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See detailFrom tech to bench: Deep Learning pipeline for image segmentation of high-throughput high-content microscopy data
Garcia Santa Cruz, Beatriz UL; Jarazo, Javier UL; Saraiva, Claudia UL et al

Poster (2019, November 29)

Automation of biological image analysis is essential to boost biomedical research. The study of complex diseases such as neurodegenerative diseases calls for big amounts of data to build models towards ... [more ▼]

Automation of biological image analysis is essential to boost biomedical research. The study of complex diseases such as neurodegenerative diseases calls for big amounts of data to build models towards precision medicine. Such data acquisition is feasible in the context of high-throughput screening in which the quality of the results relays on the accuracy of image analysis. Although the state-of-the-art solutions for image segmentation employ deep learning approaches, the high cost of manual data curation is hampering the real use in current biomedical research laboratories. Here, we propose a pipeline that employs deep learning not only to conduct accurate segmentation but also to assist with the creation of high-quality datasets in a less time-consuming solution for the experts. Weakly-labelled datasets are becoming a common alternative as a starting point to develop real-world solutions. Traditional approaches based on classical multimedia signal processing were employed to generate a pipeline specifically optimized for the high-throughput screening images of iPSC fused with rosella biosensor. Such pipeline produced good segmentation results but with several inaccuracies. We employed the weakly-labelled masks produced in this pipeline to train a multiclass semantic segmentation CNN solution based on U-net architecture. Since a strong class imbalance was detected between the classes, we employed a class sensitive cost function: Dice coe!cient. Next, we evaluated the accuracy between the weakly-labelled data and the trained network segmentation using double-blind tests conducted by experts in cell biology with experience in this type of images; as well as traditional metrics to evaluate the quality of the segmentation using manually curated segmentations by cell biology experts. In all the evaluations the prediction of the neural network overcomes the weakly-labelled data quality segmentation. Another big handicap that complicates the use of deep learning solutions in wet lab environments is the lack of user-friendly tools for non-computational experts such as biologists. To complete our solution, we integrated the trained network on a GUI built on MATLAB environment with non-programming requirements for the user. This integration allows conducting semantic segmentation of microscopy images in a few seconds. In addition, thanks to the patch-based approach it can be employed in images with different sizes. Finally, the human-experts can correct the potential inaccuracies of the prediction in a simple interactive way which can be easily stored and employed to re-train the network to improve its accuracy. In conclusion, our solution focuses on two important bottlenecks to translate leading-edge technologies in computer vision to biomedical research: On one hand, the effortless obtention of high-quality datasets with expertise supervision taking advantage of the proven ability of our CNN solution to generalize from weakly-labelled inaccuracies. On the other hand, the ease of use provided by the GUI integration of our solution to both segment images and interact with the predicted output. Overall this approach looks promising for fast adaptability to new scenarios. [less ▲]

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See detailDeep Learning Quality Control for High-Throughput High-Content Screening Microscopy Images
Garcia Santa Cruz, Beatriz UL; Jarazo, Javier UL; Schwamborn, Jens Christian UL et al

Poster (2019, October 10)

Automation of biological image analysis is essential to boost biomedical research. The study of complex diseases such as neurodegenerative diseases calls for big amounts of data to build models towards ... [more ▼]

Automation of biological image analysis is essential to boost biomedical research. The study of complex diseases such as neurodegenerative diseases calls for big amounts of data to build models towards precision medicine. Such data acquisition is feasible in the context of high-throughput high-content screening (HTHCS) in which the quality of the results relays on the accuracy of image analysis. Deep learning (DL) yields great performance in image analysis tasks especially with big amounts of data such as the produced in HTHCS contexts. Such DL and HTHCS strength is also their biggest weakness since DL solutions are highly sensitive to bad quality datasets. Hence, accurate Quality Control (QC) for microscopy HTHCS becomes an essential step to obtain reliable pipelines for HTHCS analysis. Usually, artifacts found on these platforms are the consequence of out-of-focus and undesirable density variations. The importance of accurate outlier detection becomes essential for both the training process of generic ML solutions (i.e. segmentation or classification) and the QC of the input data such solution will predict on. Moreover, during the QC of the input dataset, we aim not only to discard unsuitable images but to report the user on the quality of its dataset giving the user the choice to keep or discard the bad images. To build the QC solution we employed fluorescent microscopy images of rosella biosensor generated in the HTHCS platform. A total of 15 planes ranging from -6z to +7z steps to the two optimum planes. We evaluated 27 known focus measure operators and concluded that they have low sensitivity in noisy conditions. We propose a CNN solution which predicts the focus error based on the distance to the optimal plane, outperforming the evaluated focus operators. This QC allows for better results in cell segmentation models based on U-Net architecture as well as promising improvements in image classification tasks. [less ▲]

