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Object: We aimed to modify our previously published method for arterial input function measurements for evaluation of cerebral perfusion (dynamic susceptibility contrast MRI) such that it can be applied in humans in a clinical setting., Materials and Methods: Similarly to our previous work, a conventional measurement sequence for dynamic susceptibility contrast MRI is extended with an additional measurement slice at the neck. Measurement parameters at this slice were optimized for the blood signal (short echo time, background suppression, magnitude and phase images). Phase-based evaluation of the signal in the carotid arteries is used to obtain quantitative arterial input functions., Results: In all pilot measurements, quantitative arterial input functions were obtained. The resulting absolute perfusion parameters agree well with literature values (gray and white matter mean values of 46 and 24 mL/100 g/min, respectively, for cerebral blood flow and 3.0% and 1.6%, respectively, for cerebral blood volume)., Conclusions: The proposed method has the potential to quantify arterial input functions in the carotid arteries from a direct measurement without any additional normalization.

Introduction: Despite their importance in reward, motivation, and learning there is only sparse anatomical knowledge about the human medial forebrain bundle (MFB) and the connectivity of the ventral tegmental area (VTA). A thorough anatomical and microstructural description of the reward related PFC/OFC regions and their connection to the VTA - the superolateral branch of the MFB (slMFB) - is however mandatory to enable an interpretation of distinct therapeutic effects from different interventional treatment modalities in neuropsychiatric disorders (DBS, TMS etc.). This work aims at a normative description of the human MFB (and more detailed the slMFB) anatomy with respect to distant prefrontal connections and microstructural features., Methods and Material: Healthy subjects ( n  = 55; mean age ± SD, 40 ± 10 years; 32 females) underwent high resolution anatomical magnetic resonance imaging including diffusion tensor imaging. Connectivity of the VTA and the resulting slMFB were investigated on the group level using a global tractography approach. The Desikan/Killiany parceling (8 segments) of the prefrontal cortex was used to describe sub-segments of the MFB. A qualitative overlap with Brodmann areas was additionally described. Additionally, a pure visual analysis was performed comparing local and global tracking approaches for their ability to fully visualize the slMFB., Results: The MFB could be robustly described both in the present sample as well as in additional control analyses in data from the human connectome project. Most VTA- connections reached the superior frontal gyrus, the middel frontal gyrus and the lateral orbitofrontal region corresponding to Brodmann areas 10, 9, 8, 11, and 11m. The projections to these regions comprised 97% (right) and 98% (left) of the total relative fiber counts of the slMFB., Discussion: The anatomical description of the human MFB shows far reaching connectivity of VTA to reward-related subcortical and cortical prefrontal regions - but not to emotion-related regions on the medial cortical surface - realized via the superolateral branch of the MFB. Local tractography approaches appear to be inferior in showing these far-reaching projections. Since these local approaches are typically used for surgical targeting of DBS procedures, the here established detailed map might - as a normative template - guide future efforts to target deep brain stimulation of the slMFB in depression and other disorders related to dysfunction of reward and reward-associated learning.

The identification of pathological atrophy in MRI scans requires specialized training, which is scarce outside dedicated centers. We sought to investigate the clinical usefulness of computer-generated representations of local grey matter (GM) loss or increased volume of cerebral fluids (CSF) as normalized deviations (z-scores) from healthy aging to either aid human visual readings or directly detect pathological atrophy. Two experienced neuroradiologists rated atrophy in 30 patients with Alzheimer's disease (AD), 30 patients with frontotemporal dementia (FTD), 30 with dementia due to Lewy-body disease (LBD) and 30 healthy controls (HC) on a three-point scale in 10 anatomical regions as reference gold standard. Seven raters, varying in their experience with MRI diagnostics rated all cases on the same scale once with and once without computer-generated volume deviation maps that were overlaid on anatomical slices. In addition, we investigated the predictive value of the computer generated deviation maps on their own for the detection of atrophy as identified by the gold standard raters. Inter and intra-rater agreements of the two gold standard raters were substantial (Cohen's kappa κ > 0.62). The intra-rater agreement of the other raters ranged from fair (κ = 0.37) to substantial (κ = 0.72) and improved on average by 0.13 (0.57 < κ < 0.87) when volume deviation maps were displayed. The seven other raters showed good agreement with the gold standard in regions including the hippocampus but agreement was substantially lower in e.g. the parietal cortex and did not improve with the display of atrophy scores. Rating speed increased over the course of the study and irrespective of the presentation of voxel-wise deviations. Automatically detected large deviations of local volume were consistently associated with gold standard atrophy reading as shown by an area under the receiver operator characteristic of up to 0.95 for the hippocampus region. When applying these test characteristics to prevalences typically found in a memory clinic, we observed a positive or negative predictive value close to or above 0.9 in the hippocampus for almost all of the expected cases. The volume deviation maps derived from CSF volume increase were generally better in detecting atrophy. Our study demonstrates an agreement of visual ratings among non-experts not further increased by displaying, region-specific deviations of volume. The high predictive value of computer generated local deviations independent from human interaction and the consistent advantages of CSF-over GM-based estimations should be considered in the development of diagnostic tools and indicate clinical utility well beyond aiding visual assessments., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: To evaluate whether white matter tracts within the Papez circuit are altered in patients with unilateral hippocampal sclerosis (HS)., Methods: Twenty patients with histologically proven unilateral HS and 20 age-matched controls were studied with a 3T Epilepsy-dedicated MRI protocol including a MPRAGE sequence for hippocampus volumetry and a diffusion tensor imaging (DTI) sequence (61 diffusion-encoding directions, 2×2×2mm 3 voxels) for diffusion tensor tractography (DTT). An energy-based global tracking algorithm was used to calculate streamline counts (SC) and fractional anisotropy (FA) of cingulate, fornix, and mammillo-thalamic tracts, respectively., Results: Sclerotic hippocampi were significantly smaller compared to the contralateral side and to age-matched controls. Cingulum SC but not FA were reduced on the hippocampal sclerosis (258+81.0) and contralateral side (271+85.6) compared to age-matched controls (447+138)., Conclusion: Focusing on white matter tracts of the Papez circuit we showed that in patients with intractable temporal lobe epilepsy unilateral hippocampal sclerosis is associated with a bilateral reduction of cingulum association fibers projecting from the cingulate gyrus to the parahippocampal gyrus., (Copyright © 2017 Elsevier B.V. All rights reserved.)

