Multi-stage prediction networks for data harmonization

In this paper, we introduce multi-task learning (MTL) to data harmonization (DH); where we aim to harmonize images across different acquisition platforms and sites. This allows us to integrate information from multiple acquisitions and improve the predictive performance and learning efficiency of the harmonization model. Specifically, we introduce the Multi Stage Prediction (MSP) Network, a MTL framework that incorporates neural networks of potentially disparate architectures, trained for different individual acquisition platforms, into a larger architecture that is refined in unison. The MSP utilizes high-level features of single networks for individual tasks, as inputs of additional neural networks to inform the final prediction, therefore exploiting redundancy across tasks to make the most of limited training data. We validate our methods on a dMRI harmonization challenge dataset, where we predict three modern platform types, from one obtained from an old scanner. We show how MTL architectures, such as the MSP, produce around 20% improvement of patch-based mean-squared error over current state-of-the-art methods and that our MSP outperforms off-the-shelf MTL networks. Our code is availabl

On Quantifying Local Geometric Structures of Fiber Tracts

International audienceIn diffusion MRI, fiber tracts, represented by densely distributed 3D curves, can be estimated from diffusion weighted images using tractography. The spatial geometric structure of white matter fiber tracts is known to be complex in human brain, but it carries intrinsic information of human brain. In this paper, inspired by studies of liquid crystals, we propose tract-based director field analysis (tDFA) with total six rotationally invariant scalar indices to quantify local geometric structures of fiber tracts. The contributions of tDFA include: 1) We propose orientational order (OO) and orientational dispersion (OD) indices to quantify the degree of alignment and dispersion of fiber tracts; 2) We define the local orthogonal frame for a set of unoriented curves, which is proved to be a generalization of the Frenet frame defined for a single oriented curve; 3) With the local orthogonal frame, we propose splay, bend, and twist indices to quantify three types of orientational distortion of local fiber tracts, and a total distortion index to describe distortions of all three types. The proposed tDFA for fiber tracts is a generalization of the voxel-based DFA (vDFA) which was recently proposed for a spherical function field (i.e., an ODF field). To our knowledge, this is the first work to quantify orientational distortion (splay, bend, twist, and total distortion) of fiber tracts. Experiments show that the proposed scalar indices are useful descriptors of local geometric structures to visualize and analyze fiber tracts

Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process

Less Confusion in Diffusion MRI

With its unique ability to investigate tissue architecture and microstructure in vivo, diffusion MRI (dMRI) has gained tremendous interest and the society has been continuously triggered to develop novel dMRI image analysis approaches. With the overwhelming amount of strategies currently available it is unfortunately not always evident to the end-users how dMRI can be optimally used to address their application. In addition, differences in processing strategies lead to ambiguities as to which conclusions can reliably be drawn from dMRI data, resulting in controversies in the field. Such issues hamper a smooth transition of dMRI processing strategies into useful tools for applications. This thesis contributes to reducing the confusion in diffusion MRI by scrutinizing different steps of the processing pipeline. It focuses on making the topics accessible for a broad audience, and new methodology is proposed to make more intuitive and data-driven choices in dMRI data processing, to facilitate interpretation and visualization of dMRI data, and to investigate fundamental topics such as variability in characteristics of the dMRI signal and the geometrical organization of the brain pathways. Chapter 2 introduces the different steps of the dMRI processing pipeline and reviews the most commonly used and state-of-the-art dMRI processing techniques with a focus on the brain. Chapter 3 describes the MASSIVE (Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation) brain dataset containing multi-modal MR data and 8000 dMRI volumes of a single healthy subject. Subsets of the MASSIVE dataset can serve as representative test beds for the development of new dMRI processing techniques. In Chapter 4 a robust parameter estimation procedure is proposed coined REKINDLE (Robust Extraction of Kurtosis INDices with Linear Estimation). By means of fast reweighted linear estimation of the diffusion kurtosis model, REKINDLE aims to identify and exclude outliers. Chapter 5 describes a data-driven framework that recursively finds single fiber population (SFP) voxels to calibrate the response function for spherical deconvolution, aiming at improved estimation of the fiber orientation distribution function. In Chapter 6, the recursive framework proposed in Chapter 5 is used to localize and characterize SFPs in multiple subjects and tracts. Chapters 7 and 8 focus on a recent debate on the existence of ‘sheet structures’ in the brain. It was proposed that pathways consistently cross each other orthogonally on surfaces somewhere along their trajectory. Others stated that these sheet structures are likely artifacts mainly based on qualitative findings. In Chapter 7, condition for sheet structure is recapitulated and a method to quantify a sheet probability index (SPI) from the data is proposed. Whereas the method in Chapter 7 requires the reconstruction of many pathways with tractography, Chapter 8 proposes a different method to calculate the SPI that does not rely on tractography and is less computationally intensive. Chapter 9 proposes a novel fiber tractography visualization approach that interactively and selectively adapts the transparency rendering of fiber trajectories based on their local or global orientation. This allows for improved 3D visualization and exploration of the fiber network. Chapter 10 discusses the findings in this thesis