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See detailPATH-29. POTENTIAL OF RAMAN SPECTROSCOPY IN ONCOLOGICAL NEUROSURGERY
Kleine Borgmann, Felix; Husch, Andreas UL; Slimani, Redouane et al

Poster (2019)

Raman spectroscopy (RS) has gained increasing interest for the analysis of biological tissues within the recent years. It is a label-free, non-destructive method providing insights in biochemical ... [more ▼]

Raman spectroscopy (RS) has gained increasing interest for the analysis of biological tissues within the recent years. It is a label-free, non-destructive method providing insights in biochemical properties of tumor cells. It is possible to compare RS signals with histological properties of identical tissue parts. Therefore, RS bears promising potentials in neurosurgical neurooncology. On one hand, it could potentially be used for both intraoperative tumor diagnostics and resection control. On the other hand, it could provide important knowledge on tumor biochemistry and used for a subclassification of tumors with a potential impact on personalized therapy approaches. Within our group, we analyzed over 3000 measurement points in different brain tumors ex vivo with a robotized RS system and correlated the spectral curves with histopathological results. We separated and subclassified the data by AI-based methods. Additionally, we compared the latter results with those of a handheld probe, which is potentially navigatable for in vivo, intraoperative applications. We could demonstrate, that it is possible to separate distinct tumor groups only based on RS signals, especially by using computer-based signal analysis. Furthermore, we could demonstrate the differences of the spectra of deep-frozen and formalin-fixed tissues versus non-fixed tissues. Based on our results, we will highlight the potentials of RS for intraoperative neurosurgical application in resection control for brain tumors, as well as we will focus on the potentials for brain tumor diagnostics based purely on this method or by using it as an adjunct. Those methods bear additional potentials in the field of personalized chemotherapy approaches. [less ▲]

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See detailHabenula deep brain stimulation for refractory bipolar disorder.
Zhang, Chencheng; Kim, Seung-Goo; Li, Dianyou et al

in Brain stimulation (2019)

Bipolar disorder (BD) is a mood disorder associated with significant morbidity and mortality. In many cases, BD can be managed with pharmacotherapy, psychological therapy, or electroconvulsive therapy [1 ... [more ▼]

Bipolar disorder (BD) is a mood disorder associated with significant morbidity and mortality. In many cases, BD can be managed with pharmacotherapy, psychological therapy, or electroconvulsive therapy [1]. For some afflicted patients, however, BD is a chronic and severely disabling condition that is resistant to the aforementioned treatments. Deep brain stimulation (DBS) offers a safe and effective neurosurgical treatment for otherwise refractory movement disorders and obsessive-compulsive disorder [2,3]. [less ▲]

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See detailDystone Bewegungsstörungen bei Kindern
Hertel, Frank UL

in Bächli, Heidi; Lütschg, Jürg; Messing-Jünger, Martina (Eds.) Pädiatrische Neurochirurgie (2018)

Bei den Dystonien handelt es sich um eine heterogene Gruppe verschiedenartiger Erkrankungen mit ähnlichen Symptomen. Klassifikation, Differenzialdiagnosen und Therapien sind komplex und sollten von ... [more ▼]

Bei den Dystonien handelt es sich um eine heterogene Gruppe verschiedenartiger Erkrankungen mit ähnlichen Symptomen. Klassifikation, Differenzialdiagnosen und Therapien sind komplex und sollten von spezialisierten Zentren durchgeführt werden. Die Erkrankungen können vom Säuglings- bis ins späte Erwachsenenalter auftreten. Das Spektrum reicht von oligosymptomatischen Formen bis hin zu solchen, die schwerste Beeinträchtigungen in der Lebensqualität und Behinderungen bedingen können. Zur konservativen Therapie werden Krankengymnastik, orale Medikamente sowie lokale Injektionsbehandlungen eingesetzt. Von neurochirurgisch operativer Seite existieren die Neuromodulationstherapien (implantierbare Pumpen, Tiefe Hirnstimulation) sowie ablative Verfahren. Die neuere Literatur zeigt, dass insbesondere die kognitive Entwicklung bei Kindern umso günstiger beeinflusst werden kann, je früher eine effektive Therapie erfolgt. [less ▲]