In this initial work, the in vivo degradation of 17 O-labeled glucose was studied during cellular glycolysis. To monitor cellular glucose metabolism, direct 17 O-magnetic resonance spectroscopy (MRS) was used in the mouse brain at 9.4 T. Non-localized spectra were acquired with a custom-built transmit/receive (Tx/Rx) two-turn surface coil and a free induction decay (FID) sequence with a short TR of 5.4 ms. The dynamics of labeled oxygen in the anomeric 1-OH and 6-CH 2 OH groups was detected using a Hankel-Lanczos singular value decomposition (HLSVD) algorithm for water suppression. Time-resolved 17 O-MRS (temporal resolution, 42/10.5 s) was performed in 10 anesthetized (1.25% isoflurane) mice after injection of a 2.2 M solution containing 2.5 mg/g body weight of differently labeled 17 O-glucose dissolved in 0.9% physiological saline. From a pharmacokinetic model fit of the H 2 17 O concentration-time course, a mean apparent cerebral metabolic rate of 17 O-labeled glucose in mouse brain of CMR Glc  = 0.07 ± 0.02 μmol/g/min was extracted, which is of the same order of magnitude as a literature value of 0.26 ± 0.06 μmol/g/min reported by 18 F-fluorodeoxyglucose ( 18 F-FDG) positron emission tomography (PET). In addition, we studied the chemical exchange kinetics of aqueous solutions of 17 O-labeled glucose at the C1 and C6 positions with dynamic 17 O-MRS. In conclusion, the results of the exchange and in vivo experiments demonstrate that the C6- 17 OH label in the 6-CH 2 OH group is transformed only glycolytically by the enzyme enolase into the metabolic end-product H 2 17 O, whereas C1- 17 OH ends up in water via direct hydrolysis as well as glycolysis. Therefore, dynamic 17 O-MRS of highly labeled 17 O-glucose could provide a valuable non-radioactive alternative to FDG PET in order to investigate glucose metabolism., (Copyright © 2017 John Wiley & Sons, Ltd.)

Oxygen metabolism is altered in brain tumor regions and is quantified by the cerebral metabolic rate of oxygen consumption (CMRO 2 ). Direct dynamic 17 O MRI with inhalation of isotopically enriched 17 O 2 gas can be used to quantify CMRO 2 ; however, pixel-wise CMRO 2 quantification in human brain is challenging due to low natural abundance of 17 O isotope and, thus, the low signal-to-noise ratio (SNR) of 17 O MR images. To test the feasibility CMRO 2 mapping at a clinical 3 T MRI system, a new iterative reconstruction was proposed, which uses the edge information contained in a co-registered 1 H gradient image to construct a non-homogeneous anisotropic diffusion (AD) filter. AD-constrained reconstruction of 17 O MR images was compared to conventional Kaiser-Bessel gridding without and with Hanning filtering, and to iterative reconstruction with a total variation (TV) constraint. For numerical brain phantom and in two in vivo data sets of one healthy volunteer, AD-constrained reconstruction provided 17 O images with improved resolution of fine brain structures and resulted in higher SNR. CMRO 2 values of 0.78 - 1.55µmol/g tissue /min (white brain matter) and 1.03 - 2.01µmol/g tissue /min (gray brain matter) as well as the CMRO 2 maps are in a good agreement with the results of 15 O-PET and 17 O MRI at 7 T and at 9.4 T. In conclusion, the proposed AD-constrained reconstruction enabled calculation of 3D CMRO 2 maps at 3 T MRI system, which is an essential step towards clinical translation of 17 O MRI for non-invasive CMRO 2 quantification in tumor patients., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Objective: The aim of this study was to develop a rapid and fully automatic infarct core and tissue at risk volumetry approach in acute ischemic stroke., Methods: We evaluated an algorithm in which segmentation was restricted to 1 hemisphere and the potential lesion characterized on the basis of the perfusion parameter Tmax with a region-wise comparison of local histograms to its mirrored counterpart., Results: We applied the "Tmax inside" method to 30 cases of a public data set with ground-truth segmentations for diffusion-weighted and perfusion magnetic resonance imaging. Lesions were robustly identified with significantly higher dice coefficients (apparent diffusion coefficient, 0.83 ± 0.22; Tmax, 0.80 ± 0.05, compared with 0.53 ± 0.27 and 0.56 ± 0.18) than for a global thresholding approach., Conclusions: The proposed "Tmax inside" method is superior to the commonly used global thresholding approach. Furthermore, the method allows evaluating changes in cerebral blood volume and blood flow by taking the counterpart in the healthy hemisphere as a patient-individual reference.

Objective: To distinguish white matter remodeling directly induced by stroke lesion from that evoked by remote network dysfunction, using spatial neglect as a model., Methods: We examined 24 visual neglect/extinction patients and 17 control patients combining comprehensive analyses of diffusion tensor metrics and global fiber tracking with neuropsychological testing in the acute (6.3 ± 0.5 days poststroke) and chronic (134 ± 7 days poststroke) stroke phases., Results: Compared to stroke controls, patients with spatial neglect/extinction displayed longitudinal white matter alterations with 2 defining signatures: (1) perilesional degenerative changes characterized by congruently reduced fractional anisotropy and increased radial diffusivity (RD), axial diffusivity, and mean diffusivity, all suggestive of direct axonal damage by lesion and therefore nonspecific for impaired attention network and (2) transneuronal changes characterized by an increased RD in contralesional frontoparietal and bilateral occipital connections, suggestive of primary periaxonal involvement; these changes were distinctly related to the degree of unrecovered neglect symptoms in chronic stroke, hence emerging as network-specific alterations., Conclusions: The present data show how stroke entails global alterations of lesion-spared network architecture over time. Sufficiently large lesions of widely interconnected association cortex induce distinct, large-scale structural reorganization in domain-specific network connections. Besides their relevance to unrecovered domain-specific symptoms, these effects might also explain mechanisms of domain-general deficits in stroke patients, pointing to potential targets for therapeutic intervention., (© 2017 American Academy of Neurology.)

