Miriam H. A. Bauer

Correlation of the extent of tumor volume resection and patient survival in surgery of glioblastoma multiforme with high-field intraoperative MRI guidance

Extent of resection (EOR) still remains controversial in therapy of glioblastoma multiforme (GBM). However, an increasing number of studies favor maximum EOR as being associated with longer patient survival. One hundred thirty-five GBM patients underwent tumor resection aided by 1.5T intraoperative MRI (iMRI) and integrated multimodal navigation. Tumor volume was quantified by manual segmentation. The influences of EOR, patient age, recurrent tumor, tumor localization, and gender on survival time were examined. Intraoperative MRI detected residual tumor volume in 88 patients. In 19 patients surgery was continued; further resection resulted in final gross total resection (GTR) for 9 patients (GTR increased from 47 [34.80%] to 56 [41.49%] patients). Tumor volumes were significantly reduced from 34.25 ± 23.68% (first iMRI) to 1.22 ± 16.24% (final iMRI). According to Kaplan-Meier estimates, median survival was 14 months (95% confidence interval [CI]: 11.7-16.2) for EOR ≥ 98% and 9 months (95% CI: 7.4-10.5) for EOR <98% (P< .0001); it was 9 months (95% CI: 7.3-10.7) for patients ≥ 65 years and 12 months (95% CI: 8.4-15.6) for patients <65 years (P < .05). Multivariate analysis showed a hazard ratio of 0.39 (95% CI: 0.24-0.63; P = .001) for EOR ≥ 98% and 0.61 (95% CI: 0.38-0.97; P < .05) for patient age <65 years. To our knowledge, this is the largest study including correlation of iMRI, tumor volumetry, and survival time. We demonstrate that navigation guidance and iMRI significantly contribute to optimal EOR with low postoperative morbidity, where EOR ≥ 98% and patient age <65 years are associated with significant survival advantages. Thus, maximum EOR should be the surgical goal in GBM surgery while preserving neurological function.

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.

Nugget-cut: a segmentation scheme for spherically- and elliptically-shaped 3D objects

In this paper, a segmentation method for spherically-and elliptically-shaped objects is presented. It utilizes a user-defined seed point to set up a directed 3D graph. The nodes of the 3D graph are obtained by sampling along rays that are sent through the surface points of a polyhedron. Additionally, several arcs and a parameter constrain the set of possible segmentations and enforce smoothness. After the graph has been constructed, the minimal cost closed set on the graph is computed via a polynomial time s-t cut, creating an optimal segmentation of the object. The presented method has been evaluated on 50 Magnetic Resonance Imaging (MRI) data sets with World Health Organization (WHO) grade IV gliomas (glioblastoma multiforme). The ground truth of the tumor boundaries were manually extracted by three clinical experts (neurological surgeons) with several years (> 6) of experience in resection of gliomas and afterwards compared with the automatic segmentation results of the proposed scheme yielding an average Dice Similarity Coefficient (DSC) of 80.37±8.93%. However, no segmentation method provides a perfect result, so additional editing on some slices was required, but these edits could be achieved quickly because the automatic segmentation provides a border that fits mostly to the desired contour. Furthermore, the manual segmentation by neurological surgeons took 2-32 minutes (mean: 8 minutes), in contrast to the automatic segmentation with our implementation that took less than 5 seconds.

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.

Boundary estimation of fiber bundles derived from diffusion tensor images

PurposeDiffusion tensor imaging (DTI) is a non-invasive imaging technique that allows estimating the location of white matter tracts based on the measurement of water diffusion properties. Using DTI data, the fiber bundle boundary can be determined to gain information about eloquent structures, which is of major interest for neurosurgical interventions. In this paper, a novel approach for boundary estimation is presented.MethodsDTI in combination with diverse segmentation algorithms allows estimating the position and course of fiber tracts in the human brain. For additional information about the expansion of the fiber bundle, the introduced iterative approach uses the centerline of a tracked fiber bundle between two regions of interest (ROI). After sampling along this centerline, rays are sent out radially, discrete 2D contours are calculated, and the fiber bundle boundary is estimated in a stepwise manner. For this purpose, each ray is analyzed using several criteria, including anisotropy parameters and angle parameters, to find the boundary point.ResultsThe novel method for automatically calculating the boundaries has been applied to several artificially generated DTI datasets. Multiple parameters were varied: number of rays per plane, sampling rate and sampled points along the rays. For the DTI data used in the experiments, the method yielded a dice similarity coefficient (DSC) between 74.7 and 91.5%.ConclusionsIn this paper, a novel approach to retrieve significant information about the fiber bundle boundary from DTI data is presented. The method is a contribution to gather important knowledge about high-risk structures in neurosurgical interventions.