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See detailPost-operative deep brain stimulation assessment: Automatic data integration and report generation
Husch, Andreas UL; Petersen, Mikkel V.; Gemmar, Peter et al

in Brain Stimulation (2018)

Background The gold standard for post-operative deep brain stimulation (DBS) parameter tuning is a monopolar review of all stimulation contacts, a strategy being challenged by recent developments of more ... [more ▼]

Background The gold standard for post-operative deep brain stimulation (DBS) parameter tuning is a monopolar review of all stimulation contacts, a strategy being challenged by recent developments of more complex electrode leads. Objective Providing a method to guide clinicians on DBS assessment and parameter tuning by automatically integrating patient individual data. Methods We present a fully automatic method for visualization of individual deep brain structures in relation to a DBS lead by combining precise electrode recovery from post-operative imaging with individual estimates of deep brain morphology utilizing a 7T-MRI deep brain atlas. Results The method was evaluated on 20 STN DBS cases. It demonstrated robust automatic creation of 3D-enabled PDF reports visualizing electrode to brain structure relations and proved valuable in detecting miss placed electrodes. Discussion Automatic DBS assessment is feasible and can conveniently provide clinicians with relevant information on DBS contact positions in relation to important anatomical structures. [less ▲]

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See detailPaCER - A fully automated method for electrode trajectory and contact reconstruction in deep brain stimulation
Husch, Andreas UL; Petersen, Mikkel V.; Gemmar, Peter et al

in NeuroImage: Clinical (2018), 17

Abstract Deep brain stimulation (DBS) is a neurosurgical intervention where electrodes are permanently implanted into the brain in order to modulate pathologic neural activity. The post-operative ... [more ▼]

Abstract Deep brain stimulation (DBS) is a neurosurgical intervention where electrodes are permanently implanted into the brain in order to modulate pathologic neural activity. The post-operative reconstruction of the DBS electrodes is important for an efficient stimulation parameter tuning. A major limitation of existing approaches for electrode reconstruction from post-operative imaging that prevents the clinical routine use is that they are manual or semi-automatic, and thus both time-consuming and subjective. Moreover, the existing methods rely on a simplified model of a straight line electrode trajectory, rather than the more realistic curved trajectory. The main contribution of this paper is that for the first time we present a highly accurate and fully automated method for electrode reconstruction that considers curved trajectories. The robustness of our proposed method is demonstrated using a multi-center clinical dataset consisting of N=44 electrodes. In all cases the electrode trajectories were successfully identified and reconstructed. In addition, the accuracy is demonstrated quantitatively using a high-accuracy phantom with known ground truth. In the phantom experiment, the method could detect individual electrode contacts with high accuracy and the trajectory reconstruction reached an error level below 100 μm (0.046 ± 0.025 mm). An implementation of the method is made publicly available such that it can directly be used by researchers or clinicians. This constitutes an important step towards future integration of lead reconstruction into standard clinical care. [less ▲]

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See detailShape-aware surface reconstruction from sparse 3D point-clouds
Bernard, Florian UL; Salamanca Mino, Luis UL; Thunberg, Johan UL et al

in Medical Image Analysis (2017), 38

The reconstruction of an object’s shape or surface from a set of 3D points plays an important role in medical image analysis, e.g. in anatomy reconstruction from tomographic measurements or in the process ... [more ▼]

