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A phantom system for evaluating the effect of lipid and iron composition on qMRI parameters

Rona Shaharabani¹, Shir Filo¹, Oshrat Shtangel¹, and Aviv Mezer¹
¹The Edmond and Lily Safra Center for Brain Science, The Hebrew University of Jerusalem, Jerusalem, Israel, Jerusalem, Israel

Synopsis

Neurodegenerative diseases such as Alzheimer, Parkinson, and Multiple sclerosis are often linked to abnormal changes in lipids and iron. Lipids are the main components of any membrane, including myelin, and iron is an essential brain’s trophic. The interaction of lipids and iron with water protons is considered to be a major contributor to the brain’s MRI signals. Here we examine the combined effects of the iron and lipid compositions on quantitative MRI parameters using a novel phantom system. We quantitatively characterize the R2* dependence on lipid and iron concentrations.

Introduction

Lipids are the building blocks of all cell membranes, such as the myelin sheath¹. They comprise about half of the human brain’s dry weight^2,3 and are involved in many metabolic processes and cellular functions⁴. Changes in lipid composition were linked to neurogenerative diseases such as Alzheimer, Parkinson, and Multiple sclerosis^5,6.
The human brain requires iron to perform many vital physiological functions^7–12. For example, iron is required for myelin formation and maintenance¹³. Abnormalities in iron concentrations are associated with a number of neurogenerative diseases^9,12,14–16.
Both lipid and iron contents have been shown to have a strong effect on magnetic resonance imaging (MRI) signal^17,18. Quantitative MRI (qMRI) with physical units allows to reliably compare healthy and pathological tissue using parameters such as relaxation rates^{17,18,20–23}. Nevertheless, it’s still a major challenge to identify and quantify the contributions of specific molecular compounds to the MRI signal. Specifically, it is hard to distinguish whether changes in qMRI measurements result from changes in iron paramagnetic properties or alterations in the lipid content^8,18,23. The ability to quantify the separate contributions of iron and lipids in the brain would be beneficial for clinical diagnostics and hopefully would contribute to understating the pathogenesis of the diseases⁹.
We developed a phantom system composed of different types of lipids and iron at different concentrations. This is an extension of our previous phantom system based on lipids only^17,18. This phantom system demonstrates that qMRI parameters of relaxation time and magnetization transfer can be estimated with high reproducibility¹⁷. In the human brain, where iron and lipid content changes are correlated⁸, it may be rather difficult to distinguish between the contribution of each component separately. However, with our phantom system, we will be able to identify their specific contribution.
Our present phantom system is designed to evaluate the contribution of lipids and iron to qMRI parameters such as R2*.

Methods

The phantom system is designed as a multilamellar vesicle (MLV) encapsulating water and iron ions in-between its membrane bilayers. We used soy phosphatidylcholine (PC) and a mixture of PC with cholesterol or egg sphingomyelin (SM) at different water fractions (WF). The MLV samples were mixed in chloroform at desired mole ratios and evaporated under reduced pressure in a rotary evaporator vacuum system. The resulting lipid film was resuspended in a 10 mM ammonium bicarbonate solution, lyophilized, and subsequently hydrated in the reassembly buffer containing different concentrations of iron and deionized water (Fig. 1).
The phantoms were scanned in a Skyra 3T MRI Siemens scanner with a 32 channel receive head-coil with scan protocol for estimating different qMRI parameters such as R1, R2, R2*, MTsat, and MTV (macromolecular and lipid tissue volume)^{17,18,23,25,26}.

