Dorit Merhof

Intraoperative visualization of the pyramidal tract by diffusion-tensor-imaging-based fiber tracking

Functional neuronavigation allows intraoperative visualization of cortical eloquent brain areas. Major white matter tracts, such as the pyramidal tract, can be delineated by diffusion-tensor-imaging based fiber tracking. These tractography data were integrated into 3-D datasets applied for neuronavigation by rigid registration of the diffusion images with standard anatomical image data so that their course could be superimposed onto the surgical field during resection of gliomas. Intraoperative high-field magnetic resonance imaging was used to compensate for the effects of brain shift, which amounted up to 8 mm. Despite image distortion of echo planar images, which was identified by non-linear registration techniques, navigation was reliable. In none of the 19 patients new postoperative neurological deficits were encountered. Intraoperative visualization of major white matter tracts allows save resection of gliomas near eloquent brain areas. A possible shifting of the pyramidal tract has to be taken into account after major tumor parts are resected.

Evaluation of a human neurite growth assay as specific screen for developmental neurotoxicants

Organ-specific in vitro toxicity assays are often highly sensitive, but they lack specificity. We evaluated here examples of assay features that can affect test specificity, and some general procedures are suggested on how positive hits in complex biological assays may be defined. Differentiating human LUHMES cells were used as potential model for developmental neurotoxicity testing. Forty candidate toxicants were screened, and several hits were obtained and confirmed. Although the cells had a definitive neuronal phenotype, the use of a general cell death endpoint in these cultures did not allow specific identification of neurotoxicants. As alternative approach, neurite growth was measured as an organ-specific functional endpoint. We found that neurite extension of developing LUHMES was specifically inhibited by diverse compounds such as colchicine, vincristine, narciclasine, rotenone, cycloheximide, or diquat. These compounds reduced neurite growth at concentrations that did not compromise cell viability, and neurite growth was affected more potently than the integrity of developed neurites of mature neurons. A ratio of the EC50 values of neurite growth inhibition and cell death of >4 provided a robust classifier for compounds associated with a developmental neurotoxic hazard. Screening of unspecific toxicants in the test system always yielded ratios <4. The assay identified also compounds that accelerated neurite growth, such as the rho kinase pathway modifiers blebbistatin or thiazovivin. The negative effects of colchicine or rotenone were completely inhibited by a rho kinase inhibitor. In summary, we suggest that assays using functional endpoints (neurite growth) can specifically identify and characterize (developmental) neurotoxicants.

Intraoperative Visualization of Fiber Tracking Based Reconstruction of Language Pathways in Glioma Surgery

BACKGROUND: For neuroepithelial tumors, the surgical goal is maximum resection with preservation of neurological function. This is contributed to by intraoperative magnetic resonance imaging (iMRI) combined with multimodal navigation. OBJECTIVE: We evaluated the contribution of diffusion tensor imaging (DTI)-based fiber tracking of language pathways with 2 different algorithms (tensor deflection, connectivity analysis [CA]) integrated in the navigation on the surgical outcome. METHODS: We evaluated 32 patients with neuroepithelial tumors who underwent surgery with DTI-based fiber tracking of language pathways integrated in neuronavigation. The tensor deflection algorithm was routinely used and its results intraoperatively displayed in all cases. The CA algorithm was furthermore evaluated in 23 cases. Volumetric assessment was performed in pre- and intraoperative MR images. To evaluate the benefit of fiber tractography, language deficits were evaluated pre- and postoperatively and compared with the volumetric analysis. RESULTS: Final gross-total resection was performed in 40.6% of patients. Absolute tumor volume was reduced from 55.33 ± 63.77 cm3 to 20.61 ± 21.67 cm3 in first iMRI resection control, to finally 11.56 ± 21.92 cm3 (P < .01). Fiber tracking of the 2 algorithms showed a deviation of the displayed 3D objects by <5 mm. In long-term follow-up only 1 patient (3.1%) had a persistent language deficit. CONCLUSION: Intraoperative visualization of language-related cortical areas and the connecting pathways with DTI-based fiber tracking can be successfully performed and integrated in the navigation system. In a setting of intraoperative high-field MRI this contributes to maximum tumor resection with low postoperative morbidity.