The reconstruction of an object’s shape or surface from a set of 3D points plays an important role in medical image analysis, e.g. in anatomy reconstruction from tomographic measurements or in the process of aligning intra-operative navigation and preoperative planning data. In such scenarios, one usually has to deal with sparse data, which significantly aggravates the problem of reconstruction. However, medical applications often provide contextual information about the 3D point data that allow to incorporate prior knowledge about the shape that is to be reconstructed. To this end, we propose the use of a statistical shape model (SSM) as a prior for surface reconstruction. The SSM is represented by a point distribution model (PDM), which is associated with a surface mesh. Using the shape distribution that is modelled by the PDM, we formulate the problem of surface reconstruction from a probabilistic perspective based on a Gaussian Mixture Model (GMM). In order to do so, the given points are interpreted as samples of the GMM. By using mixture components with anisotropic covariances that are “oriented” according to the surface normals at the PDM points, a surface-based fitting is accomplished. Estimating the parameters of the GMM in a maximum a posteriori manner yields the reconstruction of the surface from the given data points. We compare our method to the extensively used Iterative Closest Points method on several different anatomical datasets/SSMs (brain, femur, tibia, hip, liver) and demonstrate superior accuracy and robustness on sparse data. [less ▲]

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See detailIntegration of sparse electrophysiological measurements with preoperative MRI using 3D surface estimation in deep brain stimulation surgery
Husch, Andreas UL; Gemmar, Peter; Thunberg, Johan UL et al

in Webster, Robert; Fei, Baowei (Eds.) Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling (2017, February 14)

Intraoperative microelectrode recordings (MER) have been used for several decades to guide neurosurgeons during the implantation of Deep Brain Stimulation (DBS) electrodes, especially when targeting the ... [more ▼]

Intraoperative microelectrode recordings (MER) have been used for several decades to guide neurosurgeons during the implantation of Deep Brain Stimulation (DBS) electrodes, especially when targeting the subthalamic nucleus (STN) to suppress the symptoms of Parkinson’s Disease. The standard approach is to use an array of up to five MER electrodes in a fixed configuration. Interpretation of the recorded signals yields a spatiallyvery sparse set of information about the morphology of the respective brain structures in the targeted area. However, no aid is currently available for surgeons to intraoperatively integrate this information with other data available on the patient’s individual morphology (e.g. MR imaging data used for surgical planning). This integration might allow surgeons to better determine the most probable position of the electrodes within the target structure during surgery. This paper suggests a method for reconstructing a surface patch from the sparse MER dataset utilizing additional a-priori knowledge about the geometrical configuration of the measurement electrodes. The conventional representation of MER measurements as intervals of target region/non-target region is therefore transformed into an equivalent boundary set representation, allowing efficient point-based calculations. Subsequently, the problem is to integrate the resulting patch with a preoperative model of the target structure, which can be formulated as registration problem minimizing a distance measure between the two surfaces. When restricting this registration procedure to translations, which is reasonable given certain geometric considerations, the problem can be solved globally by employing an exhaustive search with arbitrary precision in polynomial time. The proposed method is demonstrated using bilateral STN/Substantia Nigra segmentation data from preoperative MRIs of 17 Patients with simulated MER electrode placement. When using simulated data of heavily perturbed electrodes and subsequent MER measuremen [less ▲]

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See detailFast Correspondences for Statistical Shape Models of Brain Structures
Bernard, Florian UL; Vlassis, Nikos UL; Gemmar, Peter et al

in SPIE Medical Imaging (2016, March)

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See detailLinear Shape Deformation Models with Local Support using Graph-based Structured Matrix Factorisation
Bernard, Florian UL; Gemmar, Peter; Hertel, Frank UL et al

in Linear Shape Deformation Models with Local Support using Graph-based Structured Matrix Factorisation (2016)

Representing 3D shape deformations by linear models in high-dimensional space has many applications in computer vision and medical imaging, such as shape-based interpolation or segmentation. Commonly ... [more ▼]

Representing 3D shape deformations by linear models in high-dimensional space has many applications in computer vision and medical imaging, such as shape-based interpolation or segmentation. Commonly, using Principal Components Analysis a low-dimensional (affine) subspace of the high-dimensional shape space is determined. However, the resulting factors (the most dominant eigenvectors of the covariance matrix) have global support, i.e. changing the coefficient of a single factor deforms the entire shape. In this paper, a method to obtain deformation factors with local support is presented. The benefits of such models include better flexibility and interpretability as well as the possibility of interactively deforming shapes locally. For that, based on a well-grounded theoretical motivation, we formulate a matrix factorisation problem employing sparsity and graph-based regularisation terms. We demonstrate that for brain shapes our method outperforms the state of the art in local support models with respect to generalisation ability and sparse shape reconstruction, whereas for human body shapes our method gives more realistic deformations. [less ▲]