Diffusion-sensitized magnetic resonance imaging probes the cellular structure of the human brain, but the primary microstructural information gets lost in averaging over higher-level, mesoscopic tissue organization such as different orientations of neuronal fibers. While such averaging is inevitable due to the limited imaging resolution, we propose a method for disentangling the microscopic cell properties from the effects of mesoscopic structure. We further avoid the classical fitting paradigm and use supervised machine learning in terms of a Bayesian estimator to estimate the microstructural properties. The method finds detectable parameters of a given microstructural model and calculates them within seconds, which makes it suitable for a broad range of neuroscientific applications., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

Connectomics of brain disorders seeks to reveal how altered brain function emerges from the architecture of cerebral networks; however the causal impact of targeted cellular damage on the whole brain functional and structural connectivity remains unknown. In the central nervous system, demyelination is typically the consequence of an insult targeted at the oligodendrocytes, the cells forming and maintaining the myelin. This triggered perturbation generates cascades of pathological events that most likely alter the brain connectome. Here we induced oligodendrocyte death and subsequent demyelinating pathology via cuprizone treatment in mice and combining mouse brain resting state functional Magnetic Resonance Imaging and diffusion tractography we established functional and structural pathology-to-network signatures. We demonstrated that demyelinated brain fundamentally reorganizes its intrinsic functional connectivity paralleled by widespread damage of the structural scaffolding. We evidenced default mode-like network as core target of demyelination-induced connectivity modulations and hippocampus as the area with strongest connectional perturbations., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Purpose: To develop a fast and stable method for correcting the gibbs-ringing artifact., Methods: Gibbs-ringing is a well-known artifact which manifests itself as spurious oscillations in the vicinity of sharp image gradients at tissue boundaries. The origin can be seen in the truncation of k-space during MRI data-acquisition. Correction techniques like Gegenbauer reconstruction or extrapolation methods aim at recovering these missing data. Here, we present a simple and robust method which exploits a different view on the Gibbs-phenomenon: The truncation in k-space can be interpreted as a convolution of the underlying image with a sinc-function. As the image is reconstructed on a discretized grid, the severity of the ringing artifacts depends on how this grid is located with respect to the edge and the oscillation pattern of the function. We propose to reinterpolate the image based on local, subvoxel-shifts to sample the ringing pattern at the zero-crossings of the oscillating sinc-function., Results: With the proposed method, the artifact can simply, effectively, and robustly be removed with a minimal amount of image smoothing., Conclusions: The robustness of the method suggests it as a suitable candidate for an implementation in the standard image processing pipeline in clinical routine. Magn Reson Med 76:1574-1581, 2016. © 2015 International Society for Magnetic Resonance in Medicine., (© 2015 International Society for Magnetic Resonance in Medicine.)

Connectome genetics seeks to uncover how genetic factors shape brain functional connectivity; however, the causal impact of a single gene's activity on whole-brain networks remains unknown. We tested whether the sole targeted deletion of the mu opioid receptor gene (Oprm1) alters the brain connectome in living mice. Hypothesis-free analysis of combined resting-state fMRI diffusion tractography showed pronounced modifications of functional connectivity with only minor changes in structural pathways. Fine-grained resting-state fMRI mapping, graph theory, and intergroup comparison revealed Oprm1-specific hubs and captured a unique Oprm1 gene-to-network signature. Strongest perturbations occurred in connectional patterns of pain/aversion-related nodes, including the mu receptor-enriched habenula node. Our data demonstrate that the main receptor for morphine predominantly shapes the so-called reward/aversion circuitry, with major influence on negative affect centers., Competing Interests: The authors declare no conflict of interest.

Early and reliable detection of pulmonary embolism (PE) is critical for improving patient morbidity and mortality. The desire for low-threshold screening for pulmonary embolism is contradicted by unfavorable radiation of currently used computed tomography or nuclear techniques, while standard magnetic resonance imaging still struggles to provide sufficient diagnostic sensitivity in the lung. In this study we evaluate a molecular-targeted contrast agent against activated platelets for non-invasive detection of murine pulmonary thromboembolism using magnetic resonance imaging. By intravenous injection of human thrombin, pulmonary thromboembolism were consistently induced as confirmed by immunohistochemistry of the lung. Magnetic resonance imaging after thrombin injection showed local tissue edema in weighted images which co-localized with the histological presence of pulmonary thromboembolism. Furthermore, injection of a functionalized contrast agent targeting activated platelets provided sensitive evidence of focal accumulation of activated platelets within the edematous area, which, ex vivo, correlated well with the size of the pulmonary embolism. In summary, we here show delivery and specific binding of a functionalized molecular contrast agent against activated platelets for targeting pulmonary thromboembolism. Going forward, molecular imaging may provide new opportunities to increase sensitivity of magnetic resonance imaging for detection of pulmonary embolism.

In a longitudinal mouse study we evaluated whether diffusion tensor imaging (DTI) can monitor microstructural changes after administration of the neuromodulating drug EPO and whether erythropoietin (EPO) has an effect on cognitive performance. Twelve mice (2 groups with 6 mice each) were scanned in a 7T Bruker Biospin animal scanner with a highly resolved DTI sequence before and 16 days after intraperitoneal injections of EPO or saline. All mice underwent behavioral testing (Morris water maze) and histologic evaluation of hippocampal and corpus callosum cell proliferation and oligodendrogenesis. Whole brain DTI analysis showed significant Trace, RD and AD decrease within the dentate gyrus, subiculum, primary motor, somatosensory, and supplementary somatosensory areas and FA increase in the hippocampus, corpus callosum, and fimbria fornix in EPO treated mice only. ROI-based DTI analysis showed significant Trace and RD decrease and FA increase only in the corpus callosum of EPO treated mice, whereas in the dentate gyrus significant Trace, RD, and AD decrease occurred in both, EPO- and control-group. Behavioral tests showed that EPO treated mice performed better and learned faster than controls. Histologically, the number of BrdU-positive nuclei and optical density of DCX-labeled juvenile neurons significantly increased within the dentate gyrus, corpus callosum and fimbria fornix and the number of NG2-positive oligodendrocyte progenitors in corpus callosum and fimbria fornix, respectively. In conclusion we were able to monitor microstructural changes with DTI and showed EPO treatment-related alterations correlating with enhanced dentate gyrus and corpus callosum cell proliferation and better learning capabilities., (Copyright © 2015 Elsevier Inc. All rights reserved.)

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., (Copyright © 2015 Elsevier Inc. All rights reserved.)

The g-ratio, quantifying the ratio between the inner and outer diameters of a fiber, is an important microstructural characteristic of fiber pathways and is functionally related to conduction velocity. We introduce a novel method for estimating the MR g-ratio non-invasively across the whole brain using high-fidelity magnetization transfer (MT) imaging and single-shell diffusion MRI. These methods enabled us to map the MR g-ratio in vivo across the brain's prominent fiber pathways in a group of 37 healthy volunteers and to estimate the inter-subject variability. Effective correction of susceptibility-related distortion artifacts was essential before combining the MT and diffusion data, in order to reduce partial volume and edge artifacts. The MR g-ratio is in good qualitative agreement with histological findings despite the different resolution and spatial coverage of MRI and histology. The MR g-ratio holds promise as an important non-invasive biomarker due to its microstructural and functional relevance in neurodegeneration.

