Synopsis
Numerical phantoms have played and will continue to play an important role in the development and validation of advanced diffusion MRI techniques. They complement biological phantoms (in vitro, ex vivo and in vivo) with their controllability and physical phantoms with their flexibility. This talk will review the aspects of diffusion MRI techniques that have benefited from validation with numerical phantoms and the range of numerical phantoms currently available. Examples of using numerical phantoms for validating the mapping of tissue microstructure and structural connectivity in the brain will be presented.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 measurements in both basic research and clinical applications.
Educational Objectives
This talk aims to provide its audience with the following:
1. Overview of the aspects of diffusion MRI techniques that can be validated with numerical phantoms.
2. Overview of the range of numerical phantoms that are currently available.
3. Examples of using numerical phantoms for validating diffusion MRI techniques.
Overview
Over the past two decades, diffusion MRI has firmly established itself as the modality of choice for the non-invasive mapping of tissue microstructure and structural connectivity in the brain. Its success is a testament to the relentless efforts within the diffusion MRI research community in advancing the state-of-the-art to mitigate the limitations of earlier developments. A common denominator among all these advancements has been the adoption of numerical phantoms to provide rigorous validation.
Numerical phantoms play a unique role in validation that is complementary of other forms of phantoms (physical, in vitro, ex vivo and in vivo). Although they necessarily represent a model of the real world based on our current understanding, they provide a framework for validation that is much more controlled (with known ground truth) than in vitro, ex vivo and in vivo phantoms, and is much more flexible than physical phantoms.
This talk will give an overview of the numerical phantoms that are currently available for validating diffusion MRI techniques. It will give examples of using numerical phantoms to validate
1. microstructure imaging,
2. tractography/structural connectivity,
3. pre-processing and group comparison.
Validation of Microstructure Imaging
This is an area in which numerical phantoms have played a major role. They allow recent model-based microstructure imaging techniques to be evaluated in a controlled and flexible manner that is not possible otherwise.
Model-based microstructure imaging techniques (Stanisz et al 1997, Assaf et al 2004, Jespersen 2007, Alexander et al 2010, Fieremans et al 2011, Zhang et al 2012, Scherrer et al In Press) work by prescribing a model that uses a set of salient microstructural features (e.g. axon density and diameter) to predict the measured diffusion MRI signals, and when presented with a set of measurements, the prescribed model is used to estimate the salient features from the measurements. To be of practical use, the models chosen generally need to be sufficiently simple, thus representing a crude approximation to the reality. To understand the limitations of such approximations, numerical phantoms have been developed. They implement much more accurate models that are computationally too expensive for practical use but provide the ground truth mapping between microstructural features and the diffusion signal.
Numerical phantoms for this purpose can be divided into several types based on the models implemented. The most common class uses Monte Carlo simulation of diffusion (Szafer et al 1995, Ford et al 1997, Liu et al 2004, Peled 2007, Balls et al 2009, Hall et al 2009, Nilsson et al 2009, Fieremans et al 2010, Landman et al 2010, Yeh et al 2013), with the system implemented in the Open Source Camino software being most widely used (Hall et al 2009). This class of phantoms has been used to validate the estimation of axon density and diameter (Alexander et al 2010), crossing-fibre resolution (Ramirez-Manzanares et al 2011), exchange (Fieremans et al 2010, Nilsson et al 2010), neurite beading (Budde et al 2010) and axonal undulation (Nilsson et al 2011), time-dependent extra-axonal diffusion (Burcaw et al 2015, Lam et al 2015, De Santis et al 2016), and g-ratio mapping (Stikov et al 2011).
An alternative approach is to directly solve the diffusion equations using either finite differences (Chin et al 2002, Hwang et al 2003, Russell et al 2012, Li et al 2014) or finite element method (Hagslätt et al 2003, Moroney et al 2013, Nguyen et al 2014, Beltrachini et al 2015).
Validation of Tractography/Structural Connectivity
The tractography evaluation is dominated by physical and biological phantoms but numerical phantoms still have a role to play with its flexibility (Leemans et al 2005, Close et al 2009, Neher et al 2014). In particular, Fiberfox (Neher et al 2014), combined with Tractometer (Côté et al 2013), has recently been chosen for the Tractography Challenge 2015 hosted by the ISMRM Diffusion Study Group.
Validation of Pre-processing and Group Comparison
Numerical phantoms have also been developed to validate common pre-processing and group comparison tools. Graham et al (2016) have recently developed a tool for simulating realistic imaging artefacts in diffusion MRI and demonstrated its application to the evaluation of eddy-current distortion and motion correction techniques. Van Hecke et al (2007) have used synthetic diffusion MRI data (Leemans et al 2005) to evaluate a diffusion tensor image registration framework. The authors have also developed a framework based on simulated diffusion MRI data to assess various aspect of population-based group comparison using diffusion MRI (Van Hecke et al 2008-2011).
Acknowledgements
No acknowledgement found.References
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