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See detailShape-aware 3D Interpolation using Statistical Shape Models
Bernard, Florian UL; Salamanca Mino, Luis UL; Thunberg, Johan UL et al

in Symposium on Statistical Shape Models and Applications, Delemont, Switzerland, October 2015 (2015, October)

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See detailAssessment of Electrode Displacement and Deformation with Respect to Pre-Operative Planning in Deep Brain Stimulation
Husch, Andreas UL; Gemmar, Peter; Lohscheller, Jörg et al

in Handels, Heinz; Deserno, Thomas Martin; Meinzer, Hans-Peter (Eds.) et al Bildverarbeitung für die Medizin 2015 (2015)

The post-operative validation of deep brain stimulation electrode displacement and deformation is an important task towards improved DBS targeting. In this paper a method is proposed to align models of ... [more ▼]

The post-operative validation of deep brain stimulation electrode displacement and deformation is an important task towards improved DBS targeting. In this paper a method is proposed to align models of deep brain stimulation electrodes that are automatically extracted from post-operative CT imaging in a common coordinate system utilizing the planning data as reference. This enables the assessment of electrode displacement and deformation over the whole length of the trajectory with respect to the pre-operative planning. Accordingly, it enables the estimation of plan deviations in the surgical process as well as cross-patient statistics on electrode deformation, e.g. the bending induced by brain-shift. [less ▲]

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See detailA solution for Multi-Alignment by Transformation Synchronisation
Bernard, Florian UL; Thunberg, Johan UL; Gemmar, Peter et al

in The proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)

The alignment of a set of objects by means of transformations plays an important role in computer vision. Whilst the case for only two objects can be solved globally, when multiple objects are considered ... [more ▼]

The alignment of a set of objects by means of transformations plays an important role in computer vision. Whilst the case for only two objects can be solved globally, when multiple objects are considered usually iterative methods are used. In practice the iterative methods perform well if the relative transformations between any pair of objects are free of noise. However, if only noisy relative transformations are available (e.g. due to missing data or wrong correspondences) the iterative methods may fail. Based on the observation that the underlying noise-free transformations lie in the null space of a matrix that can directly be obtained from pairwise alignments, this paper presents a novel method for the synchronisation of pairwise transformations such that they are globally consistent. Simulations demonstrate that for a high amount of noise, a large proportion of missing data and even for wrong correspondence assignments the method delivers encouraging results. [less ▲]

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See detailTransitively Consistent and Unbiased Multi-Image Registration Using Numerically Stable Transformation Synchronisation
Bernard, Florian UL; Thunberg, Johan UL; Salamanca Mino, Luis UL et al

in MIDAS Journal (2015)

Abstract. Transitive consistency of pairwise transformations is a desir- able property of groupwise image registration procedures. The transfor- mation synchronisation method [4] is able to retrieve ... [more ▼]

Abstract. Transitive consistency of pairwise transformations is a desir- able property of groupwise image registration procedures. The transfor- mation synchronisation method [4] is able to retrieve transitively con- sistent pairwise transformations from pairwise transformations that are initially not transitively consistent. In the present paper, we present a numerically stable implementation of the transformation synchronisa- tion method for a ne transformations, which can deal with very large translations, such as those occurring in medical images where the coor- dinate origins may be far away from each other. By using this method in conjunction with any pairwise (a ne) image registration algorithm, a transitively consistent and unbiased groupwise image registration can be achieved. Experiments involving the average template generation from 3D brain images demonstrate that the method is more robust with re- spect to outliers and achieves higher registration accuracy compared to reference-based registration. [less ▲]

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See detailSusceptibility-Weighted MRI for Deep Brain Stimulation: Potentials in Trajectory Planning
Hertel, Frank UL; Husch, Andreas UL; Dooms, Georges et al

in Stereotactic & Functional Neurosurgery (2015), 93(5), 303-308

Background: Deep brain stimulation (DBS) trajectory plan- ning is mostly based on standard 3-D T1-weighted gado- linium-enhanced MRI sequences (T1-Gd). Susceptibility- weighted MRI sequences (SWI) show ... [more ▼]

