Physical hardware phantoms for the validation of diffusion MRI
Els Fieremans1

1Radiology, New York University School of Medicine

Synopsis

Physical hardware diffusion phantoms with a well-defined structure, composition and architectural organization can serve as a gold standard for the validation of diffusion MRI. In this lecture, we aim to provide guidelines on how to choose or manufacture a synthetic diffusion phantom that addresses the needs of your project, going from setting up a quality assessment diffusion protocol on a clinical scanner, developing and testing a novel diffusion sequence, validating biophysical models to evaluating tractography models.

Target audience

Scientists and clinicians interested in learning what has been done in validation of commonly used MRI diffusion methods (tractography and tissue microstructure) and applying those methods in their own scientific inquiries and the use of these in both basic research and clinical applications.

Outcome/Objectives

- Provide practical guidelines on how to choose or manufacture a synthetic phantom that addresses the needs of your project

- Understand the critical factors that determine the SNR and diffusion properties (including the mean diffusivity and fractional anisotropy) of a phantom

- Give a state-of-the art overview on both simple and advanced diffusion phantoms and how they can be used to validate diffusion experiments.

Liquids

As for most things, the overall guideline when designing a (diffusion) phantom could be “the simpler, the better”. Indeed, the human brain would be a terrible phantom, as it is far from being well characterized! On the other hand, liquids are very simple, and very useful phantoms. They typically exhibit isotropic Gaussian diffusion and can hence be characterized with a well established (time-independent) diffusion constant, making them useful as a standard for quality assurance (QA) when calibrating diffusion protocols and evaluating new diffusion sequences and postprocessing methods. Potential advantages of liquids are that they are often stable, readily available and require only a minimum of on-site laboratory work.

Following factors to consider:

- The diffusion coefficient depends on the liquid and the temperature

- T1 and T2 relaxation times will define the SNR of the measurement

- The NMR spectrum: spurious peaks in the NMR spectrum may result in ghosting and artifacts in echoplanar imaging, or, alternatively, may be a feature that can be employed to create multicompartment systems (e.g, Figure 1: fat-water).

Examples: Water has been widely used as a test object. A potential drawback of water is the relative high diffusion coefficient at room temperature (2 μm2/ms) compared to the mean diffusivity in brain (0.3 – 1 μm2/ms). Other test liquids are cyclic and linear alkanes, (poly) ethylene glycol, aqueous solutions of sucrose, polyvinylpyrolidone (PVP), albumin, and agar gels. The diffusion coefficients of water and other molecular liquids for the quantitative calibration of diffusion MRI are listed in [1, 2].

Structural phantoms

Surrounding the diffusing molecules by (NMR invisible) materials such that the diffusion becomes restricted and/or hindered opens additional ways for evaluating diffusion anisotropy as well as validating biophysical models and mimicking biological tissues. In this case, following properties of the phantom (NMR invisible) material should be considered [3]:

- Magnetic properties of the material: differences in magnetic susceptibility between the liquid and confining structure will effect the SNR and may result in imaging artifacts, as well as in a directional dependence of the SNR (and diffusion) properties with respect to the static B0-field

- Surface relaxation may affect the SNR and diffusion properties

Examples:

- Anisotropic phantoms based on fibers [3-6] (Figure 2) or capillaries [7-9] have been used extensively for quality assurance and testing of diffusion sequences, as well as for evaluating different fiber tracking algorithms[10] and validating biophysical models. The fractional anisotropy (FA) in such phantoms depends on the type of fiber, diameter, diffusion protocol and fiber material [3]. Hollow fiber systems made from glass or plastic capillaries with a known internal diameter serve as phantoms to study the diffusion in the intra-axonal space, and have been used to evaluate axonal diameter mapping using single and double pulsed field gradient methods[11-13]. Plain fiber bundles serve as a phantom to model the extra-axonal space, and have been used to study the effect of axonal packing geometry on the (time-dependent) diffusion coefficient [14], as well as to calibrate oscillating gradient spin echo diffusion weighted images. Because of the technical challenge to fabricate anisotropic fiber phantom, it is necessary to characterize the density, directionality and inner/outer diameters of the created geometries (bundles, crossings, etc.).

- Isotropic phantoms featuring multi compartment or hindered and/or restricted diffusion: Packing geometries of glass and polymer particles serve as models for restricted diffusion (in porous media, to mimic tumors, etc. ). Avian egg latebra acts as an isotropic phantom featuring restricted diffusion as in brain microstructure [15]. Cream (fat + water) can serve as a simple phantom for bi-exponential diffusion (and relaxation) to test diffusion kurtosis imaging protocols [16, 17].