Fiber tractography based on diffusion tensor imaging compared with high-angular-resolution diffusion imaging with compressed sensing : initial experience

BACKGROUND The most frequently used method for fiber tractography based on diffusion tensor imaging (DTI) is associated with restrictions in the resolution of crossing or kissing fibers and in the vicinity of tumor or edema. Tractography based on high-angular-resolution diffusion imaging (HARDI) is capable of overcoming this restriction. With compressed sensing (CS) techniques, HARDI acquisitions with a smaller number of directional measurements can be used, thus enabling the use of HARDI-based fiber tractography in clinical practice. OBJECTIVE To investigate whether HARDI+CS-based fiber tractography improves the display of neuroanatomically complex pathways and in areas of disturbed diffusion properties. METHODS Six patients with gliomas in the vicinity of language-related areas underwent 3-T magnetic resonance imaging including a diffusion-weighted data set with 30 gradient directions. Additionally, functional magnetic resonance imaging for cortical language sites was obtained. Fiber tractography was performed with deterministic streamline algorithms based on DTI using 3 different software platforms. Additionally, tractography based on reconstructed diffusion signals using HARDI+CS was performed. RESULTS HARDI+CS-based tractography displayed more compact fiber bundles compared with the DTI-based results in all cases. In 3 cases, neuroanatomically plausible fiber bundles were displayed in the vicinity of tumor and peritumoral edema, which could not be traced on the basis of DTI. The curvature around the sylvian fissure was displayed properly in 6 cases and in only 2 cases with DTI-based tractography. CONCLUSION HARDI+CS seems to be a promising approach for fiber tractography in clinical practice for neuroanatomically complex fiber pathways and in areas of disturbed diffusion, overcoming the problem of long acquisition times.

Hybrid Visualization for White Matter Tracts using Triangle Strips and Point Sprites

Diffusion tensor imaging is of high value in neurosurgery, providing information about the location of white matter tracts in the human brain. For their reconstruction, streamline techniques commonly referred to as fiber tracking model the underlying fiber structures and have therefore gained interest. To meet the requirements of surgical planning and to overcome the visual limitations of line representations, a new real-time visualization approach of high visual quality is introduced. For this purpose, textured triangle strips and point sprites are combined in a hybrid strategy employing GPU programming. The triangle strips follow the fiber streamlines and are textured to obtain a tube-like appearance. A vertex program is used to orient the triangle strips towards the camera. In order to avoid triangle flipping in case of fiber segments where the viewing and segment direction are parallel, a correct visual representation is achieved in these areas by chains of point sprites. As a result, high quality visualization similar to tubes is provided allowing for interactive multimodal inspection. Overall, the presented approach is faster than existing techniques of similar visualization quality and at the same time allows for real-time rendering of dense bundles encompassing a high number of fibers, which is of high importance for diagnosis and surgical planning

Visualization of white matter tracts with wrapped streamlines

Diffusion tensor imaging is a magnetic resonance imaging method which has gained increasing importance in neuroscience and especially in neurosurgery. It acquires diffusion properties represented by a symmetric 2nd order tensor for each voxel in the gathered dataset. From the medical point of view, the data is of special interest due lo different diffusion characteristics of varying brain tissue allowing conclusions about the underlying structures such as while matter tracts. An obvious way to visualize this data is to focus on the anisotropic areas using the major eigenvector for tractography and rendering lines for visualization of the simulation results. Our approach extends this technique to avoid line representation since lines lead 10 very complex illustrations and furthermore are mistakable. Instead, we generate surfaces wrapping bundles of lines. Thereby, a more intuitive representation of different tracts is achieved.

Correction of susceptibility artifacts in diffusion tensor data using non-linear registration

Diffusion tensor imaging can be used to localize major white matter tracts within the human brain. For surgery of tumors near eloquent brain areas such as the pyramidal tract this information is of importance to achieve an optimal resection while avoiding post-operative neurological deficits. However, due to the small bandwidth of echo planar imaging, diffusion tensor images suffer from susceptibility artifacts resulting in positional shifts and distortion. As a consequence, the fiber tracts computed from echo planar imaging data are spatially distorted. We present an approach based on non-linear registration using Bézier functions to efficiently correct distortions due to susceptibility artifacts. The approach makes extensive use of graphics hardware to accelerate the non-linear registration procedure. An improvement presented in this paper is a more robust and efficient optimization strategy based on simultaneous perturbation stochastic approximation (SPSA). Since the accuracy of non-linear registration is crucial for the value of the presented correction method, two techniques were applied in order to prove the quality of the proposed framework. First, the registration accuracy was evaluated by recovering a known transformation with non-linear registration. Second, landmark-based evaluation of the registration method for anatomical and diffusion tensor data was performed. The registration was then applied to patients with lesions adjacent to the pyramidal tract in order to compensate for susceptibility artifacts. The effect of the correction on the pyramidal tract was then quantified by measuring the position of the tract before and after registration. As a result, the distortions observed in phase encoding direction were most prominent at the cortex and the brainstem. The presented approach allows correcting fiber tract distortions which is an important prerequisite when tractography data are integrated into a stereotactic setup for intra-operative guidance.