Results

We prepared phantom samples that contain varying amounts of lipid-water fractions in 10%, 17.5%, and 25% v/v, at different concentrations of iron ions, Fe²⁺ (0.05, 0.15, 0.25, and 0.3 mg/ml).
First, we validated the phantom system and tested its reliability. We tested the MRI estimation of MTV, which is the non-water fraction, defined as 1 – WF. The qMRI MTV parameter, showed a very good agreement with the calculated lipid fraction volume (Fig. 2), for all iron concentrations. We also tested the reproducibility of the system by comparing different qMRI parameters of the same phantom taken on different days and found a strong reproducibility (R²=0.92 on average). We found that our phantom system of lipids and iron is a reliable and reproducible tool to examine the contribution of lipid and iron to qMRI parameters.
To emphasize the potential contribution of the lipids and iron concentrations phantom system, we focused on R2*. Previous studies showed a link between R2* and iron presence in the brain^11,19,22. We replicated this result in our phantom system and found a dependency of R2* and iron concentration (Fig. 3) for all lipid types. Next, we tested the dependency of R2* on the lipid-water fraction. Interestingly, we found that R2* is dependent on the lipid concentration for every lipid type phantom system (Fig. 4). We found a non-additive effect of iron and lipid-water fraction on the R2* parameter. Hence, the phantom system highlights the strong dependency of R2* on lipid concentration on top of the iron concentration.

Conclusions

We developed a phantom system, composed of different lipids, for evaluating the separate effects of the iron and lipid contents on qMRI parameters. We found that the phantom system is a reliable tool for assessing qMRI parameters. This phantom system is designed to evaluate the contribution of lipids and iron to qMRI parameters such as R1, R2, R2*, and MT.
qMRI parameters show dependency on both lipid compositions and iron. As expected, R2* was affected by iron concentration, with increased concentrations of Fe²⁺ exhibiting greater R2* for all lipid types. In addition, we found that R2* values are also highly dependent on the lipid-water fraction. Further experiments and analysis will allow us to evaluate the exact nature of the interaction between these dependencies. In conclusion, this phantom system, in which we can control the lipid type and iron content will allow us to better understand and model the biophysical sources of qMRI parameters and improve their use as biomarkers for assessing brain changes.

Acknowledgements

No acknowledgement found.

References

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Figures

Fig. 1: Schematic representation of the MLV preparation method. (a) Soy PC, alone or as a mixture, was dissolved in chloroform (b) put in a vacuum rotary evaporator to create a uniform lipid film. (c) Ammonium bicarbonate was added to dissolve the lipid film. (d) The solution was then lyophilized and (e) rehydrated with a desired iron ion buffer to create the iron ion encapsulated MLV. (f) The samples were imaged in a clinical scanner.

Fig. 2: qMRI validation of the lipid-water fraction estimation. The qMRI estimation of MTV (x-axis) correlates well with the calculated lipid-water fraction (y-axis). Each color represents a different concentration of Fe²⁺. (a) Lipid-water fraction validation for PC-Cholesterol with Fe²⁺ MLV phantoms. (b) Lipid-water fraction validation for PC with Fe²⁺ MLV phantoms. (c) Lipid-water fraction validation for PC-SM with Fe²⁺ MLV phantoms.

Fig. 3: qMRI relaxation rate R2* with increasing concentration of iron ion Fe²⁺. R2* is affected by the lipid-water fraction and iron concentration. R2* values increase with increasing concentration of Fe²⁺ for all lipid types. Each color represents a different lipid-water fraction MLV phantom. (a) Lipid MLV phantom PC-Cholesterol with Fe²⁺. (b) Lipid MLV phantom PC with Fe²⁺. (c) Lipid MLV phantom PC-SM with Fe²⁺.

Fig. 4: qMRI relaxation rate R2* with increasing concentration of lipid-water fraction. R2* values depend on the lipid-water fraction. For all lipid types systems, and a given Fe²⁺ concentration, R2* values increase with increasing lipid-water fraction. Each color represents a different Fe²⁺ concentration. (a) Lipid PC-Cholesterol with Fe²⁺ MLV phantoms. (b) Lipid PC with Fe²⁺ MLV phantoms. (c) Lipid PC-SM with Fe²⁺ MLV phantoms.

Proc. Intl. Soc. Mag. Reson. Med. 29 (2021)

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