- Phantoms consisting of permeable membranes can be used to study time-dependent diffusion.

Acknowledgements

Presenter's work is sponsored by NIH R01NS088040, Fellowship from Raymond and Beverly Sackler Laboratories for Convergence of Physical, Engineering and Biomedical Sciences, and the Alzheimer's Drug Discovery Foundation.

References

1. Holz, M., S.R. Heil, and A. Sacco, Temperature-dependent self-diffusion coefficients of water and six selected molecular liquids for calibration in accurate 1H NMR PFG measurements. Physical Chemistry Chemical Physics, 2000. 2(20): p. 4740-4742.

2. Dowell, N.G. and P.S. Tofts, Quality Assurance for Diffusion MRI, in Diffusion MRI: Theory, Methods and Applications, D. Jones, Editor. 2010, Oxford University Press: Oxford.

3. Fieremans, E., et al., The design of anisotropic diffusion phantoms for the validation of diffusion weighted magnetic resonance imaging. Physics in Medicine and Biology, 2008. 53(19): p. 5405-5419.

4. Fieremans, E., et al., Simulation and experimental verification of the diffusion in an anisotropic fiber phantom. Journal of Magnetic Resonance, 2008. 190(2): p. 189-199.

5. Lorenz, R., et al., Anisotropic Phantoms for Quantitative Diffusion Tensor Imaging and Fiber-Tracking Validation. Applied Magnetic Resonance. 33(4): p. 419-429.

6. Pullens, P., A. Roebroeck, and R. Goebel, Ground truth hardware phantoms for validation of diffusion-weighted MRI applications. J Magn Reson Imaging, 2010. 32(2): p. 482-8.

7. Yanasak, N. and J. Allison, Use of capillaries in the construction of an MRI phantom for the assessment of diffusion tensor imaging: demonstration of performance. Magn Reson Imaging, 2006. 24(10): p. 1349-1361.

8. von, D. and R.M. Henkelman, Orientational diffusion reflects fiber structure within a voxel. Magn Reson Med, 2002. 48(3): p. 454-459.

9. Lin, C.-P., et al., Validation of diffusion spectrum magnetic resonance imaging with manganese-enhanced rat optic tracts and ex vivo phantoms. NeuroImage, 2003. 19(3): p. 482-495.

10. Côté, M.-A., et al., Tractometer: Towards validation of tractography pipelines. Medical Image Analysis. 17(7): p. 844-857.

11. Komlosh, M.E., et al., Pore diameter mapping using double pulsed-field gradient MRI and its validation using a novel glass capillary array phantom. Journal of Magnetic Resonance, 2011. 208(1): p. 128-135.

12. Shemesh, N., et al., Noninvasive bipolar double-pulsed-field-gradient NMR reveals signatures for pore size and shape in polydisperse, randomly oriented, inhomogeneous porous media. The Journal of Chemical Physics, 2010. 133(4): p. 044705.

13. Morozov, D., et al., Modeling of the diffusion MR signal in calibrated model systems and nerves. NMR in Biomedicine, 2013. 26(12): p. 1787-1795.

14. Burcaw, L.M., E. Fieremans, and D.S. Novikov, Mesoscopic structure of neuronal tracts from time-dependent diffusion. Neuroimage, 2015. 114: p. 18-37.

15. Maier, S.E., D. Mitsouras, and R.V. Mulkern, Avian Egg Latebra as Brain Tissue Water Diffusion Model. Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine, 2014. 72(2): p. 501-509.

16. Ababneh, Z., et al., Dairy cream as a phantom material for biexponential diffusion decay. Magnetic Resonance Materials in Physics, Biology and Medicine, 2004. 17(2): p. 95-100.

17. Fieremans, E., A. Pires, and J.H. Jensen, A simple isotropic phantom for diffusional kurtosis imaging. Magnetic Resonance in Medicine, 2012. 68(2): p. 537-42.

Figures

Figure 1: Top: A fiber phantom made from polyethylene fibers tightly held together by a shrinking tube (partially removed). Bottom: Resulting fiber tracts after DTI imaging and tracking.

Figure 2: mean diffusivity (MD) and kurtosis (MK) in a cream phantom, which can be described by a slow (fat) and fast (water) diffusing component. The chemical shift between fat and water allows for imaging the fat and water separately (MK ≈ 0) and combined (MK ≈ 1).



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)