Optic Radiation Fiber Tractography in Glioma Patients Based on High Angular Resolution Diffusion Imaging with Compressed Sensing Compared with Diffusion Tensor Imaging - Initial Experience

Objective Up to now, fiber tractography in the clinical routine is mostly based on diffusion tensor imaging (DTI). However, there are known drawbacks in the resolution of crossing or kissing fibers and in the vicinity of a tumor or edema. These restrictions can be overcome by tractography based on High Angular Resolution Diffusion Imaging (HARDI) which in turn requires larger numbers of gradients resulting in longer acquisition times. Using compressed sensing (CS) techniques, HARDI signals can be obtained by using less non-collinear diffusion gradients, thus enabling the use of HARDI-based fiber tractography in the clinical routine. Methods Eight patients with gliomas in the temporal lobe, in proximity to the optic radiation (OR), underwent 3T MRI including a diffusion-weighted dataset with 30 gradient directions. Fiber tractography of the OR using a deterministic streamline algorithm based on DTI was compared to tractography based on reconstructed diffusion signals using HARDI+CS. Results HARDI+CS based tractography displayed the OR more conclusively compared to the DTI-based results in all eight cases. In particular, the potential of HARDI+CS-based tractography was observed for cases of high grade gliomas with significant peritumoral edema, larger tumor size or closer proximity of tumor and reconstructed fiber tract. Conclusions Overcoming the problem of long acquisition times, HARDI+CS seems to be a promising basis for fiber tractography of the OR in regions of disturbed diffusion, areas of high interest in glioma surgery.

Novel Fast Marching for Automated Segmentation of the Hippocampus (FMASH): method and validation on clinical data.

With hippocampal atrophy both a clinical biomarker for early Alzheimers Disease (AD) and implicated in many other neurological and psychiatric diseases, there is much interest in the accurate, reproducible delineation of this region of interest (ROI) in structural MR images. Here we present Fast Marching for Automated Segmentation of the Hippocampus (FMASH): a novel approach using the Sethian Fast Marching (FM) technique to grow a hippocampal ROI from an automatically-defined seed point. Segmentation performance is assessed on two separate clinical datasets, utilising expert manual labels as gold standard to quantify Dice coefficients, false positive rates (FPR) and false negative rates (FNR). The first clinical dataset (denoted CMA) contains normal controls (NC) and atrophied AD patients, whilst the second is a collection of NC and bipolar (BP) patients (denoted BPSA). An optimal and robust stopping criterion is established for the propagating FM front and the final FMASH segmentation estimates compared to two commonly-used methods: FIRST/FSL and Freesurfer (FS). Results show that FMASH outperforms both FIRST and FS on the BPSA data, with significantly higher Dice coefficients (0.80±0.01) and lower FPR. Despite some intrinsic bias for FIRST and FS on the CMA data, due to their training, FMASH performs comparably well on the CMA data, with an average bilateral Dice coefficient of 0.82±0.01. Furthermore, FMASH most accurately captures the hippocampal volume difference between NC and AD, and provides a more accurate estimation of the problematic hippocampus-amygdala border on both clinical datasets. The consistency in performance across the two datasets suggests that FMASH is applicable to a range of clinical data with differing image quality and demographics.

Optimized data preprocessing for multivariate analysis applied to 99mTc-ECD SPECT data sets of Alzheimer's patients and asymptomatic controls

Multivariate image analysis has shown potential for classification between Alzheimers disease (AD) patients and healthy controls with a high-diagnostic performance. As image analysis of positron emission tomography (PET) and single photon emission computed tomography (SPECT) data critically depends on appropriate data preprocessing, the focus of this work is to investigate the impact of data preprocessing on the outcome of the analysis, and to identify an optimal data preprocessing method. In this work, technetium-99methylcysteinatedimer (99mTc-ECD) SPECT data sets of 28 AD patients and 28 asymptomatic controls were used for the analysis. For a series of different data preprocessing methods, which includes methods for spatial normalization, smoothing, and intensity normalization, multivariate image analysis based on principal component analysis (PCA) and Fisher discriminant analysis (FDA) was applied. Bootstrap resampling was used to investigate the robustness of the analysis and the classification accuracy, depending on the data preprocessing method. Depending on the combination of preprocessing methods, significant differences regarding the classification accuracy were observed. For 99mTc-ECD SPECT data, the optimal data preprocessing method in terms of robustness and classification accuracy is based on affine registration, smoothing with a Gaussian of 12 mm full width half maximum, and intensity normalization based on the 25% brightest voxels within the whole-brain region.