Introduction: Several major association fiber tracts are known to be part of the language processing system. There is evidence that high angular diffusion-based MRI is able to separate these fascicles in a constant way. In this study, we wanted to proof this thesis using a novel whole brain "global tracking" approach and to test for possible lateralization., Methods: Global tracking was performed in six healthy right-handed volunteers for the arcuate fascicle (AF), the medial longitudinal fascicle (MdLF), the inferior fronto-occipital fascicle (IFOF), and the inferior longitudinal fascicle (ILF). These fiber tracts were characterized quantitatively using the number of streamlines (SL) and the mean fractional anisotropy (FA)., Results: We were able to characterize the AF, the MdLF, the IFOF, and the ILF consistently in six healthy volunteers using global tracking. A left-sided dominance (LI > 0.2) for the AF was found in all participants. The MdLF showed a left-sided dominance in four participants (one female, three male). Regarding the FA, no lateralization (LI > 0.2) could be shown in any of the fascicles., Conclusion: Using a novel global tracking algorithm we confirmed that the courses of the primary language processing associated fascicles can consistently be differentiated. Additionally we were able to show a streamline-based left-sided lateralization in the AF of all right-handed healthy subjects.

Purpose: The correction of chemical shift artifacts in MR images of fluorinated molecules with a multi-resonance spectrum is investigated. The goal is to find a deconvolution method which is capable of correcting the artifact, thereby enhancing signal-to-noise ratio (SNR) and revealing signal that vanishes in the noise in the original image., Theory and Methods: Simulations for inspecting the influence of MRI acquisition parameters on the possibility to correct the artifact are performed. Artifact correction is studied on the spectrum of a perfluorocarbon compound by means of deconvolution of complex images with a measured or an optimized point spread function and by using lasso regularization., Results: Distinct parameter settings for image acquisition are identified that should be avoided for successful deconvolution. With in vitro and in vivo images it is shown that the SNR of the corrected image can be increased significantly by 20-50% by a regularized deconvolution of the artifact image and (19) F signal can be revealed from noise., Conclusion: By deconvolution, SNR can be enhanced as compared to an image which only exploits the strongest peak of the spectrum. Thus, the limit of detection of the (19) F signal can be lowered by exciting all resonances and by means of correcting the chemical shift artifact., (© 2014 Wiley Periodicals, Inc.)

Complex cognitive abilities such as planning are known to critically rely on activity of bilateral mid-dorsolateral prefrontal cortex (mid-dlPFC). However, the functional relevance of the structural connectivity between left and right mid-dlPFC is yet unknown. Here, we applied global tractography to derive streamline counts as estimates of the structural connectivity between mid-dlPFC homologs and related it to planning performance in the Tower of London task across early to midadulthood, assuming a moderating effect of age. Multiple regression analyses with interaction effects revealed that streamline counts between left and right mid-dlPFC were negatively associated with planning performance specifically in early postadolescence. From the fourth life decade on, there was a trend for a reversed, positive association. These differential findings were corroborated by converging results from fractional anisotropy and white-matter density estimates in the genu of the corpus callosum where fibers connecting mid-dlPFC homologs traversed. Moreover, the results for streamline counts were regionally specific, marking the strength of mid-dlPFC connectivity as critical in predicting interindividual differences in planning performance across different stages of adulthood. Taken together, present findings provide first evidence for nonadditive effects of age on the relation between complex cognitive abilities and the structural connectivity of mid-dlPFC homologs., (© The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Tubular shaped networks appear not only in medical images like X-ray-, time-of-flight MRI- or CT-angiograms but also in microscopic images of neuronal networks. We present EMILOVE (Efficient Monte-carlo Image-analysis for the Location Of Vascular Entity), a novel modeling algorithm for tubular networks in biomedical images. The model is constructed using tablet shaped particles and edges connecting them. The particles encode the intrinsic information of tubular structure, including position, scale and orientation. The edges connecting the particles determine the topology of the networks. For simulated data, EMILOVE was able to accurately extract the tubular network. EMILOVE showed high performance in real data as well; it successfully modeled vascular networks in real cerebral X-ray and time-of-flight MRI angiograms. We also show some promising, preliminary results on microscopic images of neurons.

Purpose: We recently presented a method for the quantitative measurement of the arterial input function which allows for determination of absolute cerebral blood flow (CBF) values without adjustable parameters. The aim of the present work is to estimate absolute CBF values by using this new technique and to compare it with the gold standard for cerebral perfusion, H(2)(15)O positron emission tomography., Methods: Pigs (13) were comparatively investigated by each method performing multiple measurement runs. The reproducibility of both methods was assessed by a voxel-wise correlation of repeated measurements. An intersubject evaluation was performed on median whole-brain CBF estimates., Results: The mean CBF (MRI) was 20±4mL/100g/min  for gray matter, the mean CBF (positron emission tomography) was 24±6mL/100g/min  for gray and white matter. The reproducibility for MRI correlated with r = 0.85 and P<0.0001, for positron emission tomography with r = 0.76 and P<0.0001. The correlation for the median whole-brain CBF in MRI and positron emission tomography was r = 0.60 and P = 0.04., Conclusions: The proposed method allows for determination of quantitative CBF without normalization factors. The relatively low estimates of absolute CBF most likely results from the higher age of the pigs as compared to other studies. The intermediate correlation between both methods is caused by physiological intraindividual fluctuations of the CBF and by a limited reproducibility of both methods., (© 2013 Wiley Periodicals, Inc.)

An emerging field of human brain imaging deals with the characterization of the connectome, a comprehensive global description of structural and functional connectivity within the human brain. However, the question of how functional and structural connectivity are related has not been fully answered yet. Here, we used different methods to estimate the connectivity between each voxel of the cerebral cortex based on functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data in order to obtain observer-independent functional-structural connectomes of the human brain. Probabilistic fiber-tracking and a novel global fiber-tracking technique were used to measure structural connectivity whereas for functional connectivity, full and partial correlations between each voxel pair's fMRI-timecourses were calculated. For every voxel, two vectors consisting of functional and structural connectivity estimates to all other voxels in the cortex were correlated with each other. In this way, voxels structurally and functionally connected to similar regions within the rest of the brain could be identified. Areas forming parts of the 'default mode network' (DMN) showed the highest agreement of structure-function connectivity. Bilateral precuneal and inferior parietal regions were found using all applied techniques, whereas the global tracking algorithm additionally revealed bilateral medial prefrontal cortices and early visual areas. There were no significant differences between the results obtained from full and partial correlations. Our data suggests that the DMN is the functional brain network, which uses the most direct structural connections. Thus, the anatomical profile of the brain seems to shape its functional repertoire and the computation of the whole-brain functional-structural connectome appears to be a valuable method to characterize global brain connectivity within and between populations., (© 2013 Elsevier Inc. All rights reserved.)

