Uran Ferizi

A ranking of diffusion MRI compartment models with in vivo human brain data.

Diffusion magnetic resonance imaging (MRI) microstructure imaging provides a unique noninvasive probe into tissue microstructure. The technique relies on biophysically motivated mathematical models, relating microscopic tissue features to the magnetic resonance (MR) signal. This work aims to determine which compartment models of diffusion MRI are best at describing measurements from in vivo human brain white matter.

White matter compartment models for in vivo diffusion MRI at 300mT/m.

This paper compares a range of compartment models for diffusion MRI data on in vivo human acquisitions from a standard 60mT/m system (Philips 3T Achieva) and a unique 300mT/m system (Siemens Connectom). The key aim is to determine whether both systems support broadly the same models or whether the Connectom higher gradient system supports significantly more complex models. A single volunteer underwent 8h of acquisition on each system to provide uniquely wide and dense sampling of the available space of pulsed-gradient spin-echo (PGSE) measurements. We select a set of promising models from the wide set of possible three-compartment models for in vivo white matter (WM) that previous work and preliminary experiments suggest as strong candidates, but extend them to fit for compartmental T2 and diffusivity. We focus on the corpus callosum where the WM fibre architecture is simplest and compare their ability to explain the measured data, using Akaikes information criterion (AIC), and to predict unseen data, using cross-validation. We also compare the stability of parameter estimates in the presence of i) noise, using bootstrapping, and ii) spatial variation, using visual assessment and comparison with anatomical knowledge. Broadly similar models emerge from the AIC and cross-validation experiments in both data sets. Specifically, a three-compartment model consisting of either a Bingham distribution of sticks or a Cylinder for the intracellular compartment, an anisotropic diffusion tensor (DT) model for the extracellular compartment, as well as an isotropic CSF compartment, performs consistently well. However, various other models also perform well and no single model emerges as clear winner. The WM data (with virtually no CSF contamination) do not support compartmental T2 but partially support compartmental diffusivity. Evaluation of parameter stability favours simpler models than those identified by AIC or cross-validation. They suggest that the level of complexity in models underpinning currently popular microstructure imaging techniques such as NODDI, CHARMED, or ActiveAx, where the number of free parameters is about 4 or 5 rather than 10 or 11, may reflect the level of complexity achievable for a useful technique on current systems, although the 300mT/m data may support more complex models.

The Importance of Being Dispersed: A Ranking of Diffusion MRI Models for Fibre Dispersion Using In Vivo Human Brain Data

In this work we compare parametric diffusion MRI models which explicitly seek to explain fibre dispersion in nervous tissue. These models aim at providing more specific biomarkers of disease by disentangling these structural contributions to the signal. Some models are drawn from recent work in the field; others have been constructed from combinations of existing compartments that aim to capture both intracellular and extracellular diffusion. To test these models we use a rich dataset acquired in vivo on the corpus callosum of a human brain, and then compare the models via the Bayesian Information Criteria. We test this ranking via bootstrapping on the data sets, and cross-validate across unseen parts of the protocol. We find that models that capture fibre dispersion are preferred. The results show the importance of modelling dispersion, even in apparently coherent fibres.

Diffusion MRI microstructure models with in vivo human brain Connectome data: results from a multi‐group comparison

A large number of mathematical models have been proposed to describe the measured signal in diffusion‐weighted (DW) magnetic resonance imaging (MRI). However, model comparison to date focuses only on specific subclasses, e.g. compartment models or signal models, and little or no information is available in the literature on how performance varies among the different types of models. To address this deficiency, we organized the ‘White Matter Modeling Challenge’ during the International Symposium on Biomedical Imaging (ISBI) 2015 conference. This competition aimed to compare a range of different kinds of models in their ability to explain a large range of measurable in vivo DW human brain data. Specifically, we assessed the ability of models to predict the DW signal accurately for new diffusion gradients and b values. We did not evaluate the accuracy of estimated model parameters, as a ground truth is hard to obtain. We used the Connectome scanner at the Massachusetts General Hospital, using gradient strengths of up to 300 mT/m and a broad set of diffusion times. We focused on assessing the DW signal prediction in two regions: the genu in the corpus callosum, where the fibres are relatively straight and parallel, and the fornix, where the configuration of fibres is more complex. The challenge participants had access to three‐quarters of the dataset and their models were ranked on their ability to predict the remaining unseen quarter of the data. The challenge provided a unique opportunity for a quantitative comparison of diverse methods from multiple groups worldwide. The comparison of the challenge entries reveals interesting trends that could potentially influence the next generation of diffusion‐based quantitative MRI techniques. The first is that signal models do not necessarily outperform tissue models; in fact, of those tested, tissue models rank highest on average. The second is that assuming a non‐Gaussian (rather than purely Gaussian) noise model provides little improvement in prediction of unseen data, although it is possible that this may still have a beneficial effect on estimated parameter values. The third is that preprocessing the training data, here by omitting signal outliers, and using signal‐predicting strategies, such as bootstrapping or cross‐validation, could benefit the model fitting. The analysis in this study provides a benchmark for other models and the data remain available to build up a more complete comparison in the future.