Background: Deep brain stimulation (DBS) trajectory plan- ning is mostly based on standard 3-D T1-weighted gado- linium-enhanced MRI sequences (T1-Gd). Susceptibility- weighted MRI sequences (SWI) show neurovascular struc- tures without the use of contrast agents. The aim of this study was to investigate whether SWI might be useful in DBS trajectory planning. Methods: We performed bilateral DBS planning using conventional T1-Gd images of 10 patients with different kinds of movement disorders. Afterwards, we matched SWI sequences and compared the visibility of vas- cular structures in both imaging modalities. Results: By ana- lyzing 100 possible trajectories, we found a potential vascu- lar conflict in 13 trajectories based on T1-Gd in contrast to 53 in SWI. Remarkably, all vessels visible in T1-Gd were also de- picted in SWI, whereas SWI showed many additional vascular structures which could not be identified in T1-Gd. Conclu- sion/Discussion: The sensitivity for detecting neurovascular structures for DBS planning seems to be significantly higher in SWI. As SWI does not require a contrast agent, we suggest that SWI may be a valuable alternative to T1-Gd MRI for DBS trajectory planning. Furthermore, the data analysis suggests that vascular interactions of DBS trajectories might be more frequent than expected from the very low incidence of symptomatic bleedings. The explanation for this is currently the subject of debate and merits further studies. [less ▲]

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See detailDiffusion tensor imaging--arcuate fasciculus and the importance for the neurosurgeon.
Hana, Ardian; Dooms, Georges; Boecher-Schwarz, Hans et al

in Clinical neurology and neurosurgery (2015), 132

OBJECTIVE: Tumors in eloquent areas of the brain like Broca or Wernicke might have disastrous consequences for patients. We intended to visualize the arcuate fasciculus (AF) and to demonstrate his ... [more ▼]

OBJECTIVE: Tumors in eloquent areas of the brain like Broca or Wernicke might have disastrous consequences for patients. We intended to visualize the arcuate fasciculus (AF) and to demonstrate his relation with the corticospinal tract and the visual pathway using diffusion tensor imaging (DTI). METHODS: We depicted between 2012 and 2014 the AF in 71 patients. Men and women of all ages were included. Eleven patients had postoperative controls also. We used a 3DT1-sequence for the navigation. Furthermore T2- and DTI-sequences were performed. The FOV was 200 x 200 mm(2), slice thickness 2mm, and an acquisition matrix of 96 x 96 yielding nearly isotropic voxels of 2 x 2 x 2 mm. 3-Tesla-MRI was carried out strictly axial using 32 gradient directions and one b0-image. We used Echo-Planar-Imaging (EPI) and ASSET parallel imaging with an acceleration factor of 2. b-Value was 800 s/mm(2). Additional scanning time was less than 9 min. RESULTS: AF was portrayed in 63 patients bilaterally. In one glioblastoma patient it was impossible to visualize the left AF and in seven other patients we could not portray the right one. The lesions affected AF by disrupting or displacing the fibers. CONCLUSIONS: DTI might be a useful tool to portray AF. It is time-saving and can be used to preserve morbidity in patients with lesions in eloquent brain areas. It might give deeper insights of the white matter and the reorganization of AF-fibers postoperatively. [less ▲]

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See detailStabilization with the Dynamic Cervical Implant: a novel treatment approach following cervical discectomy and decompression.
Matge, Guy; Berthold, Christophe; Gunness, Vimal Raj Nitish et al

in Journal of neurosurgery. Spine (2015), 22(3), 237-45

OBJECT: Although cervical total disc replacement (TDR) has shown equivalence or superiority to anterior cervical discectomy and fusion (ACDF), potential problems include nonphysiological motion ... [more ▼]