Evaluation of blood supply of different organs relies on labeling blood with a suitable tracer. The tracer kinetics is linear: Tracer concentration at an observation site is a linear response to an input somewhere upstream the arterial flow. The corresponding impulse response functions are currently treated empirically without incorporating the relation to the vascular morphology of an organ. In this work we address this relation for the first time. We demonstrate that the form of the response function in the entire arterial tree is reduced to that of individual vessel segments under approximation of good blood mixing at vessel bifurcations. The resulting expression simplifies significantly when the geometric scaling of the vascular tree is taken into account. This suggests a new way to access the vascular morphology in vivo using experimentally determined response functions. However, it is an ill-posed inverse problem as demonstrated by an example using measured arterial spin labeling in large brain arteries. We further analyze transport in individual vessel segments and demonstrate that experimentally accessible tracer concentration in vessel segments depends on the measurement principle. Explicit expressions for the response functions are obtained for the major middle part of the arterial tree in which the blood flow in individual vessel segments can be treated as laminar. When applied to the analysis of regional cerebral blood flow measurements for which the necessary arterial input is evaluated in the carotid arteries, present theory predicts about 20% underestimation, which is in agreement with recent experimental data.

Visual neglect results from dysfunction within the spatial attention network. The structural connectivity in undamaged brain tissue in neglect has barely been investigated until now. In the present study, we explored the microstructural white matter characteristics of the contralesional hemisphere in relation to neglect severity and recovery in acute stroke patients. We compared age-matched healthy subjects and three groups of acute stroke patients (9 ± 0.5 days after stroke): (i) patients with nonrecovered neglect (n = 12); (ii) patients with rapid recovery from initial neglect (within the first week post-stroke, n = 7), (iii) stroke patients without neglect (n = 17). We analyzed the differences between groups in grey and white matter density and fractional anisotropy (FA) and used fiber tracking to identify the affected fibers. Patients with nonrecovered neglect differed from those with rapid recovery by FA-reduction in the left inferior parietal lobe. Fibers passing through this region connect the left-hemispheric analogues of the ventral attention system. Compared with healthy subjects, neglect patients with persisting neglect had FA-reduction in the left superior parietal lobe, optic radiation, and left corpus callosum/cingulum. Fibers passing through these regions connect centers of the left dorsal attention system. FA-reduction in the identified regions correlated with neglect severity. The study shows for the first time white matter changes within the spatial attention system remote from the lesion and correlating with the extent and persistence of neglect. The data support the concept of neglect as disintegration within the whole attention system and illustrate the dynamics of structural-functional correlates in acute stroke., (Copyright © 2014 Wiley Periodicals, Inc.)

Purpose: Currently, the time required for image reconstruction is prohibitively long if data are acquired using multidimensional imaging trajectories that make use of multichannel systems equipped with nonlinear gradients. Methods are presented that reduce the computational complexity of the iterative time-domain reconstruction algorithm down from O(N(4)) to O(N(3))., Theory: For generalized projections, a large class of multidimensional imaging trajectories, the encoding matrix can be focused to sparse bands by introducing an appropriate filter function along the frequency-encoding direction. The reconstruction can be speeded up by ignoring values below a predefined threshold level., Methods: Two methods are presented that differ in how the filter is incorporated into the reconstruction algorithm. The first method represents, without implementation of a threshold, a weighted version of the time-domain method, while the second method is equivalent to it., Results: Simulation and measurement results show that image reconstruction from high-resolution imaging data can be speeded up by up to two orders of magnitude. While the weighted reconstruction requires more iterations to reach an optimum than the second method, it is less sensitive to thresholding., Conclusion: For complex spatial encoding strategies that involve nonlinear gradient fields, fast and accurate image reconstruction methods are provided that are particularly efficient for high-resolution anatomical imaging., (Copyright © 2013 Wiley Periodicals, Inc.)

Purpose: Respiration induced dynamic field map changes in the brain are quantified and the influence on the magnitude signal (physiological noise) is investigated. Dynamic off-resonance correction allows to reduce the signal fluctuations overlaying the blood oxygenation level dependent signal in T2*-weighted functional imaging., Theory and Methods: A single-shot whole brain imaging technique with 100 ms temporal resolution was used to measure dynamic off-resonance maps that were calculated from the incremental changes of the image phase. These off-resonance maps are then used to dynamically update the off-resonance corrected reconstruction., Results: A global resonance offset and a pronounced gradient in head-foot direction were identified as the main components of the change during a respiration cycle. On average, correction for these fluctuations decreases the magnitude fluctuations by around 30%., Conclusion: Single shot 3D imaging allows for a robust quantification of dynamic off-resonance changes in the brain. Correction for these fluctuations removes the physiological noise component associated with dynamic point spread function changes., (Copyright © 2013 Wiley Periodicals, Inc.)

Background and Purpose: Fiber tractography is increasingly used in the preoperative evaluation of endangered fiber bundles. From a clinical point of view, an accurate and methodologically transparent procedure is desired. Our aim was to evaluate the recently described global tracking algorithm compared with other established methods, such as deterministic and probabilistic tractography., Materials and Methods: Twenty patients, candidates for excision of epileptogenic lesions, were subjected to higher-angular resolution diffusion imaging-based fiber tractography. Seed points were created without manual bias, predominantly by FreeSurfer and voxel-based atlases. We focused on 2 important fiber bundles, namely the descending motor pathways and the optic radiation. Postoperatively, the accuracy of the predicted fiber route was controlled by structural MR imaging and by inflicted functional deficits., Results: Among the 3 evaluated methods, global tracking was the only method capable of reconstructing the full extent of the descending motor pathways, including corticobulbar fibers from the area of face representation. Still, probabilistic tractography depicted the optic radiation better, especially the Meyer loop. The deterministic algorithm performed less adequately., Conclusions: The probabilistic method seems to be the best balance between computational time and effectiveness and seems to be the best choice in most cases, particularly for the optic radiation. If, however, a detailed depiction of the fiber anatomy is intended and tract crossings are implicated, then the computationally time-consuming global tracking should be preferred.

