Jan Sijbers

Bio: Jan Sijbers is an academic researcher from University of Antwerp. The author has contributed to research in topics: Iterative reconstruction & Diffusion MRI. The author has an hindex of 61, co-authored 427 publications receiving 15031 citations. Previous affiliations of Jan Sijbers include King's College London & Katholieke Universiteit Leuven.

ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data

Abstract: Introduction Diffusion tensor imaging (DTI) is becoming a standard addition to routine MR imaging for investigating microstructural tissue properties (e.g., see [1] for a recent review). With this research field rapidly evolving, the need for efficient and user-friendly diffusion MR processing/analysis software packages is also increasing. Here, a new MR diffusion toolbox – dubbed ExploreDTI – is officially presented for the first time. ExploreDTI is a non-commercial package that combines many of the key MR diffusion processing tools that have appeared in the recent literature, but which have not necessarily been widely available. The package will be made freely available to academic institutions following the ISMRM meeting in Hawaii. The main features of ExploreDTI are summarized in the following sections. Platform ExploreDTI is written in Matlab (The Mathworks Inc., Natick, Massachusetts, USA) and, as such, works across multiple platforms (Windows PC, Unix, Mac). The focus of ExploreDTI is on interactive display and manipulation of data, such as WM fiber tracts (Fig. 1), brain surface renderings (Fig. 2), and diffusion glyphs (principal diffusion directions (PDD), cuboids, ellipsoids, and fiber orientation distribution (FOD) objects – Fig. 3). While many aspects of the processing have been vectorized, optimal results will be obtained with a high-performance graphics card. Key features • Data Reconstruction: ExploreDTI can take, as input, raw diffusion-weighted (DW) data in multiple formats including Dicom, Analyze, NIFTI, and Matlab formats – and is easily adapted to handle others. The diffusion tensor can be estimated by linear, weighted linear, and non-linear least squares methods with (or without) the RESTORE method [2]. Q-ball Imaging (QBI) [3] and Constrained Spherical Deconvolution (CSD) [4] reconstructions are integrated as are overlays of other modalities, such as T1 structural data (Figs. 4, 5) or atlas labels (Fig. 6). • Motion / Eddy Current Correction: This pre-processing step corrects the DW images for subject motion and Eddy-current-induced distortions, incorporating the Bmatrix rotation to preserve the orientational information correctly [5]. • Quality Assessment Tools: ExploreDTI contains quality assessment tools to investigate artefacts (due to motion, distortions, signal dropouts, etc.) based on analyses of residuals and outliers of the diffusion tensor fit [6] (Fig. 7). • Fiber tractography: Both deterministic [7] (Figs. 1, 4) and wild bootstrap (Figs. 8, 9) [8] streamline tracking algorithms are supported for DTI, QBI, and CSD [2, 3]. Atlas labels and ROIs (‘AND’, ‘OR/SEED’, ‘NOT’) can be defined to select fiber tracts of interest (or segments thereof – Fig. 10), such as described, for instance, in [9]. ExploreDTI also incorporates the PASTA tools [10] so that quantitative measures along the tracts, such as fractional and relative anisotropy, the ‘Westin’-measures [11], mean/longitudinal/transverse diffusivity, or other modality information can be calculated. • Synthetic fiber phantoms: ExploreDTI allows for the generation of synthetic MR diffusion fiber phantoms with a wide range of architectural complexity given a set of predefined parameters, such as the b-value, voxel size, number of gradient directions, mean diffusivity, width of the fiber bundle(s) etc. [12, 13] (Fig. 11). • Visualizations: In addition to brain surface renderings, diffusion glyphs for DTI/CSD/QBI, and image maps (FA, mean diffusivity, etc.), hyper-streamtubes (streamtubes with variable width – Fig. 12) are integrated to visualize specific local fiber tract properties, such as the 95% “cone of uncertainty” [10]. Snapshot and Movie tools are included for rapid and easy export of key results into presentations. Summary Although developed in Matlab, ExploreDTI can be compiled as a standalone application, obviating the need for a Matlab licence. ExploreDTI can take output from other tracking packages – and provide a way of navigating through them in an efficient manner. ExploreDTI incorporates a detailed manual, with worked examples and screenshots to enable you to get quickly acquainted with its many features. More information can be found on http://www.ExploreDTI.com.

Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging.

Abstract: It has long been recognized that the diffusion tensor model is inappropriate to characterize complex fiber architecture, causing tensor-derived measures such as the primary eigenvector and fractional anisotropy to be unreliable or misleading in these regions. There is however still debate about the impact of this problem in practice. A recent study using a Bayesian automatic relevance detection (ARD) multicompartment model suggested that a third of white matter (WM) voxels contain crossing fibers, a value that, whilst already significant, is likely to be an underestimate. The aim of this study is to provide more robust estimates of the proportion of affected voxels, the number of fiber orientations within each WM voxel, and the impact on tensor-derived analyses, using large, high-quality diffusion-weighted data sets, with reconstruction parameters optimized specifically for this task. Two reconstruction algorithms were used: constrained spherical deconvolution (CSD), and the ARD method used in the previous study. We estimate the proportion of WM voxels containing crossing fibers to be ∼90% (using CSD) and 63% (using ARD). Both these values are much higher than previously reported, strongly suggesting that the diffusion tensor model is inadequate in the vast majority of WM regions. This has serious implications for downstream processing applications that depend on this model, particularly tractography, and the interpretation of anisotropy and radial/axial diffusivity measures.

Fourier-transform method of fringe-pattern analysis for computer-based topography and interferometry

Abstract: A fast-Fourier-transform method of topography and interferometry is proposed. By computer processing of a noncontour type of fringe pattern, automatic discrimination is achieved between elevation and depression of the object or wave-front form, which has not been possible by the fringe-contour-generation techniques. The method has advantages over moire topography and conventional fringe-contour interferometry in both accuracy and sensitivity. Unlike fringe-scanning techniques, the method is easy to apply because it uses no moving components.

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Abstract: In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients—manually annotated by up to four raters—and to 65 comparable scans generated using tumor image simulation software Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%–85%), illustrating the difficulty of this task We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource

Anisotropic diffusion in image processing

Abstract: Preface Through many centuries physics has been one of the most fruitful sources of inspiration for mathematics. As a consequence, mathematics has become an economic language providing a few basic principles which allow to explain a large variety of physical phenomena. Many of them are described in terms of partial diierential equations (PDEs). In recent years, however, mathematics also has been stimulated by other novel elds such as image processing. Goals like image segmentation, multiscale image representation, or image restoration cause a lot of challenging mathematical questions. Nevertheless, these problems frequently have been tackled with a pool of heuristical recipes. Since the treatment of digital images requires very much computing power, these methods had to be fairly simple. With the tremendous advances in computer technology in the last decade, it has become possible to apply more sophisticated techniques such as PDE-based methods which have been inspired by physical processes. Among these techniques, parabolic PDEs have found a lot of attention for smoothing and restoration purposes, see e.g. 113]. To restore images these equations frequently arise from gradient descent methods applied to variational problems. Image smoothing by parabolic PDEs is closely related to the scale-space concept where one embeds the original image into a family of subsequently simpler , more global representations of it. This idea plays a fundamental role for extracting semantically important information. The pioneering work of Alvarez, Guichard, Lions and Morel 11] has demonstrated that all scale-spaces fulllling a few fairly natural axioms are governed by parabolic PDEs with the original image as initial condition. Within this framework, two classes can be justiied in a rigorous way as scale-spaces: the linear diiusion equation with constant dif-fusivity and nonlinear so-called morphological PDEs. All these methods satisfy a monotony axiom as smoothing requirement which states that, if one image is brighter than another, then this order is preserved during the entire scale-space evolution. An interesting class of parabolic equations which pursue both scale-space and restoration intentions is given by nonlinear diiusion lters. Methods of this type have been proposed for the rst time by Perona and Malik in 1987 190]. In v vi PREFACE order to smooth the image and to simultaneously enhance semantically important features such as edges, they apply a diiusion process whose diiusivity is steered by local image properties. These lters are diicult to analyse mathematically , as they may act locally like a backward diiusion process. …