Recipes To Validate Diffusion
Mariam Andersson1
1Danish Research Centre for Magnetic Resonance, Copenhagen, Denmark

Synopsis

Keywords: Contrast mechanisms: Diffusion, Contrast mechanisms: Microstructure

Diffusion MRI can provide clinically valuable information regarding the microstructural composition of tissue. There exist many signal representations and models that provide different interpretations of tissue structure of components. However, their validation is necessary to ensure their sensitivity and specificity to the underlying anatomy of interest. This lecture aims to outline the key points to consider when evaluating diffusion MRI techniques and outcome measures, and to present recent advancements in MRI validation techniques, including: novel tissue imaging modalities, Monte Carlo simulations on numerical phantoms, and physical phantoms.

Introduction

By probing the diffusion of water molecules occurring within tissue, diffusion MRI can non-invasively provide information about its microstructural composition1. This has significant clinical value and potential, since the diffusion MRI signal thus may contain information about tissue damage and pathology. For instance, the evaluation of diffusion weighted images (DWI) and apparent diffusion coefficient (ADC) maps is crucial to the hyperacute (0-6 hours) diagnosis of ischaemic stroke2. In oncologic imaging, DWI can be used for tumour evaluation in the brain3, breast4 and other areas5. Moreover, the fitting of signal representations (such as the diffusion tensor) or models to the diffusion MRI signal can provide more advanced interpretations and measures of tissue structure that are clinically relevant. Differences in diffusion tensor imaging6 (DTI) measures of fractional anisotropy (FA), for example, have been demonstrated in amyotrophic lateral sclerosis7 and multiple sclerosis8 compared to healthy controls. Microstructural models such as neurite orientation dispersion and density imaging (NODDI9), soma and neurite density imaging (SANDI10), ActiveAx11 all aim to extract cellular information such as axonal volume fraction, soma volume fraction, axon diameter from the diffusion MRI signal.

Recipes for validation

For use in the clinic, it is important that these diffusion MRI outcome measures are both sensitive and specific to the pathology or anatomy that is being studied. To ensure this, we thus need to: 1) verify that our diffusion MRI technique can detect changes to the anatomy we are interested in across for the scales on which it exists, and for feasible experimental parameters; and 2) validate the diffusion MRI outcome measure with some form of “ground truth” from an independent imaging contrast or modality.

A common theme throughout the lecture is the importance of comparing “apples to apples” when evaluating a diffusion MRI technique or outcome measure against another modality or contrast. It is crucial to be aware of what we are measuring with diffusion MRI (affected by voxel size, tissue conditions, sequence parameters, etc.) in order to design an appropriate validation approach. Although we have not yet reached this stage of truly being able to compare “apples to apples”, diffusion MRI validation is an iterative process1 with continuous progress.

In this lecture, we will expand on the above and go through recent developments in diffusion MRI validation including: imaging with other modalities12–15, synthetic phantoms for simulations16–18 and physical phantoms.

Acknowledgements

No acknowledgement found.

References

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Proc. Intl. Soc. Mag. Reson. Med. 32 (2024)