Florian Weiler

Mean upper cervical cord area (MUCCA) measurement in long-standing multiple sclerosis: relation to brain findings and clinical disability.

Background: The majority of patients with multiple sclerosis (MS) present with spinal cord pathology. Spinal cord atrophy is thought to be a marker of disease severity, but in long-disease duration its relation to brain pathology and clinical disability is largely unknown. Objective: Our aim was to investigate mean upper cervical cord area (MUCCA) in patients with long-standing MS and assess its relation to brain magnetic resonance imaging (MRI) measures and clinical disability. Methods: MUCCA was measured in 196 MS patients and 55 healthy controls using 3DT1-weighted cervical images obtained at 3T MRI. Clinical disability was measured using the Expanded Disability Status Scale (EDSS), Nine-Hole-Peg test (9-HPT), and 25 feet Timed Walk Test (TWT). Stepwise linear regression was performed to assess the association between MUCCA and MRI measures, and between MUCCA and clinical disability. Results: MUCCA was smaller (mean 11.7%) in MS patients compared with healthy controls (72.56±9.82 and 82.24±7.80 mm2 respectively; p<0.001), most prominently in male patients. MUCCA was associated with normalized brain volume, and number of cervical cord lesions. MUCCA was independently associated with EDSS, TWT, and 9-HPT. Conclusion: MUCCA was reduced in MS patients compared with healthy controls. It provides a relevant marker for clinical disability in long-standing disease, independent of other MRI measures.

Differential patterns of spinal cord and brain atrophy in NMO and MS

Objective: To investigate spinal cord and brain atrophy in neuromyelitis optica (NMO), and its relationship with other MRI measurements and clinical disability, compared with patients with multiple sclerosis (MS) and healthy controls (HC). Methods: We recruited 35 patients with NMO, 35 patients with MS, and 35 HC, who underwent both spinal cord and brain MRI. Mean upper cervical cord area (MUCCA), brain parenchymal fraction (BPF), gray matter fraction (GMF), white matter fraction (WMF), and spinal cord and brain lesion loads were measured and compared among groups. Multivariate associations between MUCCA and brain volume measurement and clinical variables were assessed by partial correlations and multiple linear regression. Results: Patients with NMO showed smaller MUCCA than HC (p = 0.004), and patients with MS had a trend of smaller MUCCA compared to HC (p = 0.07), with no significant difference between the patient groups. Patients with NMO showed lower BPF than HC, and patients with MS had lower BPF and GMF than patients with NMO. In NMO, MUCCA was correlated with Expanded Disability Status Scale score (EDSS), number of relapses, and total spinal cord lesion length, while in MS, MUCCA was correlated with WMF and EDSS. MUCCA was the only independent variable for predicting clinical disability measured by EDSS in NMO (R2 = 0.55, p < 0.001) and MS (R2 = 0.17, p = 0.013). Conclusion: NMO showed predominately spinal cord atrophy with mild brain atrophy, while MS demonstrated more brain atrophy, especially in the gray matter. MUCCA is the main MRI-derived parameter for explaining clinical disability in NMO and MS, and may serve as a potential biomarker for further clinical trials, especially in NMO.

A GPU-based fiber tracking framework using geometry shaders

The clinical application of fiber tracking becomes more widespread. Thus it is of high importance to be able to produce high quality results in a very short time. Additionally, research in this field would benefit from fast implementation and evaluation of new algorithms. In this paper we present a GPU-based fiber tracking framework using latest features of commodity graphics hardware such as geometry shaders. The implemented streamline algorithm performs fiber reconstruction of a whole brain using 30,000 seed points in less than 120 ms on a high-end GeForce GTX 280 graphics board. Seed points are sent to the GPU which emits up to a user-defined number of fiber points per seed vertex. These are recorded to a vertex buffer that can be rendered or downloaded to main memory for further processing. If the output limit of the geometry shader is reached before the stopping criteria are fulfilled, the last vertices generated are then used in a subsequent pass where the geometry shader continues the tracking. Since all the data resides on graphics memory the intermediate steps can be visualized in real-time. The fast reconstruction not only allows for an interactive change of tracking parameters but, since the tracking code is implemented using GPU shaders, even for a runtime change of the algorithm. Thus, rapid development and evaluation of different algorithms and parameter sets becomes possible, which is of high value for e.g. research on uncertainty in fiber tracking.

