Multi-perspective region-based CNNs for vertebrae labeling in intraoperative long-length images.

PURPOSE: Effective aggregation of intraoperative x-ray images that capture the patient anatomy from multiple view-angles has the potential to enable and improve automated image analysis that can be readily performed during surgery. We present multi-perspective region-based neural networks that leverage knowledge of the imaging geometry for automatic vertebrae labeling in Long-Film images - a novel tomographic imaging modality with an extended field-of-view for spine imaging. METHOD: A multi-perspective network architecture was designed to exploit small view-angle disparities produced by a multi-slot collimator and consolidate information from overlapping image regions. A second network incorporates large view-angle disparities to jointly perform labeling on images from multiple views (viz., AP and lateral). A recurrent module incorporates contextual information and enforce anatomical order for the detected vertebrae. The three modules are combined to form the multi-view multi-slot (MVMS) network for labeling vertebrae using images from all available perspectives. The network was trained on images synthesized from 297 CT images and tested on 50 AP and 50 lateral Long-Film images acquired from 13 cadaveric specimens. Labeling performance of the multi-perspective networks was evaluated with respect to the number of vertebrae appearances and presence of surgical instrumentation. RESULTS: The MVMS network achieved an F1 score of >96% and an average vertebral localization error of 3.3 mm, with 88.3% labeling accuracy on both AP and lateral images - (15.5% and 35.0% higher than conventional Faster R-CNN on AP and lateral views, respectively). Aggregation of multiple appearances of the same vertebra using the multi-slot network significantly improved the labeling accuracy (p < 0.05). Using the multi-view network, labeling accuracy on the more challenging lateral views was improved to the same level as that of the AP views. The approach demonstrated robustness to the presence of surgical instrumentation, commonly encountered in intraoperative images, and achieved comparable performance in images with and without instrumentation (88.9% vs. 91.2% labeling accuracy). CONCLUSION: The MVMS network demonstrated effective multi-perspective aggregation, providing means for accurate, automated vertebrae labeling during spine surgery. The algorithms may be generalized to other imaging tasks and modalities that involve multiple views with view-angle disparities (e.g., bi-plane radiography). Predicted labels can help avoid adverse events during surgery (e.g., wrong-level surgery), establish correspondence with labels in preoperative modalities to facilitate image registration, and enable automated measurement of spinal alignment metrics for intraoperative assessment of spinal curvature.

Joint synthesis and registration network for deformable MR-CBCT image registration for neurosurgical guidance.

Objective.The accuracy of navigation in minimally invasive neurosurgery is often challenged by deep brain deformations (up to 10 mm due to egress of cerebrospinal fluid during neuroendoscopic approach). We propose a deep learning-based deformable registration method to address such deformations between preoperative MR and intraoperative CBCT.Approach.The registration method uses a joint image synthesis and registration network (denoted JSR) to simultaneously synthesize MR and CBCT images to the CT domain and perform CT domain registration using a multi-resolution pyramid. JSR was first trained using a simulated dataset (simulated CBCT and simulated deformations) and then refined on real clinical images via transfer learning. The performance of the multi-resolution JSR was compared to a single-resolution architecture as well as a series of alternative registration methods (symmetric normalization (SyN), VoxelMorph, and image synthesis-based registration methods).Main results.JSR achieved median Dice coefficient (DSC) of 0.69 in deep brain structures and median target registration error (TRE) of 1.94 mm in the simulation dataset, with improvement from single-resolution architecture (median DSC = 0.68 and median TRE = 2.14 mm). Additionally, JSR achieved superior registration compared to alternative methods-e.g. SyN (median DSC = 0.54, median TRE = 2.77 mm), VoxelMorph (median DSC = 0.52, median TRE = 2.66 mm) and provided registration runtime of less than 3 s. Similarly in the clinical dataset, JSR achieved median DSC = 0.72 and median TRE = 2.05 mm.Significance.The multi-resolution JSR network resolved deep brain deformations between MR and CBCT images with performance superior to other state-of-the-art methods. The accuracy and runtime support translation of the method to further clinical studies in high-precision neurosurgery.

Deformable MR-CT image registration using an unsupervised, dual-channel network for neurosurgical guidance.

