B W Kreher

Universitätsklinikum Freiburg, Freiburg an der Elbe, Lower Saxony, Germany

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Publications (15)31.5 Total impact

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    ABSTRACT: Until very recently, the study of neural architecture using fixed tissue has been a major scientific focus of neurologists and neuroanatomists. A non-invasive detailed insight into the brain's axonal connectivity in vivo has only become possible since the development of diffusion tensor magnetic resonance imaging (DT-MRI). This unique approach of analyzing axonal projections in the living brain was used in the present study to describe major white matter fiber tracts of the mouse brain and also to identify for the first time non-invasively the rich connectivity between the amygdala and different target regions. To overcome the difficulties associated with high spatially and temporally resolved DT-MRI measurements a 4-shot diffusion weighted spin echo (SE) echo planar imaging (EPI) protocol was adapted to mouse brain imaging at 9.4T. Diffusion tensor was calculated from data sets acquired by using 30 diffusion gradient directions while keeping the acquisition time at 91 min. Two fiber tracking algorithms were employed. A deterministic approach (fiber assignment by continuous tracking - FACT algorithm) allowed us to identify and generate the 3D representations of various neural pathways. A probabilistic approach was further used for the generation of probability maps of connectivity with which it was possible to investigate - in a statistical sense - all possible connecting pathways between selected seed points. We show here applications to determine the connection probability between regions belonging to the visual or limbic systems. This method does not require a priori knowledge about the projections' trajectories and is shown to be efficient even if the investigated pathway is long or three-dimensionally complex. Additionally, high resolution images of rotational invariant parameters of the diffusion tensor, such as fractional anisotropy, volume ratio or main eigenvalues allowed quantitative comparisons in-between regions of interest (ROIs) and showed significant differences between various white matter regions.
    NMR in Biomedicine 08/2010; 23(7):884-96. DOI:10.1002/nbm.1496 · 3.04 Impact Factor
  • Vry · D Saur · R Umarova · B Kreher · S Schnell · V Glauche · F Hamzei · C Weiller
    NeuroImage 07/2009; 47. DOI:10.1016/S1053-8119(09)71868-3 · 6.36 Impact Factor
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    S Schnell · D Saur · B W Kreher · J Hennig · H Burkhardt · V G Kiselev
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    ABSTRACT: The purpose of this study is the classification of high angular resolution diffusion imaging (HARDI) in vivo data using a model-free approach. This is achieved by using a Support Vector Machine (SVM) algorithm taken from the field of supervised statistical learning. Six classes of image components are determined: grey matter, parallel neuronal fibre bundles in white matter, crossing neuronal fibre bundles in white matter, partial volume between white and grey matter, background noise and cerebrospinal fluid. The SVM requires properties derived from the data as input, the so called feature vector, which should be rotation invariant. For our application we derive such a description from the spherical harmonic decomposition of the HARDI signal. With this information the SVM is trained in order to find the function for separating the classes. The SVM is systematically tested with simulated data and then applied to six in vivo data sets. This new approach is data-driven and enables fully automatic HARDI data segmentation without employing a T1 MPRAGE scan and subjective expert intervention. This was demonstrated on five test in vivo data sets giving robust results. The segmentation results could be used as a priori knowledge for increasing the performance of fibre tracking as well as for other clinical and diagnostic applications of diffusion weighted imaging (DWI).
    NeuroImage 04/2009; 46(3):642-51. DOI:10.1016/j.neuroimage.2009.03.003 · 6.36 Impact Factor
  • Klinische Neurophysiologie 01/2009; 120(1). DOI:10.1016/j.clinph.2008.07.117 · 0.12 Impact Factor
  • B W Kreher · S Schnell · I Mader · K.A. Il'yasov · J Hennig · V G Kiselev · D Saur
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    ABSTRACT: Probability mapping of connectivity is a powerful tool to determine the fibre structure of white matter in the brain. Probability maps are related to the degree of connectivity to a chosen seed area. In many applications, however, it is necessary to isolate a fibre bundle that connects two areas. A frequently suggested solution is to select curves, which pass only through two or more areas. This is very inefficient, especially for long-distance pathways and small areas. In this paper, a novel probability-based method is presented that is capable of extracting neuronal pathways defined by two seed points. A Monte Carlo simulation based tracking method, similar to the Probabilistic Index of Connectivity (PICo) approach, was extended to preserve the directional information of the main fibre bundles passing a voxel. By combining two of these extended visiting maps arising from different seed points, two independent parameters are determined for each voxel: the first quantifies the uncertainty that a voxel is connected to both seed points; the second represents the directional information and estimates the proportion of fibres running in the direction of the other seed point (connecting fibre) or face a third area (merging fibre). Both parameters are used to calculate the probability that a voxel is part of the bundle connecting both seed points. The performance and limitations of this DTI-based method are demonstrated using simulations as well as in vivo measurements.