Multiple sclerosis risk loci correlate with cervical cord atrophy and may explain the course of disability

Genome-wide association studies (GWAS) underscore the genetic basis of multiple sclerosis (MS); however, only few of the newly reported genetic variations relevant in MS have been replicated or correlated for clinical/paraclinical phenotypes such as spinal cord atrophy in independent patient cohorts. We genotyped 141 MS patients for 58 variations reported to reach significance in GWAS. Expanded disability status scale (EDSS) and disease duration (DD) are available from regular clinical examinations. MRI included sagittal high-resolution 3D T1-weighted magnetization-prepared rapid acquisition gradient echo of the cervical cord region used for volumetry. Due dependency of mean upper cervical cord area (MUCCA) with EDSS and/or DD, correction operations were performed compensating for EDSS/DD. We assessed each MS risk locus for possible MUCCA association. We identified twelve risk loci that significantly correlated with MUCCA. For nine loci—BATF, CYP27B1, IL12B, NFKB1, IL7, PLEK, EVI5, TAGAP and nrs669607—patients revealed significantly higher degree of atrophy; TYK2, RGS1 and CLEC16A revealed inverse effects. The weighted genetic risk score over the twelve loci showed significant correlation with MUCCA. Our data reveal a risk gene depending paraclinical/clinical phenotype. Since MUCCA clearly correlates with disability, the candidates identified here may serve as prognostic markers for disability progression.

State-of-the-Art Computer Graphics in Neurosurgical Planning and Risk Assessment

We present a novel software assistant that unlocks new potentials in neurosurgical planning and risk assessment. It allows surgeons to approach the task in an intuitive manner, by providing them with the possibility to simultaneously observe all relevant data of a case in synchronized 2D and 3D views. State-of-the-art technologies from the field of computer graphics are combined to allow simultaneous interactive rendering of anatomical and functional MR data in combination with manually segmented objects and slice-based overlays. This allows surgeons to perceive a clearer impression of the anatomical and functional structures affected by an intervention, and especially the way they are related to each other. Thus, it significantly facilitates the finding of an optimal intervention strategy.

Different patterns of longitudinal brain and spinal cord changes and their associations with disability progression in NMO and MS

ObjectiveTo investigate the longitudinal spinal cord and brain changes in neuromyelitis optica (NMO) and multiple sclerosis (MS) and their associations with disability progression.Patients and methodsWe recruited 28 NMO, 22 MS, and 20 healthy controls (HC), who underwent both spinal cord and brain MRI at baseline. Twenty-five NMO and 20 MS completed 1-year follow-up. Baseline spinal cord and brain lesion loads, mean upper cervical cord area (MUCCA), brain, and thalamus volume and their changes during a 1-year follow-up were measured and compared between groups. All the measurements were also compared between progressive and non-progressive groups in NMO and MS.ResultsMUCCA decreased significantly during the 1-year follow-up in NMO not in MS. Percentage brain volume changes (PBVC) and thalamus volume changes in MS were significantly higher than NMO. MUCCA changes were significantly different between progressive and non-progressive groups in NMO, while baseline brain lesion volume and PBVC were associated with disability progression in MS. MUCCA changes during 1-year follow-up showed association with clinical disability in NMO.ConclusionSpinal cord atrophy changes were associated with disability progression in NMO, while baseline brain lesion load and whole brain atrophy changes were related to disability progression in MS.Key Points• Spinal cord atrophy progression was observed in NMO.• Spinal cord atrophy changes were associated with disability progression in NMO.• Brain lesion and atrophy were related to disability progression in MS.