PURPOSE: The accuracy of minimally invasive, intracranial neurosurgery can be challenged by deformation of brain tissue - e.g., up to 10 mm due to egress of cerebrospinal fluid during neuroendoscopic approach. We report an unsupervised, deep learning-based registration framework to resolve such deformations between preoperative MR and intraoperative CT with fast runtime for neurosurgical guidance. METHOD: The framework incorporates subnetworks for MR and CT image synthesis with a dual-channel registration subnetwork (with synthesis uncertainty providing spatially varying weights on the dual-channel loss) to estimate a diffeomorphic deformation field from both the MR and CT channels. An end-to-end training is proposed that jointly optimizes both the synthesis and registration subnetworks. The proposed framework was investigated using three datasets: (1) paired MR/CT with simulated deformations; (2) paired MR/CT with real deformations; and (3) a neurosurgery dataset with real deformation. Two state-of-the-art methods (Symmetric Normalization and VoxelMorph) were implemented as a basis of comparison, and variations in the proposed dual-channel network were investigated, including single-channel registration, fusion without uncertainty weighting, and conventional sequential training of the synthesis and registration subnetworks. RESULTS: The proposed method achieved: (1) Dice coefficient = 0.82±0.07 and TRE = 1.2 ± 0.6 mm on paired MR/CT with simulated deformations; (2) Dice coefficient = 0.83 ± 0.07 and TRE = 1.4 ± 0.7 mm on paired MR/CT with real deformations; and (3) Dice = 0.79 ± 0.13 and TRE = 1.6 ± 1.0 mm on the neurosurgery dataset with real deformations. The dual-channel registration with uncertainty weighting demonstrated superior performance (e.g., TRE = 1.2 ± 0.6 mm) compared to single-channel registration (TRE = 1.6 ± 1.0 mm, p < 0.05 for CT channel and TRE = 1.3 ± 0.7 mm for MR channel) and dual-channel registration without uncertainty weighting (TRE = 1.4 ± 0.8 mm, p < 0.05). End-to-end training of the synthesis and registration subnetworks also improved performance compared to the conventional sequential training strategy (TRE = 1.3 ± 0.6 mm). Registration runtime with the proposed network was ∼3 s. CONCLUSION: The deformable registration framework based on dual-channel MR/CT registration with spatially varying weights and end-to-end training achieved geometric accuracy and runtime that was superior to state-of-the-art baseline methods and various ablations of the proposed network. The accuracy and runtime of the method may be compatible with the requirements of high-precision neurosurgery.

Deformable 3D-2D registration for high-precision guidance and verification of neuroelectrode placement.

Purpose.Accurate neuroelectrode placement is essential to effective monitoring or stimulation of neurosurgery targets. This work presents and evaluates a method that combines deep learning and model-based deformable 3D-2D registration to guide and verify neuroelectrode placement using intraoperative imaging.Methods.The registration method consists of three stages: (1) detection of neuroelectrodes in a pair of fluoroscopy images using a deep learning approach; (2) determination of correspondence and initial 3D localization among neuroelectrode detections in the two projection images; and (3) deformable 3D-2D registration of neuroelectrodes according to a physical device model. The method was evaluated in phantom, cadaver, and clinical studies in terms of (a) the accuracy of neuroelectrode registration and (b) the quality of metal artifact reduction (MAR) in cone-beam CT (CBCT) in which the deformably registered neuroelectrode models are taken as input to the MAR.Results.The combined deep learning and model-based deformable 3D-2D registration approach achieved 0.2 ± 0.1 mm accuracy in cadaver studies and 0.6 ± 0.3 mm accuracy in clinical studies. The detection network and 3D correspondence provided initialization of 3D-2D registration within 2 mm, which facilitated end-to-end registration runtime within 10 s. Metal artifacts, quantified as the standard deviation in voxel values in tissue adjacent to neuroelectrodes, were reduced by 72% in phantom studies and by 60% in first clinical studies.Conclusions.The method combines the speed and generalizability of deep learning (for initialization) with the precision and reliability of physical model-based registration to achieve accurate deformable 3D-2D registration and MAR in functional neurosurgery. Accurate 3D-2D guidance from fluoroscopy could overcome limitations associated with deformation in conventional navigation, and improved MAR could improve CBCT verification of neuroelectrode placement.

3D vertebrae labeling in spine CT: an accurate, memory-efficient (Ortho2D) framework.

Purpose.Accurate localization and labeling of vertebrae in computed tomography (CT) is an important step toward more quantitative, automated diagnostic analysis and surgical planning. In this paper, we present a framework (called Ortho2D) for vertebral labeling in CT in a manner that is accurate and memory-efficient.Methods. Ortho2D uses two independent faster R-convolutional neural network networks to detect and classify vertebrae in orthogonal (sagittal and coronal) CT slices. The 2D detections are clustered in 3D to localize vertebrae centroids in the volumetric CT and classify the region (cervical, thoracic, lumbar, or sacral) and vertebral level. A post-process sorting method incorporates the confidence in network output to refine classifications and reduce outliers. Ortho2D was evaluated on a publicly available dataset containing 302 normal and pathological spine CT images with and without surgical instrumentation. Labeling accuracy and memory requirements were assessed in comparison to other recently reported methods. The memory efficiency of Ortho2D permitted extension to high-resolution CT to investigate the potential for further boosts to labeling performance.Results. Ortho2D achieved overall vertebrae detection accuracy of 97.1%, region identification accuracy of 94.3%, and individual vertebral level identification accuracy of 91.0%. The framework achieved 95.8% and 83.6% level identification accuracy in images without and with surgical instrumentation, respectively. Ortho2D met or exceeded the performance of previously reported 2D and 3D labeling methods and reduced memory consumption by a factor of ∼50 (at 1 mm voxel size) compared to a 3D U-Net, allowing extension to higher resolution datasets than normally afforded. The accuracy of level identification increased from 80.1% (for standard/low resolution CT) to 95.1% (for high-resolution CT).Conclusions. The Ortho2D method achieved vertebrae labeling performance that is comparable to other recently reported methods with significant reduction in memory consumption, permitting further performance boosts via application to high-resolution CT.