Repeatability of Soma and Neurite Metrics in Cortical and Subcortical Grey Matter

Diffusion magnetic resonance imaging is a technique which has long been used to study white matter microstructure in vivo. Recent advancements in hardware and modelling techniques have opened up interest in disentangling tissue compartments in the grey matter. In this study, we evaluate the repeatability of soma and neurite density imaging in a sample of six healthy adults scanned five times on an ultra-strong gradient magnetic resonance scanner (300 mT/m). Repeatability was expressed as an intraclass correlation coefficient (ICC). Our findings reveal that measures of soma density (mean ICC = 0.976), neurite density (mean ICC = 0.959) and apparent soma size (mean ICC = 0.923) are highly reliable across multiple cortical and subcortical networks. Overall, we demonstrate the promise of moving advanced grey matter microstructural imaging towards applications of development, ageing, and disease

Sheet Probability Index (SPI): Characterizing the geometrical organization of the white matter with diffusion MRI

\u3cp\u3eThe question whether our brain pathways adhere to a geometric grid structure has been a popular topic of debate in the diffusion imaging and neuroscience societies. Wedeen et al. (2012a, b) proposed that the brain's white matter is organized like parallel sheets of interwoven pathways. Catani et al. (2012) concluded that this grid pattern is most likely an artifact, resulting from methodological biases that cause the tractography pathways to cross in orthogonal angles. To date, ambiguities in the mathematical conditions for a sheet structure to exist (e.g. its relation to orthogonal angles) combined with the lack of extensive quantitative evidence have prevented wide acceptance of the hypothesis. In this work, we formalize the relevant terminology and recapitulate the condition for a sheet structure to exist. Note that this condition is not related to the presence or absence of orthogonal crossing fibers, and that sheet structure is defined formally as a surface formed by two sets of interwoven pathways intersecting at arbitrary angles within the surface. To quantify the existence of sheet structure, we present a novel framework to compute the sheet probability index (SPI), which reflects the presence of sheet structure in discrete orientation data (e.g. fiber peaks derived from diffusion MRI). With simulation experiments we investigate the effect of spatial resolution, curvature of the fiber pathways, and measurement noise on the ability to detect sheet structure. In real diffusion MRI data experiments we can identify various regions where the data supports sheet structure (high SPI values), but also areas where the data does not support sheet structure (low SPI values) or where no reliable conclusion can be drawn. Several areas with high SPI values were found to be consistent across subjects, across multiple data sets obtained with different scanners, resolutions, and degrees of diffusion weighting, and across various modeling techniques. Under the strong assumption that the diffusion MRI peaks reflect true axons, our results would therefore indicate that pathways do not form sheet structures at every crossing fiber region but instead at well-defined locations in the brain. With this framework, sheet structure location, extent, and orientation could potentially serve as new structural features of brain tissue. The proposed method can be extended to quantify sheet structure in directional data obtained with techniques other than diffusion MRI, which is essential for further validation.\u3c/p\u3

Resolving bundle-specific intra-axonal T₂ values within a voxel using diffusion-relaxation tract-based estimation.

At the typical spatial resolution of MRI in the human brain, approximately 60-90% of voxels contain multiple fiber populations. Quantifying microstructural properties of distinct fiber populations within a voxel is therefore challenging but necessary. While progress has been made for diffusion and T <sub>1</sub> -relaxation properties, how to resolve intra-voxel T <sub>2</sub> heterogeneity remains an open question. Here a novel framework, named COMMIT-T <sub>2</sub> , is proposed that uses tractography-based spatial regularization with diffusion-relaxometry data to estimate multiple intra-axonal T <sub>2</sub> values within a voxel. Unlike previously-proposed voxel-based T <sub>2</sub> estimation methods, which (when applied in white matter) implicitly assume just one fiber bundle in the voxel or the same T <sub>2</sub> for all bundles in the voxel, COMMIT-T <sub>2</sub> can recover specific T <sub>2</sub> values for each unique fiber population passing through the voxel. In this approach, the number of recovered unique T <sub>2</sub> values is not determined by a number of model parameters set a priori, but rather by the number of tractography-reconstructed streamlines passing through the voxel. Proof-of-concept is provided in silico and in vivo, including a demonstration that distinct tract-specific T <sub>2</sub> profiles can be recovered even in the three-way crossing of the corpus callosum, arcuate fasciculus, and corticospinal tract. We demonstrate the favourable performance of COMMIT-T <sub>2</sub> compared to that of voxelwise approaches for mapping intra-axonal T <sub>2</sub> exploiting diffusion, including a direction-averaged method and AMICO-T <sub>2</sub> , a new extension to the previously-proposed Accelerated Microstructure Imaging via Convex Optimization (AMICO) framework

Towards quantification of the brain’s sheet structure in diffusion MRI data

The recent hypothesis on the occurrence of sheet structure in the brain has posed many questions to the diffusion MRI (dMRI) community as to whether this structure actually exists and can be measured with dMRI. In this work, we exploit the capability of the discrete Lie bracket to infer information on the existence of sheet structure in real dMRI data