Central Atrophy Early in Multiple Sclerosis: Third Ventricle Volumetry versus Planimetry.

Cerebral atrophy has been suggested to be a reliable magnetic resonance imaging (MRI) predictor of subsequent disability in all stages of multiple sclerosis (MS). However, no accepted methodology for routine clinical use exists to date. We sought an easy to apply and fast technique to evaluate cerebral ventricular volume in patients with MS with similar accuracy as a semiautomatic volumetric method.

Highly accurate volumetry of the spinal cord

Quantitative analysis of the spinal cord from MR images is of significant clinical interest when studying certain neurologic diseases. Especially for multiple sclerosis, a number of studies have analyzed the relation between spinal cord atrophy and clinically monitored progression of the disease. A commonly analyzed parameter in this field is the mean cross-sectional area of the cord, which can also be expressed as the average volume per cm. In this paper, we present a novel approach for precise measurement of the volume, length, and cross-sectional area of the spinal cord from T1-weighted MR images. It is computationally fast, with a low effort of required user interaction. It is based on a semi-automated pre-segmentation of a sub-section of the spinal cord, followed by an automated Gaussian mixture-model fit for volume calculation. Additionally, the centerline of the cord is extracted, which allows for calculation of the mean cross-sectional area of the measured section. We evaluate the accuracy of our method with respect to scan/re-scan reproducibility as well as intra- and inter-rater agreement. We achieved a mean coefficient of variation of 0.62% over repeated MR acquisitions, mean CoV of 0.39% for intra-rater comparison, and a mean CoV of 0.28% for inter-rater comparison by five different observers. These results prove a high sensitivity to detect even small changes in atrophy, as it could typically be observed over the temporal progression of MS

An interactive ROI tool for DTI fiber tracking

Fiber tracking is one of the clinically most well-established analysis techniques for Diffusion Tensor Imaging data (DTI). It facilitates the reconstruction of anatomically known white matter structures by tracing trajectories on a tensor field obtained from diffusion weighted MR images. A crucial step when using this technique is the placement and shape of regions-of-interest (ROIs) to identify the structures in question. Typically, free-hand contours or simple geometric shapes like rectangles are placed in regions, where a given structure can be identified using the color coded DTI representation. However, such approaches result in a high variability of the resulting tracts and usually require additional filtering and placement of multiple ROIs. Also, the generation of accurate ROIs using a free-hand tool requires a significant amount of interaction time. We present a method which allows for interactive generation of anatomically meaningful ROIs for DTI fiber tracking based on geometric similarities of the underlying tensor field. The method works similar to the magicwand tool known from image editing software tools to create reasonable, fully image based ROIs using a single mouseclick.

Exploring DeepMedic for the purpose of segmenting white matter hyperintensity lesions

DeepMedic, an open source software library based on a multi-channel multi-resolution 3D convolutional neural network, has recently been made publicly available for brain lesion segmentations. It has already been shown that segmentation tasks on MRI data of patients having traumatic brain injuries, brain tumors, and ischemic stroke lesions can be performed very well. In this paper we describe how it can efficiently be used for the purpose of detecting and segmenting white matter hyperintensity lesions. We examined if it can be applied to single-channel routine 2D FLAIR data. For evaluation, we annotated 197 datasets with different numbers and sizes of white matter hyperintensity lesions. Our experiments have shown that substantial results with respect to the segmentation quality can be achieved. Compared to the original parametrization of the DeepMedic neural network, the timings for training can be drastically reduced if adjusting corresponding training parameters, while at the same time the Dice coefficients remain nearly unchanged. This enables for performing a whole training process within a single day utilizing a NVIDIA GeForce GTX 580 graphics board which makes this library also very interesting for research purposes on low-end GPU hardware.