Daan Christiaens

Denoising of diffusion MRI using random matrix theory

We introduce and evaluate a post-processing technique for fast denoising of diffusion-weighted MR images. By exploiting the intrinsic redundancy in diffusion MRI using universal properties of the eigenspectrum of random covariance matrices, we remove noise-only principal components, thereby enabling signal-to-noise ratio enhancements. This yields parameter maps of improved quality for visual, quantitative, and statistical interpretation. By studying statistics of residuals, we demonstrate that the technique suppresses local signal fluctuations that solely originate from thermal noise rather than from other sources such as anatomical detail. Furthermore, we achieve improved precision in the estimation of diffusion parameters and fiber orientations in the human brain without compromising the accuracy and spatial resolution.

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

&NA; Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non‐invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub‐challenges: Sub‐Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state‐of‐the‐art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state‐of‐the‐art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub‐acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles‐challenge.org). HighlightsEvaluation framework for automatic stroke lesion segmentation from MRIPublic multi‐center, multi‐vendor, multi‐protocol databases releasedOngoing fair and automated benchmark with expert created ground truth setsComparison of 14+7 groups who responded to an open challenge in MICCAISegmentation feasible in acute and unsolved in sub‐acute cases Graphical abstract Figure. No caption available.

Global tractography of multi-shell diffusion-weighted imaging data using a multi-tissue model.

Diffusion-weighted imaging and tractography provide a unique, non-invasive technique to study the macroscopic structure and connectivity of brain white matter in vivo. Global tractography methods aim at reconstructing the full-brain fiber configuration that best explains the measured data, based on a generative signal model. In this work, we incorporate a multi-shell multi-tissue model based on spherical convolution, into a global tractography framework, which allows to deal with partial volume effects. The required tissue response functions can be estimated from and hence calibrated to the data. The resulting track reconstruction is quantitatively related to the apparent fiber density in the data. In addition, the fiber orientation distribution for white matter and the volume fractions of gray matter and cerebrospinal fluid are produced as ancillary results. Validation results on simulated data demonstrate that this data-driven approach improves over state-of-the-art streamline and global tracking methods, particularly in the valid connection rate. Results in human brain data correspond to known white matter anatomy and show improved modeling of partial voluming. This work is an important step toward detecting and quantifying white matter changes and connectivity in healthy subjects and patients.

Quiet echo planar imaging for functional and diffusion MRI

To develop a purpose‐built quiet echo planar imaging capability for fetal functional and diffusion scans, for which acoustic considerations often compromise efficiency and resolution as well as angular/temporal coverage.

Convex Non-negative Spherical Factorization of Multi-Shell Diffusion-Weighted Images

Diffusion-weighted imaging DWI allows to probe tissue microstructure non-invasively and study healthy and diseased white matter WM in vivo. Yet, less research has focussed on modelling grey matter GM, cerebrospinal fluid CSF and other tissues. Here, we introduce a fully data-driven approach to spherical deconvolution, based on convex non-negative matrix factorization. Our approach decomposes multi-shell DWI data, represented in the basis of spherical harmonics, into tissue-specific orientation distribution functions and corresponding response functions. We evaluate the proposed method in phantom simulations and in vivo brain images, and demonstrate its ability to reconstruct WM, GM and CSF, unsupervised and solely relying on DWI.

Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics

Diffusion MRI (dMRI)-based tractography offers unique abilities to map whole-brain structural connections in human and animal brains. However, dMRI-based tractography indirectly measures white matter tracts, with suboptimal accuracy and reliability. Recently, sophisticated methods including constrained spherical deconvolution (CSD) and global tractography have been developed to improve tract reconstructions through modeling of more complex fiber orientations. Our study aimed to determine the accuracy of connectome reconstruction for three dMRI-based tractography approaches: diffusion tensor (DT)-based, CSD-based and global tractography. Therefore, we validated whole brain structural connectome reconstructions based on ten ultrahigh-resolution dMRI rat brain scans and 106 cortical regions, from which varying tractography parameters were compared against standardized neuronal tracer data. All tested tractography methods generated considerable numbers of false positive and false negative connections. There was a parameter range trade-off between sensitivity: 0.06–0.63 interhemispherically and 0.22–0.86 intrahemispherically; and specificity: 0.99–0.60 interhemispherically and 0.99–0.23 intrahemispherically. Furthermore, performance of all tractography methods decreased with increasing spatial distance between connected regions. Similar patterns and trade-offs were found, when we applied spherical deconvolution informed filtering of tractograms, streamline thresholding and group-based average network thresholding. Despite the potential of CSD-based and global tractography to handle complex fiber orientations at voxel level, reconstruction accuracy, especially for long-distance connections, remains a challenge. Hence, connectome reconstruction benefits from varying parameter settings and combination of tractography methods to account for anatomical variation of neuronal pathways.

Dynamic field mapping and motion correction using interleaved double spin-echo diffusion MRI

Diffusion MRI (dMRI) analysis requires combining data from many images and this generally requires corrections for image distortion and for subject motion during what may be a prolonged acquisition. Particularly in non-brain applications, changes in pose such as respiration can cause image distortion to be time varying, impeding static field map-based correction. In addition, motion and distortion correction is challenging at high b-values due to the low signal-to-noise ratio (SNR). In this work we develop a new approach that breaks the traditional “one-volume, one-weighting” paradigm by interleaving low-b and high-b slices, and combine this with a reverse phase-encoded double-spin echo sequence. Interspersing low and high b-value slices ensures that the low-b, high-SNR data is in close spatial and temporal proximity to support dynamic field map estimation from the double spin-echo acquisition and image-based motion correction. This information is propagated to high-b slices with interpolation across space and time. The method is tested in the challenging environment of fetal dMRI and it is demonstrated using data from 8 pregnant volunteers that combining dynamic distortion correction with slice-by-slice motion correction increases data consistency to facilitate advanced analyses where conventional methods fail.

A Voxel-Wise, Cascaded Classification Approach to Ischemic Stroke Lesion Segmentation

Automated localisation and segmentation of stroke lesions in patients is of great interest to clinicians and researchers alike. We propose a supervised method based on cascaded extremely randomised trees for lesion segmentation, working on a per voxel basis in native subject space. The proposed pipeline is evaluated in the MICCAI Ischemic Stroke Lesion Segmentation (ISLES) challenge, both with nested cross-validation on the training data as well as on independent, multi-centre test data. We obtained good performance although inter-subject variability is large, and reached 3rd place in the SPES sub-challenge.

Groupwise Deformable Registration of Fiber Track Sets Using Track Orientation Distributions

Diffusion-weighted imaging (DWI) and tractography allow to study the macroscopic structure of white matter in vivo. We present a novel method for deformable registration of unsegmented full-brain fiber track sets extracted from DWI data. Our method attempts to align the track orientation distributions (TODs) of multiple subjects, rather than individual tracks. As such, it can handle complex track configurations and allows for multi-resolution registration. We validated the registration method on synthetically deformed DWI data and on data of 15 healthy subjects, and achieved sub-voxel accuracy in dense white matter structures. This work is, to the best of our knowledge, the first demonstration of direct registration of probabilistic tractography data.

Atlas-Guided Global Tractography: Imposing a Prior on the Local Track Orientation

Since its introduction over a decade ago, diffusion tractography has come a long way from local, deterministic methods, over probabilistic approaches, towards global tractography. Yet, the development of tractography methods has been largely focused on single subject data, and very little on cross-population analysis and inter-subject variability. In this work, we extend global tractography with a prior on the local track orientation distribution (TOD), derived from 20 normal subjects. The proposed method is evaluated in five independent subjects. Results show that adding such prior regularizes the reconstructed track distribution, although registration errors can induce local artefacts. We conclude that atlas-guided global tractography can improve the fibre reconstruction and ultimately detect and quantify inter-subject differences in tractography.