Validation is arguably the bottleneck in the diffusion magnetic resonance imaging (MRI) community. This paper evaluates and compares 20 algorithms for recovering the local intra-voxel fiber structure from diffusion MRI data and is based on the results of the "HARDI reconstruction challenge" organized in the context of the "ISBI 2012" conference. Evaluated methods encompass a mixture of classical techniques well known in the literature such as diffusion tensor, Q-Ball and diffusion spectrum imaging, algorithms inspired by the recent theory of compressed sensing and also brand new approaches proposed for the first time at this contest. To quantitatively compare the methods under controlled conditions, two datasets with known ground-truth were synthetically generated and two main criteria were used to evaluate the quality of the reconstructions in every voxel: correct assessment of the number of fiber populations and angular accuracy in their orientation. This comparative study investigates the behavior of every algorithm with varying experimental conditions and highlights strengths and weaknesses of each approach. This information can be useful not only for enhancing current algorithms and develop the next generation of reconstruction methods, but also to assist physicians in the choice of the most adequate technique for their studies.

We present a novel method to incorporate prior knowledge into normalized cuts. The prior is incorporated into the cost function by maximizing the similarity of the prior to one partition and the dissimilarity to the other. This simple formulation can also be extended to multiple priors to allow the modeling of the shape variations. A shape model obtained by PCA on a training set can be easily integrated into the new framework. This is in contrast to other methods that usually incorporate prior knowledge by hard constraints during optimization. The eigenvalue problem inferred by spectral relaxation is not sparse, but can still be solved efficiently. We apply this method to biomedical data sets as well as natural images of people from a public database and compare it with other normalized cut based segmentation algorithms. We demonstrate that our method gives promising results and can still give a good segmentation even when the prior is not accurate.

One overarching challenge of clinical magnetic resonance imaging (MRI) is to quantify tissue structure at the cellular scale of micrometers, based on an MRI acquisition with a millimeter resolution. Diffusion MRI (dMRI) provides the strongest sensitivity to the cellular structure. However, interpreting dMRI measurements has remained a highly ill-posed inverse problem. Here we propose a framework that resolves the above challenge for human white matter fibers, by unifying intra-voxel mesoscopic modeling with global fiber tractography. Our algorithm is based on a Simulated Annealing approach which simultaneously optimizes diffusion parameters and fiber locations. Each fiber carries its by their individual set of diffusion parameters which allows to link them structural relationships.

Purpose: Global tracking (GT) is a recently published fibre tractography (FT) method that takes simultaneously all fibres into account during their reconstruction. The purpose of this study was to compare this new method with fibre assignment by continuous tracking (FACT) and probabilistic tractography (PT) for the detection of the corticospinal tract (CST) in patients with gliomas., Methods: Tractography of the CST was performed in 17 patients with eight low grade and nine anaplastic astrocytomas located in the motor cortex or the corticospinal tract. Diffusions metrics as fractional anisotropy (FA), mean (MD), axial (AD) and radial diffusivity (RD) were obtained. The methods were additionally applied on a physical phantom to assess their accuracy., Results: PT was successful in all (100 %), GT in 16 (94 %) and FACT in 15 patients (88 %). The case where GT and FACT, both, missed the CST showed the highest AD and RD, whereas the one where FACT algorithm, alone, was not successfully showed the lowest AD and RD of the group. FA was reduced on the pathologic side (FApath 0.35 ± 0.16 (mean ± SD) versus FAcontralateral 0.51 ± 0.15, pcorr < 0.03). RD was increased on the pathologic side (RDpath 0.67 ± 0.29 × 10(-3) mm(2)/s versus RDcontralateral 0.46 ± 0.08 × 10(-3) mm(2)/s, pcorr < 0.03). In the phantom measurement, only GT did not detect false positive fibres at fibre crossings., Conclusion: PT performed well even in areas of increased diffusivities indicating a severe oedema or disintegration of tissue. FACT was also susceptible to a decrease of diffusivities and to a susceptibility artefact, where GT was robust.

Diffusion-weighted magnetic resonance imaging provides information about the nerve fiber bundle geometry of the human brain. While the inference of the underlying fiber bundle orientation only requires single q-shell measurements, the absolute determination of their volume fractions is much more challenging with respect to measurement techniques and analysis. Unfortunately, the usually employed multi-compartment models cannot be applied to single q-shell measurements, because the compartment's diffusivities cannot be resolved. This work proposes an equation for fiber orientation densities that can infer the absolute fraction up to a global factor. This equation, which is inspired by the classical mass preservation law in fluid dynamics, expresses the fiber conservation associated with the assumption that fibers do not terminate in white matter. Simulations on synthetic phantoms show that the approach is able to derive the densities correctly for various configurations. Experiments with a pseudo ground truth phantom show that even for complex, brain-like geometries the method is able to infer the densities correctly. In-vivo results with 81 healthy volunteers are plausible and consistent. A group analysis with respect to age and gender show significant differences, such that the proposed maps can be used as a quantitative measure for group and longitudinal analysis., (Copyright © 2013 Elsevier Inc. All rights reserved.)

MR-encephalography allows the observation of functional signal in the brain at a frequency of 10 Hz, permitting filtering of physiological "noise" and the detection of single event activations. High temporal resolution is achieved by the use of undersampled non-Cartesian trajectories, parallel imaging and regularized image reconstruction. MR-encephalography is based on 3D-encoding, allowing undersampling in two dimensions and providing advantages in terms of signal to noise ratio. Long readout times, which are necessary for single shot whole brain imaging (up to 75 ms), cause off-resonance artifacts. To meet this issue, a spherical stack of spirals trajectory is proposed in this work. By examining the trajectories in local k-space, it is shown that in areas of strong susceptibility gradients spatial information is fundamentally lost, making a meaningful image reconstruction impossible in the affected areas. It is shown that the loss of spatial information is reduced when using a stack of spirals trajectory compared to concentric shells., (Copyright © 2013 Elsevier Inc. All rights reserved.)

A major challenge in neuroscience is to accurately decipher in vivo the entire brain circuitry (connectome) at a microscopic level. Currently, the only methodology providing a global noninvasive window into structural brain connectivity is diffusion tractography. The extent to which the reconstructed pathways reflect realistic neuronal networks depends, however, on data acquisition and postprocessing factors. Through a unique combination of approaches, we designed and evaluated herein a framework for reliable fiber tracking and mapping of the living mouse brain connectome. One important wiring scheme, connecting gray matter regions and passing fiber-crossing areas, was closely examined: the lemniscal thalamocortical (TC) pathway. We quantitatively validated the TC projections inferred from in vivo tractography with correlative histological axonal tracing in the same wild-type and reeler mutant mice. We demonstrated noninvasively that changes in patterning of the cortical sheet, such as highly disorganized cortical lamination in reeler, led to spectacular compensatory remodeling of the TC pathway.