A Fast and Robust Graph-Based Approach for Boundary Estimation of Fiber Bundles Relying on Fractional Anisotropy Maps

In this paper, a fast and robust graph-based approach for boundary estimation of fiber bundles derived from Diffusion Tensor Imaging (DTI) is presented. DTI is a non-invasive imaging technique that allows the estimation of the location of white matter tracts based on measurements of water diffusion properties. Depending on DTI data, the fiber bundle boundary can be determined to gain information about eloquent structures, which is of major interest for neurosurgery. DTI in combination with tracking algorithms allows the estimation of position and course of fiber tracts in the human brain. The presented method uses these tracking results as the starting point for a graph-based approach. The overall method starts by computing the fiber bundle centerline between two user-defined regions of interests (ROIs). This centerline determines the planes that are used for creating a directed graph. Then, the mincut of the graph is calculated, creating an optimal boundary of the fiber bundle.

Merits and Limits of Tractography Techniques for the Uninitiated

The implementation of fiber tracking or tractography modules in commercial navigation systems resulted in a broad availability of visualization possibilities for major white matter tracts in the neurosurgical community. Unfortunately the implemented algorithms and tracking approaches do not represent the state of the art of tractography strategies and may lead to false tracking results. The application of advanced tractography techniques for neurosurgical procedures poses even additional challenges that relate to effects of the individual anatomy that might be altered by edema and tumor, to stereotactic inaccuracies due to image distortion, as well as to registration inaccuracies and brain shift.

Segmentation of fiber tracts based on an accuracy analysis on diffusion tensor software phantoms

Due to its unique sensitivity to tissue microstructure, one of the primary applications of diffusion-weighted magnetic resonance imaging is the reconstruction of neural fiber pathways by means of fiber-tracking algorithms. In this work, we make use of realistic diffusion-tensor software phantoms in order to carry out an analysis of the precision of streamline tractography by systematically varying certain properties of the simulated image data (noise, tensor anisotropy, and image resolution) as well as certain fiber-tracking parameters (number of seed points and step length). Building upon the gained knowledge about the precision of the analyzed fiber-tracking algorithm, we proceed by suggesting a fuzzy segmentation algorithm for diffusion tensor images which better estimates the precise spatial extent of a tracked fiber bundle. The presented segmentation algorithm utilizes information given by the estimated main diffusion direction in a voxel and the respective uncertainty, and its validity is confirmed by both qualitative and quantitative analyses.

DTI segmentation via the combined analysis of connectivity maps and tensor distances

We describe a novel approach to extract the neural tracts of interest from a diffusion tensor image (DTI). Compared to standard streamline tractography, existing probabilistic methods are able to capture fiber paths that deviate from the main tensor diffusion directions. At the same time, tensor clustering methods are able to more precisely delimit the border of the bundle. To the best of our knowledge, we propose the first algorithm which combines the advantages supplied by probabilistic and tensor clustering approaches. The algorithm includes a post-processing step to limit partial-volume related segmentation errors. We extensively test the accuracy of our algorithm on different configurations of a DTI software phantom for which we systematically vary the image noise, the number of gradients, the geometry of the fiber paths and the angle between adjacent and crossing fiber bundles. The reproducibility of the algorithm is supported by the segmentation of the corticospinal tract of nine patients. Additional segmentations of the corticospinal tract, the arcuate fasciculus, and the optic radiations are in accordance with anatomical knowledge. The required user interaction is comparable to that of streamline tractography, which allows for an uncomplicated integration of the algorithm into the clinical routine.