Alexander Leemans

Bio: Alexander Leemans is an academic researcher from Utrecht University. The author has contributed to research in topics: Diffusion MRI & Fractional anisotropy. The author has an hindex of 64, co-authored 289 publications receiving 17932 citations. Previous affiliations of Alexander Leemans include Australian Catholic University & Cardiff University.

The B-matrix must be rotated when correcting for subject motion in DTI data.

Abstract: To estimate diffusion tensor MRI (DTI) measures, such as fractional anisotropy and fiber orientation, reliably, a large number of diffusion-encoded images is needed, preferably cardiac gated to reduce pulsation artifacts. However, the concomitant longer acquisition times increase the chances of subject motion adversely affecting the estimation of these measures. While correcting for motion artifacts improves the accuracy of DTI, an often overlooked step in realigning the images is to reorient the B-matrix so that orientational information is correctly preserved. To the best of our knowledge, most research groups and software packages currently omit this reorientation step. Given the recent explosion of DTI applications including, for example, neurosurgical planning (in which errors can have drastic consequences), it is important to investigate the impact of neglecting to perform the B-matrix reorientation. In this work, a systematic study to investigate the effect of neglecting to reorient the B-matrix on DTI data during motion correction is presented. The consequences for diffusion fiber tractography are also discussed. Magn Reson Med 61:1336–1349, 2009.

The challenge of mapping the human connectome based on diffusion tractography

Abstract: Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. Here, we report the encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent). However, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups. Taken together, our results demonstrate and confirm fundamental ambiguities inherent in tract reconstruction based on orientation information alone, which need to be considered when interpreting tractography and connectivity results. Our approach provides a novel framework for estimating reliability of tractography and encourages innovation to address its current limitations.

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.

Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration

Abstract: Cerebral small vessel disease (SVD) is a common accompaniment of ageing. Features seen on neuroimaging include recent small subcortical infarcts, lacunes, white matter hyperintensities, perivascular spaces, microbleeds, and brain atrophy. SVD can present as a stroke or cognitive decline, or can have few or no symptoms. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive deficits, physical disabilities, and other symptoms of neurodegeneration. Terminology and definitions for imaging the features of SVD vary widely, which is also true for protocols for image acquisition and image analysis. This lack of consistency hampers progress in identifying the contribution of SVD to the pathophysiology and clinical features of common neurodegenerative diseases. We are an international working group from the Centres of Excellence in Neurodegeneration. We completed a structured process to develop definitions and imaging standards for markers and consequences of SVD. We aimed to achieve the following: first, to provide a common advisory about terms and definitions for features visible on MRI; second, to suggest minimum standards for image acquisition and analysis; third, to agree on standards for scientific reporting of changes related to SVD on neuroimaging; and fourth, to review emerging imaging methods for detection and quantification of preclinical manifestations of SVD. Our findings and recommendations apply to research studies, and can be used in the clinical setting to standardise image interpretation, acquisition, and reporting. This Position Paper summarises the main outcomes of this international effort to provide the STandards for ReportIng Vascular changes on nEuroimaging (STRIVE).