    NeuroImage 10/2008; 43(1):81-9. DOI:10.1016/j.neuroimage.2008.06.023 · 6.36 Impact Factor
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    B.W. Kreher · I Mader · V.G. Kiselev
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    ABSTRACT: Reconstruction of neuronal fibers using diffusion-weighted (DW) MRI is an emerging method in biomedical research. Existing fiber-tracking algorithms are commonly based on the "walker principle." Fibers are reconstructed as trajectories of "walkers," which are guided according to local diffusion properties. In this study, a new method of fiber tracking is proposed that does not engage any "walking" algorithm. It resolves a number of inherent problems of the "walking" approach, in particular the reconstruction of crossing and spreading fibers. In the proposed method, the fibers are built with small line elements. Each line element contributes an anisotropic term to the simulated DW signal, which is adjusted to the measured signal. This method demonstrates good results for simulated fibers. A single in vivo result demonstrates the successful reconstruction of the dominant neuronal pathways. A comparison with the diffusion tensor imaging (DTI)-based fiber assignment with continuous tracking (FACT) method and the probabilistic index of connectivity (PICo) method based on a multitensor model is performed for the callosal fibers. The result shows a strong increase in the number of reconstructed fibers. These almost fill the total white matter (WM) volume and connect a large area of the cortex. The method is very computationally expensive. Possible ways to address this problem are discussed.
    Magnetic Resonance in Medicine 10/2008; 60(4):953-63. DOI:10.1002/mrm.21749 · 3.57 Impact Factor
  • Aktuelle Neurologie 09/2008; 35. DOI:10.1055/s-0028-1086529 · 0.32 Impact Factor
  • Aktuelle Neurologie 09/2008; 35. DOI:10.1055/s-0028-1086600 · 0.32 Impact Factor
  • Aktuelle Neurologie 01/2007; 34. DOI:10.1055/s-2007-987504 · 0.32 Impact Factor
  • B W Kreher · J F Schneider · I Mader · E Martin · J Hennig · K A Il'yasov
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    ABSTRACT: A multidiffusion-tensor model (MDT) is presented containing two anisotropic and one isotropic diffusion tensors. This approach has the ability to detect areas of fiber crossings and resolve the direction of crossing fibers. The mean diffusivity and the ratio of the tensor compartments were merged to one independent parameter by fitting MDT to the diffusion-weighted intensities of a two-point data acquisition scheme. By an F-test between the errors of the standard single diffusion tensor and the more complex MDT, fiber crossings were detected and the more accurate model was chosen voxel by voxel. The performance of crossing detection was compared with the spherical harmonics approach in simulations as well as in vivo. Similar results were found in both methods. The MDT model, however, did not only detect crossings but also yielded the single fiber directions. The FACT algorithm and a probabilistic connectivity algorithm were extended to support the MDT model. For example, a mean angular error smaller than 10 degrees was found for the MDT model in a simulated fiber crossing with an SNR of 80. By tracking the corticospinal tract the MDT-based tracks reached a significantly greater area of the gyrus precentralis.
    Magnetic Resonance in Medicine 11/2005; 54(5):1216-25. DOI:10.1002/mrm.20670 · 3.57 Impact Factor
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    ABSTRACT: Quantitative diffusion tensor imaging (DTI) is a novel method of magnetic resonance (MR) imaging providing information on the brain’s microstructure in vivo. DTI can be effectively measured with modern clinical MR scanners. However, imaging sequence details required for accurateb matrix calculation and for following DTI quantification are normally unknown to the user. In this work, we investigated the accuracy ofb value approximation if theb matrix is calculated without taking into account the effect of imaging gradients. It was found that an error of more than 4% in DTI estimation arises for a quite typical brain imaging protocol. The errors in mean diffusivity and fractional anisotropy index depend on diffusion tensor shape and eigenvectors orientation and exceed noise level in DTI quantification. These errors however have a strong impact on fiber tracking — up to 30% difference was found between the fiber tracks corresponding to exact and approximate calculated DTI data. Since these errors are dependent on imaging parameters and sequence implementation, accurateb matrix calculations are important for adequate comparison between data acquired on different MR scanners and also for data measured with the different imaging protocols.
