B W Kreher

University of Freiburg, Freiburg, Baden-Württemberg, Germany

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Publications (21)50.99 Total impact

  • Nina Gahr · Kassa Darge · Gabriele Hahn · Björn W Kreher · Miriam von Buiren · Markus Uhl ·
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    ABSTRACT: The purpose of this study was to assess the apparent diffusion coefficient (ADC) of neuroblastic tumours and to evaluate if the ADC can enable differentiation of neuroblastoma and ganglioneuroma/ganglioneuroblastoma. 16 histologically classified tumours (10 neuroblastomas and 6 ganglioneuroma/ganglioneuroblastoma) were investigated in 15 children. Diffusion-weighted echo-planar imaging was performed with a b-value of 800s/mm². The contrast of tumour tissue depicted with T2-weighted images and diffusion-weighted images was evaluated by means of region-of-interest (ROI) measurements and a calculation of the ADC by a software tool. The ADC of the psoas-muscle was measured to establish an internal standard, too. The mean ADC of the 10 neuroblastomas was 0.81×10⁻³mm²/s (SD 0.29×10⁻³mm²/s, range 0.39-1.47×10⁻³)mm²/s). The mean ADC of the four ganglioneuroma and two ganglioneuroblastoma was 1.6×10⁻³mm²/s (SD 0.340×10⁻³mm²/s, range 1.13-1.99)×10⁻³mm²/s. The difference was significant in the t-test (p=0.01). We found no ganglioneuroma or ganglioneuroblastoma with an ADC below 1.1×10⁻³mm²/s. There is a significant difference of the ADC of neuroblastoma compared to the ADC of ganglioneuroma/ganglioneuroblastoma. These first results suggest that the diffusion-weighted imaging could differentiate neuroblastoma and ganglioneuroma/ganglioneuroblastoma by calculating the ADC.
    European journal of radiology 05/2010; 79(3):443-6. DOI:10.1016/j.ejrad.2010.04.005 · 2.37 Impact Factor
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    ABSTRACT: Functional and structural alterations of the anterior cingulate cortex (ACC), a key region for emotional and cognitive processing, are associated with borderline personality disorder (BPD). However, the interhemispheric structural connectivity between the left and right ACC and between other prefrontal regions in this condition is unknown. We acquired diffusion-tensor imaging data from 20 healthy women and 19 women with BPD and comorbid attention-deficit hyperactivity disorder (ADHD). Interhemispheric structural connectivity between both sides of the ACC, dorsolateral prefrontal cortices and medial orbitofrontal cortices was assessed by a novel probabilistic diffusion tensor-based fiber tracking method. In the BPD group as compared with healthy controls, we found decreased interhemispheric structural connectivity between both ACCs in fiber tracts that pass through the anterior corpus callosum and connect dorsal areas of the ACCs. Decreased interhemispheric structural connectivity between both ACCs may be a structural correlate of BPD. Copyright (c) 2009 Elsevier Ireland Ltd. All rights reserved.
    Psychiatry Research 02/2010; 181(2):151-4. DOI:10.1016/j.pscychresns.2009.08.004 · 2.47 Impact Factor
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    ABSTRACT: Many recent studies reported altered functional connectivity within the frontolimbic circuitry in a wide range of neuropsychiatric disorders. However, functional connectivity must rely on structural connections. In this study we applied a novel probabilistic fiber tracking method to assess the structural connectivity between the amygdala and different prefrontal brain regions in vivo. Twenty healthy subjects were investigated with diffusion tensor imaging. Probabilistic fiber tracking was started from the amygdala and different prefrontal brain regions. Resulting probability maps were combined using an extended multiplication of probabilistic maps to identify the most probable anatomical pathways connecting these structures. We found one ventral pathway through the uncinate fascicle, connecting the amygdala and the medial and lateral orbitofrontal cortices. In addition to this ventral pathway, we depicted distinct dorsal pathways (medial and lateral), which connect the amygdala with the anterior cingulate cortex and the dorsolateral prefrontal cortex. The dorso-medial pathway proceeds through the inferior thalamic peduncle, while the dorsolateral pathway travels through the external capsule. We believe that our approach provides a promising tool to assess the integrity of specific structural connections in patients with neuropsychiatric disorders.
    Psychiatry Research 11/2009; 174(3):217-22. DOI:10.1016/j.pscychresns.2009.05.001 · 2.47 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
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    ABSTRACT: Built on an analogy between the visual and auditory systems, the following dual stream model for language processing was suggested recently: a dorsal stream is involved in mapping sound to articulation, and a ventral stream in mapping sound to meaning. The goal of the study presented here was to test the neuroanatomical basis of this model. Combining functional magnetic resonance imaging (fMRI) with a novel diffusion tensor imaging (DTI)-based tractography method we were able to identify the most probable anatomical pathways connecting brain regions activated during two prototypical language tasks. Sublexical repetition of speech is subserved by a dorsal pathway, connecting the superior temporal lobe and premotor cortices in the frontal lobe via the arcuate and superior longitudinal fascicle. In contrast, higher-level language comprehension is mediated by a ventral pathway connecting the middle temporal lobe and the ventrolateral prefrontal cortex via the extreme capsule. Thus, according to our findings, the function of the dorsal route, traditionally considered to be the major language pathway, is mainly restricted to sensory-motor mapping of sound to articulation, whereas linguistic processing of sound to meaning requires temporofrontal interaction transmitted via the ventral route.
    Proceedings of the National Academy of Sciences 12/2008; 105(46):18035-40. DOI:10.1073/pnas.0805234105 · 9.67 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
  • D Kratochvil · B. W Kreher · S Schnell · D Kümmerer · M. S Vry · R Umarova · C Weiller · D Saur ·