Data-Driven Deformable 3D-2D Registration for Guiding Neuroelectrode Placement in Deep Brain Stimulation.

Purpose: Deep brain stimulation is a neurosurgical procedure used in treatment of a growing spectrum of movement disorders. Inaccuracies in electrode placement, however, can result in poor symptom control or adverse effects and confound variability in clinical outcomes. A deformable 3D-2D registration method is presented for high-precision 3D guidance of neuroelectrodes. Methods: The approach employs a model-based, deformable algorithm for 3D-2D image registration. Variations in lead design are captured in a parametric 3D model based on a B-spline curve. The registration is solved through iterative optimization of 16 degrees-of-freedom that maximize image similarity between the 2 acquired radiographs and simulated forward projections of the neuroelectrode model. The approach was evaluated in phantom models with respect to pertinent imaging parameters, including view selection and imaging dose. Results: The results demonstrate an accuracy of (0.2 ± 0.2) mm in 3D localization of individual electrodes. The solution was observed to be robust to changes in pertinent imaging parameters, which demonstrate accurate localization with ≥20° view separation and at 1/10th the dose of a standard fluoroscopy frame. Conclusions: The presented approach provides the means for guiding neuroelectrode placement from 2 low-dose radiographic images in a manner that accommodates potential deformations at the target anatomical site. Future work will focus on improving runtime though learning-based initialization, application in reducing reconstruction metal artifacts for 3D verification of placement, and extensive evaluation in clinical data from an IRB study underway.

Data-Driven Detection and Registration of Spine Surgery Instrumentation in Intraoperative Images.

Purpose: Conventional model-based 3D-2D registration algorithms can be challenged by limited capture range, model validity, and stringent intraoperative runtime requirements. In this work, a deep convolutional neural network was used to provide robust initialization of a registration algorithm (known-component registration, KC-Reg) for 3D localization of spine surgery implants, combining the speed and global support of data-driven approaches with the previously demonstrated accuracy of model-based registration. Methods: The approach uses a Faster R-CNN architecture to detect and localize a broad variety and orientation of spinal pedicle screws in clinical images. Training data were generated using projections from 17 clinical cone-beam CT scans and a library of screw models to simulate implants. Network output was processed to provide screw count and 2D poses. The network was tested on two test datasets of 2,000 images, each depicting real anatomy and realistic spine surgery instrumentation - one dataset involving the same patient data as in the training set (but with different screws, poses, image noise, and affine transformations) and one dataset with five patients unseen in the test data. Assessment of device detection was quantified in terms of accuracy and specificity, and localization accuracy was evaluated in terms of intersection-over-union (IOU) and distance between true and predicted bounding box coordinates. Results: The overall accuracy of pedicle screw detection was ~86.6% (85.3% for the same-patient dataset and 87.8% for the many-patient dataset), suggesting that the screw detection network performed reasonably well irrespective of disparate, complex anatomical backgrounds. The precision of screw detection was ~92.6% (95.0% and 90.2% for the respective same-patient and many-patient datasets). The accuracy of screw localization was within 1.5 mm (median difference of bounding box coordinates), and median IOU exceeded 0.85. For purposes of initializing a 3D-2D registration algorithm, the accuracy was observed to be well within the typical capture range of KC-Reg.1. Conclusions: Initial evaluation of network performance indicates sufficient accuracy to integrate with algorithms for implant registration, guidance, and verification in spine surgery. Such capability is of potential use in surgical navigation, robotic assistance, and data-intensive analysis of implant placement in large retrospective datasets. Future work includes correspondence of multiple views, 3D localization, screw classification, and expansion of the training dataset to a broader variety of anatomical sites, number of screws, and types of implants.

Known-component metal artifact reduction (KC-MAR) for cone-beam CT.