    Applied Magnetic Resonance 03/2005; 29(1):107-122. DOI:10.1007/BF03166958 · 1.17 Impact Factor
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    I Mader · V Glauche · H Mast · M Unfried · K A Il 'yasov · B W Kreher
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    ABSTRACT: Introduction Fiber tracking of the optic radiation is often hampered by the strong bending of the Meyer loop or by mis-tracking into the temporal pole, when the FACT algorithm is used. Another problem is the definition of regions of interest, because they have to be created manually in the individual anatomical data set of the patient. The purpose of this study was to overcome the problems of the FACT algorithm by using probability maps, and to create the seed points by a parameterised method. Methods Eight patients with hippocampal sclerosis previous to selective amygdalohippocampectomy were investigated by Diffusion Tensor Imaging (DTI) at a 3T whole body system. Parameters of the FLAIR SE EPI sequence were: TR 11.8s, TI 2257ms, TE 96ms, pixel size 2x2x2 mm 3 , b-value 1000s/mm 2 , and 61 diffusion encoding directions. Geometric distortions were automatically corrected [1]. Offline DTI analysis was performed by an in-house developed DTI and Fibertools Software Package [2] running under Matlab (The Mathworks, USA). For the depiction of the lateral geniculate corpus (CGL) and for the primary visual cortex (V1, Brodman area 17), anatomical maps from the WFU Pick Atlas [3] were normalised onto the b0 images of each individual patient data set by using spm5. These maps were imported into the DTI and Fibertools software [2] and used as seed points. Arising from these four seed points (CGL and V1 on both sides) probability maps were calculated for each optic radiation (www.uniklinik-freiburg.de/mr/live/arbeitsgruppen/diffusion/fibertools_en.html). To achieve a further quantitative evaluation of the found probabilities of the optic radiation, additional ROIs were created as orbitals around each CGL, with a diameter of 10mm, 30mm, and 50mm and a thickness of 20mm each. The first orbital is then resulting in a sphere, Fig. 1. From the cross section of the optic radiation and the orbitals, the calculated probabilities were extracted and pooled into a pathologic side and a healthy side. A log-it transformation according to P log = P 0 /1-P 0 was performed to achieve a normal distribution. Additionally, the number of found pixels containing probabilities of optic radiation was also extracted for the pathologic and the healthy side, respectively. A paired t-test was performed. Results In all patients, the optic radiation was equally depicted by probability maps in the orbitals nearest to the CGL and nearest to V1 on both sides. Only in the intermediate part (2 nd orbital) a significant difference was visible. Here, the probabilities were higher on the pathologic side, Table 1. The numbers of pixels containing the optic radiation was also equal on both sides. Discussion The preoperative detection of optic radiation in patients with hippocampal sclerosis is desired to protect this fiber bundle during operation. By using probabilistic maps and automated atlas-based definition of seed points (CGL and V1), the optic radiation in all patients was depicted, Fig. 2. As this fiber structure is not expected to change with hippocampal sclerosis, it is somewhat unexpected to find a significant difference of probabilities in the intermediate part of the optic radiation. This might be attributed to volume changes on the pathologic side (increasing side ventricle) and subsequent narrowing of the fiber bundle which may lead to a higher fractional anisotropy. The equal number of found pixels on both sides, however, contradicts this hypothesis, but also indicates a robust means for pre-operative depiction of optic radiation. (a) (b) Figure 1 Figure 2 Example for the cross-sections of the optic radiation and the Example of a probabilistic map of the optic radiation on the orbitals, from where the probabilities and numbers of found side of hippocampal sclerosis (a) and of the healthy side (b). Red pixels were selected. colour encodes high, blue low probabilities.
  • Source
    R Lorenz · B W Kreher · J Hennig · M E Bellemann · K A Il 'yasov
  • Schnell S · Saur D · Kreher BW · Hennig J · Burkhardt H · Kiselev VG
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    B W Kreher · I Mader · J Hennig · K A Il 'yasov