2013;70:1283-1292.

10. Wen, J., Cross, A. H. & Yablonskiy, D. A. On the role of physiological fluctuations in quantitative gradient echo MRI: implications for GEPCI, QSM, and SWI. Magn Reson Med. 2015;73:195-203.

11. Luo, J., Jagadeesan, B. D., Cross, A. H. & Yablonskiy, D. A. Gradient Echo Plural Contrast Imaging - Signal model and derived contrasts: T2*, T1, Phase, SWI, T1f, FST2*and T2*-SWI. Neuroimage. 2012;60:1073-1082.

12. Ulrich, X. & Yablonskiy, D. A. Separation of cellular and BOLD contributions to T2* signal relaxation. Magn Reson Med. 2016;75:606-615.

13. Wen, J., Goyal, M. S., Astafiev, S. V., Raichle, M. E. & Yablonskiy, D. A. Genetically defined cellular correlates of the baseline brain MRI signal. Proc Natl Acad Sci U S A. 2018;115:E9727-E9736,5.

14. Cornell RM & Schwertmann U. The Iron Oxides: Structure, Properties, Reactions, Occurrences, and Uses. Wiley-VCH: Weinheim. 2003.

Figures

Figure 1. Example of images reconstructed from a single MRI acquisition of qGRE signal: S0 (T1-weighted image), quantitative R2* and R2t* maps, and QSM maps.

Figure 2. Age dependence of magnetic susceptibilities (SI units) of heme iron (in deoxyhemoglobin) and nonheme iron in caudate. Data obtained from 22 healthy control subjects (ages 26-63 years).

Figure 3. Age dependence of magnetic susceptibilities (SI units) of heme iron (in deoxyhemoglobin) and nonheme iron in putamen. Data obtained from 22 healthy control subjects (ages 26-63 years).

Figure 4. Age dependence of magnetic susceptibilities (SI units) of heme iron (in deoxyhemoglobin) and nonheme iron in pallidum. Data obtained from 22 healthy control subjects (ages 26-63 years).

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)
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