Imaging of cerebral perfusion by tracking the first passage of an exogenous paramagnetic contrast agent (termed dynamic susceptibility contrast, MRI) has been used in the clinical practice for about a decade. However, the primary goal of dynamic susceptibility contrast MRI to directly quantify the local cerebral blood flow remains elusive. The major challenge of dynamic susceptibility contrast MRI is to measure the contrast inflow to the brain, i.e., the arterial input function. The measurement is complicated by the limited dynamic range of MRI pulse sequences that are optimized for a good contrast in brain tissue but are suboptimal for a much higher tracer concentration in arterial blood. In this work, we suggest a novel method for direct arterial input function quantification. The arterial input function is measured in the carotid arteries with a dedicated plug-in to the conventional pulse sequence to enable resolution of T(2) on the order of a millisecond. The new technique is compatible with the clinical measurement protocols. Applied to the pig model (N = 13), the method demonstrates robustness of the arterial input function measurement. The cardiac output and cerebral blood volume, obtained without adjustable parameters, agree well with positron emission tomography measurements and values found in the literature., (Copyright © 2012 Wiley Periodicals, Inc.)

The reliable estimation of fiber orientation distributions from diffusion-sensitized magnetic resonance imaging is an important processing step for revealing structural connectivity in the human brain. Fiber orientation distributions are usually represented in spherical harmonic (SH) domain, but many modern spatial regularization techniques were not yet formulated in SH-domain such that they cannot benefit from the well known advantages. In this article we propose a novel SH-formulation of the contour enhancement propagator (fiber continuity) which enables to implement a variety of processing steps in SH-domain.

We present a new framework for image segmentation with statistical shape model enhanced level sets represented as a linear combination of non-Euclidean radial basis functions (RBFs). The shape prior for the level set is represented as a probabilistic map created from the training data and registered with the target image. The new framework has the following advantages: 1) the explicit RBF representation of the level set allows the level set evolution to be represented as ordinary differential equations and reinitialization is no longer required. 2) The non-Euclidean distance RBFs makes it possible to incorporate image information into the basis functions, which results in more accurate and topologically more flexible solutions. Experimental results are presented to demonstrate the advantages of the method, as well as critical analysis of level sets versus the combination of both methods.

With the advent of novel biomedical 3D image acquisition techniques, the efficient and reliable analysis of volumetric images has become more and more important. The amount of data is enormous and demands an automated processing. The applications are manifold, ranging from image enhancement, image reconstruction, and image description to object/feature detection and high-level contextual feature extraction. In most scenarios, it is expected that geometric transformations alter the output in a mathematically well-defined manner. In this paper we emphasis on 3D translations and rotations. Many algorithms rely on intensity or low-order tensorial-like descriptions to fulfill this demand. This paper proposes a general mathematical framework based on mathematical concepts and theories transferred from mathematical physics and harmonic analysis into the domain of image analysis and pattern recognition. Based on two basic operations, spherical tensor differentiation and spherical tensor multiplication, we show how to design a variety of 3D image processing methods in an efficient way. The framework has already been applied to several biomedical applications ranging from feature and object detection tasks to image enhancement and image restoration techniques. In this paper, the proposed methods are applied on a variety of different 3D data modalities stemming from medical and biological sciences.

MR-encephalography is a technique that allows real-time observation of functional changes in the brain with a time-resolution of 100 ms. The high sampling rate is enabled by the use of undersampled image acquisition with regularized reconstruction. The article describes a novel imaging method for fast three-dimensional-MR-encephalography whole brain coverage based on undersampled, single-shot concentric shells trajectories and the use of multiple small receiver coils. The technique allows the observation of changes in blood oxygenation level dependent signal as a measure of brain physiology at very high temporal resolution., (Copyright © 2011 Wiley Periodicals, Inc.)

We present a method for densely computing local rotation invariant image descriptors in volumetric images. The descriptors are based on a transformation to the harmonic domain, which we compute very efficiently via differential operators. We show that this fast voxelwise computation is restricted to a family of basis functions that have certain differential relationships. Building upon this finding, we propose local descriptors based on the Gaussian Laguerre and spherical Gabor basis functions and show how the coefficients can be computed efficiently by recursive differentiation. We exemplarily demonstrate the effectiveness of such dense descriptors in a detection and classification task on biological 3D images. In a direct comparison to existing volumetric features, among them 3D SIFT, our descriptors reveal superior performance.

Fiber orientation distributions (FODs) based on diffusion-sensitized magnetic resonance imaging are usually symmetric, primarily due to the nature of the diffusion. In contrast, the underlying fiber configurations are not, as bending or fanning configurations are inherently asymmetric. We propose to dismiss the symmetry of the FOD to additionally encode the asymmetry of the underlying fiber configuration. This is of particular importance for low resolution images that are common in diffusion weighted imaging. We set up the mathematical foundations and geometric interpretations of asymmetric FODs and show how one can benefit from these considerations. We infer a continuity condition that is used as a prior during FOD estimation by constrained spherical deconvolution. This new prior shows superior performance in comparison to other spatial regularization strategies in reliability and accuracy.

Accurate identification of protein secondary structures is beneficial to understand three-dimensional structures of biological macromolecules. In this paper, a novel refined classification framework is proposed, which treats alpha-helix identification as a machine learning problem by representing each voxel in the density map with its Spherical Harmonic Descriptors (SHD). An energy function is defined to provide statistical analysis of its identification performance, which can be applied to all the α-helix identification approaches. Comparing with other existing α-helix identification methods for intermediate resolution electron density maps, the experimental results demonstrate that our approach gives the best identification accuracy and is more robust to the noise.

Background: Diffusion-MRI provides a unique window on brain anatomy and insights into aspects of tissue structure in living humans that could not be studied previously. There is a major effort in this rapidly evolving field of research to develop the algorithmic tools necessary to cope with the complexity of the datasets., Objectives: This work illustrates our strategy that encompasses the development of a modularized and open software tool for data processing, visualization and interactive exploration in diffusion imaging research and aims at reinforcing sustainable evaluation and progress in the field., Methods: In this paper, the usability and capabilities of a new application and toolkit component of the Medical Imaging and Interaction Toolkit (MITK, www.mitk.org), MITK-DI, are demonstrated using in-vivo datasets., Results: MITK-DI provides a comprehensive software framework for high-performance data processing, analysis and interactive data exploration, which is designed in a modular, extensible fashion (using CTK) and in adherence to widely accepted coding standards (e.g. ITK, VTK). MITK-DI is available both as an open source software development toolkit and as a ready-to-use installable application., Conclusions: The open source release of the modular MITK-DI tools will increase verifiability and comparability within the research community and will also be an important step towards bringing many of the current techniques towards clinical application.