OBJECT: Although cervical total disc replacement (TDR) has shown equivalence or superiority to anterior cervical discectomy and fusion (ACDF), potential problems include nonphysiological motion (hypermobility), accelerated degeneration of the facet joints, particulate wear, and compromise of the mechanical integrity of the endplate during device fixation. Dynamic cervical stabilization is a novel motion-preserving concept that facilitates controlled, limited flexion and extension, but prevents axial rotation and lateral bending, thereby reducing motion across the facet joints. Shock absorption of the Dynamic Cervical Implant (DCI) device is intended to protect adjacent levels from accelerated degeneration. METHODS: The authors conducted a prospective evaluation of 53 consecutive patients who underwent DCI stabilization for the treatment of 1-level (n = 42), 2-level (n = 9), and 3-level (n = 2) cervical disc disease with radiculopathy or myelopathy. Forty-seven patients (89%) completed all clinical and radiographic outcomes at a minimum of 24 months. Clinical outcomes consisted of Neck Disability Index (NDI) and visual analog scale (VAS) scores, neurological function at baseline and at latest follow-up, as well as patient satisfaction. Flexion-extension radiography was evaluated for device motion, implant migration, subsidence, and heterotopic ossification. Cervical sagittal alignment (Cobb angle), functional spinal unit (FSU) angle, and range of motion (ROM) at index and adjacent levels were evaluated with WEB 1000 software. RESULTS: The NDI score, VAS neck and arm pain scores, and neurological deficits were significantly reduced at each postoperative time point compared with baseline (p < 0.0001). At 24 months postoperatively, 91% of patients were very satisfied and 9% somewhat satisfied, while 89% would definitely and 11% would probably elect to have the same surgery again. In 47 patients with 58 operated levels, the radiographic assessment showed good motion (5 degrees -12 degrees ) of the device in 57%, reduced motion (2 degrees -5 degrees ) in 34.5%, and little motion (0-2 degrees ) in 8.5%. The Cobb and FSU angles improved, showing a clear tendency for lordosis with the DCI. Motion greater than 2 degrees of the treated segment could be preserved in 91.5%, while 8.5% had a near segmental fusion. Mean ROM at index levels demonstrated satisfying motion preservation with DCI. Mean ROM at upper and lower adjacent levels showed maintenance of adjacent-level kinematics. Heterotopic ossification, including 20% minor and 15% major, had no direct impact on clinical results. There were 2 endplate subsidences detected with an increased segmental lordosis. One asymptomatic anterior device migration required reoperation. Three patients underwent a secondary surgery in another segment during follow-up, twice for a new disc herniation and once for an adjacent degeneration. There was no posterior migration and no device breakage. CONCLUSIONS: Preliminary results indicate that the DCI implanted using a proper surgical technique is safe and facilitates excellent clinical outcomes, maintains index-and adjacent-level ROM in the majority of cases, improves sagittal alignment, and may be suitable for patients with facet arthrosis who would otherwise not be candidates for cervical TDR. Shock absorption together with maintained motion in the DCI may protect adjacent levels from early degeneration in longer follow-up. [less ▲]

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See detailVisualization of the medial forebrain bundle using diffusion tensor imaging.
Hana, Ardian; Hana, Anisa; Dooms, Georges et al

in Frontiers in Neuroanatomy (2015), 9

Diffusion tensor imaging is a technique that enables physicians the portrayal of white matter tracts in vivo. We used this technique in order to depict the medial forebrain bundle (MFB) in 15 consecutive ... [more ▼]

Diffusion tensor imaging is a technique that enables physicians the portrayal of white matter tracts in vivo. We used this technique in order to depict the medial forebrain bundle (MFB) in 15 consecutive patients between 2012 and 2015. Men and women of all ages were included. There were six women and nine men. The mean age was 58.6 years (39-77). Nine patients were candidates for an eventual deep brain stimulation. Eight of them suffered from Parkinson's disease and one had multiple sclerosis. The remaining six patients suffered from different lesions which were situated in the frontal lobe. These were 2 metastasis, 2 meningiomas, 1 cerebral bleeding, and 1 glioblastoma. We used a 3DT1-sequence for the navigation. Furthermore T2- and DTI- sequences were performed. The FOV was 200 x 200 mm(2), slice thickness 2 mm, and an acquisition matrix of 96 x 96 yielding nearly isotropic voxels of 2 x 2 x 2 mm. 3-Tesla-MRI was carried out strictly axial using 32 gradient directions and one b0-image. We used Echo-Planar-Imaging (EPI) and ASSET parallel imaging with an acceleration factor of 2. b-value was 800 s/mm(2). The maximal angle was 50 degrees . Additional scanning time was < 9 min. We were able to visualize the MFB in 12 of our patients bilaterally and in the remaining three patients we depicted the MFB on one side. It was the contralateral side of the lesion. These were 2 meningiomas and one metastasis. Portrayal of the MFB is possible for everyday routine for neurosurgical interventions. As part of the reward circuitry it might be of substantial importance for neurosurgeons during deep brain stimulation in patients with psychiatric disorders. Surgery in this part of the brain should always take the preservation of this white matter tract into account. [less ▲]

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