    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(S 2). DOI:10.1055/s-2007-987504 · 0.32 Impact Factor
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    Bernd André Jung · Björn W Kreher · Michael Markl · Jürgen Hennig ·
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    ABSTRACT: The spatial arrangement of myocardial fiber structure affects the mechanical and electrical properties of the heart. Therefore, information on the structure and dynamics of the orientation of the muscle fibers in the human heart might provide significant insight into principles of the mechanics of normal ventricular contraction and electrical propagation and may subsequently aid pre- and postsurgical evaluation of patients. Fiber orientation is inherently linked to cardiac wall motion, which can be measured with phase contrast magnetic resonance imaging (MRI), also termed tissue phase mapping (TPM). This study provides initial results of the visualization of velocity data with fiber tracking algorithms and discusses implications for the fiber orientations. In order to generate datasets with sufficient volume coverage and resolution TPM measurements with three-dimensional (3D) velocity encoding were executed during breath-hold periods and free breathing. Subsequent postprocessing evaluation with a tracking algorithm for acceleration fields derived from the velocity data was performed. Myocardial acceleration tracking illustrated the dynamics of fiber structure during four different phases of left ventricular performance, that include isovolumetric contraction (IVC), mid-systole, isovolumetric relaxation (IVR), and mid-diastole. Exact reconstruction of the myocardial fiber structure from velocity data requires mathematical modeling of spatiotemporal evolution of the velocity fields. 'Acceleration fibers' were reconstructed at these four phases during the cardiac cycle, and these findings may become (a) surrogate parameters in the normal ventricle, (b) baseline markers for subsequent clinical studies of abnormal hearts with altered architecture, and (c) may help to explain and illustrate functional features of cardiac performance in structural models like the helical ventricular myocardial band.
    European Journal of Cardio-Thoracic Surgery 05/2006; 29 Suppl 1:S158-64. DOI:10.1016/j.ejcts.2006.02.060 · 3.30 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
  • K. A. Il’yasov · G. Barta · B. W. Kreher · M. E. Bellemann · J. Hennig ·
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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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    R Lorenz · B W Kreher · J Hennig · M E Bellemann · K A Il 'yasov ·

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    ABSTRACT: Die anatomische Zuordnung von einzelnen Thalamuskernen ist auf T1- und T2-gewichteten Aufnahmen aufgrund fehlender Signalunterschiede zwischen den einzelnen Kernen nicht mglich. Mittels Diffusions-Tensor-Bildgebung (DTI) ist es mglich geworden, Thalamuskerne aufgrund der Richtung der in ihnen verlaufenden Axone und aufgrund ihrer Faserverbindungen zu charakterisieren, wenngleich diese Methoden nicht generell zugnglich sind. Es bestehen weiterhin Schwierigkeiten bei der Abgrenzung einzelner Kerngebiete anhand von farbkodierten Darstellungen der Diffusionshauptrichtungen. Deshalb wurde in dieser Studie versucht, mittels eines einfachen Tracking-Algorithmus Thalamuskerngebiete anhand ihrer axonalen Verbindungen darzustellen, um zu berprfen, inwieweit eine Charakterisierung einzelner Thalamuskerne mglich ist.The anatomic distinction of particular thalamic nuclei is not straightforward on T1- and T2-weighted images due to similar signal intensities of the different nuclei. Recently, an assignment of different thalamic nuclei according to the course of their axons and their fiber connections has been shown. These methods, however, are not generally available. There are still problems to delineate the borders of the particular thalamic nuclei based solely on color-coded main diffusion directions. Thus, fiber tracking by a simple tracking algorithm was used to observe axonal connections to the cortex in order to characterize particular thalamic nuclei.
    Clinical Neuroradiology 01/2004; 14(3):194-201. DOI:10.1007/s00062-004-5388-0 · 2.25 Impact Factor
  • Schnell S · Saur D · Kreher BW · Hennig J · Burkhardt H · Kiselev VG ·

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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.
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    B W Kreher · I Mader · J Hennig · K A Il 'yasov ·