Diffusion-sensitized magnetic resonance imaging provides information about the fibrous structure of the human brain. However, this information is not sufficient to reconstruct the underlying fiber network, because the nature of diffusion provides only conditional fiber densities. That is, it is possible to infer the percentage of bundles that pass a voxel with a certain direction, but the absolute number of fibers is inaccessible. In this work we propose a conservation equation for tensor fields that can infer this number up to a factor. Simulations on synthetic phantoms show that the approach is able to derive the densities correctly for various configurations. In-vivo results on 20 healthy volunteers are plausible and consistent, while a rigorous evaluation is difficult, because conclusive data from both MRI and histology remain elusive even on the most studied brain structures.

The accurate and reliable estimation of fiber orientation distributions, based on diffusion-sensitized magnetic resonance images is a major prerequisite for tractography algorithms or any other derived statistical analysis. In this work, we formulate the principle of fiber continuity (FC), which is based on the simple observation that the imaging of fibrous tissue implies certain expectations for the measured images. From this principle we derive a prior for the estimation of fiber orientation distributions based on high angular resolution diffusion imaging (HARDI). We demonstrate on simulated, phantom, and in vivo data the superiority of the proposed approach. Further, we propose another application of the FC principle, named FC flow, a method to resolve complex crossing regions solely based on diffusion tensor imaging (DTI). The idea is to infer directional information in crossing regions from adjacent anisotropic areas.

MR-Encephalography (MREG) is a technique that allows real time observation of functional changes in the brain that appears within 100 msec. The high sampling rate is achieved at the cost of some spatial resolution. The article describes a novel imaging method for fast three-dimensional-MR-encephalography whole brain coverage based on rosette trajectories and the use of multiple small receiver coils. The technique allows the observation of changes in brain physiology at very high temporal resolution. A highly undersampled three-dimensional rosette trajectory is chosen, to perform single shot acquisition of k-space data within 23 msec. By using a 32-channel head coil array and regularized nonuniform Fourier transformation reconstruction, the spatial resolution is sufficient to detect even subtle centers of activation (e.g. human MT+). The method was applied to visual block design paradigms and compared with echo planar imaging-based functional MRI. As a proof-of-principle of the method's ability to detect local differences in the hemodynamic response functions, the analyzed MR-encephalography data revealed a spatially dependent delay of the arrival of the blood oxygenation level dependent response within the visual cortex., (Copyright © 2010 Wiley-Liss, Inc.)

As it provides the only method for mapping white matter fibers in vivo, diffusion MRI tractography is gaining importance in clinical and neuroscience research. However, despite the increasing availability of different diffusion models and tractography algorithms, it remains unclear how to select the optimal fiber reconstruction method, given certain imaging parameters. Consequently, it is of utmost importance to have a quantitative comparison of these models and algorithms and a deeper understanding of the corresponding strengths and weaknesses. In this work, we use a common dataset with known ground truth and a reproducible methodology to quantitatively evaluate the performance of various diffusion models and tractography algorithms. To examine a wide range of methods, the dataset, but not the ground truth, was released to the public for evaluation in a contest, the "Fiber Cup". 10 fiber reconstruction methods were evaluated. The results provide evidence that: 1. For high SNR datasets, diffusion models such as (fiber) orientation distribution functions correctly model the underlying fiber distribution and can be used in conjunction with streamline tractography, and 2. For medium or low SNR datasets, a prior on the spatial smoothness of either the diffusion model or the fibers is recommended for correct modelling of the fiber distribution and proper tractography results. The phantom dataset, the ground truth fibers, the evaluation methodology and the results obtained so far will remain publicly available on: http://www.lnao.fr/spip.php?rubrique79 to serve as a comparison basis for existing or new tractography methods. New results can be submitted to fibercup09@gmail.com and updates will be published on the webpage., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Global fiber reconstruction aims at providing a consistent view of the fiber architecture in the whole volume of cerebral white matter on the basis of diffusion-sensitized magnetic resonance imaging. A new realization of this principle is presented. The method utilizes data acquired with high angular resolution diffusion imaging (HARDI), a measurement method that fulfills clinical requirements. For the first time among global reconstruction methods, the computation time is acceptable for a broad class of practical applications. The method does not involve any boundary conditions that prescribe the location of the ends of reconstructed fibers. This helps to minimize necessary user interaction and operator dependence. Results obtained in a physical phantom demonstrate a high reconstruction quality. In vivo results have been obtained in several volunteers. The algorithm found a number of prominent fascicles including those in the limbic system, which had been problematic for a previously published version of global tracking., (Copyright © 2010 Elsevier Inc. All rights reserved.)

In this paper, we present a novel approach for a trainable rotation invariant detection of complex structures in 3D microscopic multichannel data using a nonlinear filter approach. The basic idea of our approach is to compute local features in a window around each 3D position and map these features by means of a nonlinear mapping onto new local harmonic descriptors of the local window. These local harmonic descriptors are then combined in a linear way to form the output of the filter. The optimal combination of the computed local harmonic descriptors is determined in previous training step, and allows the filter to be adapted to an arbitrary structure depending on the problem at hand. Our approach is not limited to scalar-valued images and can also be used for vector-valued (multichannel) images such as gradient vector flow fields. We present realizations of a scalar-valued and a vector-valued multichannel filter. Our proposed algorithm was quantitatively evaluated on colorectal cancer cell lines (cells grown under controlled conditions), on which we successfully detected complex 3D mitotic structures. For a qualitative evaluation we tested our algorithms on human 3D tissue samples of colorectal cancer. We compare our results with a steerable filter approach as well as a morphology-based approach.

Steerable filters are a valuable tool for various low-level vision tasks. In this paper, we argue for the use of complex analysis in the context of 2-D steerable filters. In particular, we recommend the use of complex partial derivatives as a computational basis. Complex derivatives have a major advantage in comparison to real derivatives: they show a canonical rotation behavior, namely a rotation affects the derivative just by a multiplication with a complex unit number. So, the complex derivatives can be steered in a more elegant way and above that they are less expensive to compute. We present several analytical formulas for common and new filter kernels in terms of complex derivatives. Further we relate the complex derivatives of a Gaussian with the Gauss-Laguerre transform and show that the Gauss-Laguerre functions provide an optimal signal representation for local and smooth images. We discuss various finite difference schemes for the realization of the derivatives and use them in practice. In a first experiment, we use a newly introduced filter kernel for anisotropic blurring. The complex formalism offers an elegant way to locally adapt the shape and orientation of the kernel. Second, we use the proposed filters as matched filters to detect vessels in retinal images.