Diffusion tensor imaging of articular cartilage at 3T correlates with histology and biomechanics in a mechanical injury model.

We establish a mechanical injury model for articular cartilage to assess the sensitivity of diffusion tensor imaging (DTI) in detecting cartilage damage early in time. Mechanical injury provides a more realistic model of cartilage degradation compared with commonly used enzymatic degradation.

CHAPTER 22:Quantitative MRI for Detection of Cartilage Damage

In this chapter we revisit the experimental basis supporting the use of magnetic resonance imaging (MRI) to diagnose cartilage degeneration. We include those MRI parameters that are measured in vivo on clinical scanners (Chapter 23). Clinical MRI can detect severe damage with high accuracy (91%), but provides only moderate accuracy (76.7%) in detecting early damage. MRI measurements of cartilage thickness and volume are accurate (<10%), reproducible (2–10%) and highly correlated (Pearsons r = 0.58–0.997) with non-magnetic resonance measurements of thickness and volume. Quantitative biomarkers for cartilage composition show moderate-to-strong correlations with the histology score (Spearmans ρ = 0.31–0.77), and moderate-to-excellent correlation with cartilage composition (r = 0.26–0.99). Although the MRI biomarkers vary significantly between healthy and damaged cartilage (Cohens d = 0.39–2.20), only a few studies analyzed their diagnostic value using a non-MRI standard of reference (e.g. histology, arthroscopy). Thus, further evidence is needed to support the claim that quantitative MRI biomarkers can provide added value to clinical MRI. We conclude this chapter with examples of our most recent experiments in the validation of diffusion tensor imaging (DTI) parameters as biomarkers for cartilage damage including the ability to detect damage after mechanical injury, and the validation of a clinical DTI protocol.

Ranking diffusion-MRI models with in-vivo human brain data

Diffusion MRI microstructure imaging provides a unique non-invasive probe into the microstructure of biological tissue. Its analysis relies on mathematical models relating microscopic tissue features to the MR signal. This work aims to determine which compartment models of diffusion MRI are best at describing the signal from in-vivo brain white matter. Recent work shows that three compartment models, including restricted intra-axonal, glial compartments and hindered extra-cellular diffusion, explain best multi b-value data sets from fixed rat brain tissue. Here, we perform a similar experiment using in-vivo human data. We compare one, two and three compartment models, ranking them with standard model selection criteria. Results show that, as with fixed tissue, three compartment models explain the data best, although simpler models emerge for the in-vivo data. We also find that splitting the scanning into shorter sessions has little effect on the models fitting and that the results are reproducible. The full ranking assists the choice of model and imaging protocol for future microstructure imaging applications in the brain.

A robust diffusion tensor model for clinical applications of MRI to cartilage

Diffusion tensor imaging (DTI) of articular cartilage is a promising technique for the early diagnosis of osteoarthritis (OA). However, in vivo diffusion tensor (DT) measurements suffer from low signal‐to‐noise ratio (SNR) that can result in bias when estimating the six parameters of the full DT, thus reducing sensitivity. This study seeks to validate a simplified four‐parameter DT model (zeppelin) for obtaining more robust and sensitive in vivo DTI biomarkers of cartilage.

Artificial Intelligence Applied to Osteoporosis: A Performance Comparison of Machine Learning Algorithms in Predicting Fragility Fractures From MRI Data: Artificial Intelligence Applied to Osteoporosis

A current challenge in osteoporosis is identifying patients at risk of bone fracture.

Concepts of Diffusion in MRI

In this chapter, we cover the basic concepts of diffusion from a non-mathematical perspective. From the random walk of a water molecule to the effect of obstacles on diffusion in biological tissue and the basic principles of configuring a magnetic resonance imaging machine to be sensitive to diffusion phenomena. We cover the important microstructural properties of central nervous system tissue and their impact on the diffusion characteristics of water in biological tissue.