Intraoperative cone-beam CT (CBCT) is increasingly used for surgical navigation and validation of device placement. In spinal deformity correction, CBCT provides visualization of pedicle screws and fixation rods in relation to adjacent anatomy. This work reports and evaluates a method that uses prior information regarding such surgical instrumentation for improved metal artifact reduction (MAR). The known-component MAR (KC-MAR) approach achieves precise localization of instrumentation in projection images using rigid or deformable 3D-2D registration of component models, thereby overcoming residual errors associated with segmentation-based methods. Projection data containing metal components are processed via 2D inpainting of the detector signal, followed by 3D filtered back-projection (FBP). Phantom studies were performed to identify nominal algorithm parameters and quantitatively investigate performance over a range of component material composition and size. A cadaver study emulating screw and rod placement in spinal deformity correction was conducted to evaluate performance under realistic clinical imaging conditions. KC-MAR demonstrated reduction in artifacts (standard deviation in voxel values) across a range of component types and dose levels, reducing the artifact to 5-10 HU. Accurate component delineation was demonstrated for rigid (screw) and deformable (rod) models with sub-mm registration errors, and a single-pixel dilation of the projected components was found to compensate for partial-volume effects. Artifacts associated with spine screws and rods were reduced by 40%-80% in cadaver studies, and the resulting images demonstrated markedly improved visualization of instrumentation (e.g. screw threads) within cortical margins. The KC-MAR algorithm combines knowledge of surgical instrumentation with 3D image reconstruction in a manner that overcomes potential pitfalls of segmentation. The approach is compatible with FBP-thereby maintaining simplicity in a manner that is consistent with surgical workflow-or more sophisticated model-based reconstruction methods that could further improve image quality and/or help reduce radiation dose.

3D-2D Image Registration in Virtual Long-Film Imaging: Application to Spinal Deformity Correction.

PURPOSE: Intraoperative 2D virtual long-film (VLF) imaging is investigated for 3D guidance and confirmation of the surgical product in spinal deformity correction. Multi-slot-scan geometry (rather than a single-slot "topogram") is exploited to produce parallax views of the scene for accurate 3D colocalization from a single radiograph. METHODS: The multi-slot approach uses additional angled collimator apertures to form fan-beams with disparate views (parallax) of anatomy and instrumentation and to extend field-of-view beyond the linear motion limits. Combined with a knowledge of surgical implants (pedicle screws and/or spinal rods modeled as "known components"), 3D-2D image registration is used to solve for pose estimates via optimization of image gradient correlation. Experiments were conducted in cadaver studies emulating the system geometry of the O-arm (Medtronic, Minneapolis MN). RESULTS: Experiments demonstrated feasibility of multi-slot VLF and quantified the geometric accuracy of 3D-2D registration using VLF acquisitions. Registration of pedicle screws from a single VLF yielded mean target registration error of (2.0±0.7) mm, comparable to the accuracy of surgical trackers and registration using multiple radiographs (e.g., AP and LAT). CONCLUSIONS: 3D-2D registration in a single VLF image offers a promising new solution for image guidance in spinal deformity correction. The ability to accurately resolve pose from a single view absolves workflow challenges of multiple-view registration and suggests application beyond spine surgery, such as reduction of long-bone fractures.

Image quality and dose characteristics for an O-arm intraoperative imaging system with model-based image reconstruction.

PURPOSE: To assess the imaging performance and radiation dose characteristics of the O-arm CBCT imaging system (Medtronic Inc., Littleton MA) and demonstrate the potential for improved image quality and reduced dose via model-based image reconstruction (MBIR). METHODS: Two main studies were performed to investigate previously unreported characteristics of the O-arm system. First is an investigation of dose and 3D image quality achieved with filtered back-projection (FBP) - including enhancements in geometric calibration, handling of lateral truncation and detector saturation, and incorporation of an isotropic apodization filter. Second is implementation of an MBIR algorithm based on Huber-penalized likelihood estimation (PLH) and investigation of image quality improvement at reduced dose. Each study involved measurements in quantitative phantoms as a basis for analysis of contrast-to-noise ratio and spatial resolution as well as imaging of a human cadaver to test the findings under realistic imaging conditions. RESULTS: View-dependent calibration of system geometry improved the accuracy of reconstruction as quantified by the full-width at half maximum of the point-spread function - from 0.80 to 0.65 mm - and yielded subtle but perceptible improvement in high-contrast detail of bone (e.g., temporal bone). Standard technique protocols for the head and body imparted absorbed dose of 16 and 18 mGy, respectively. For low-to-medium contrast (<100 HU) imaging at fixed spatial resolution (1.3 mm edge-spread function) and fixed dose (6.7 mGy), PLH improved CNR over FBP by +48% in the head and +35% in the body. Evaluation at different dose levels demonstrated 30% increase in CNR at 62% of the dose in the head and 90% increase in CNR at 50% dose in the body. CONCLUSIONS: A variety of improvements in FBP implementation (geometric calibration, truncation and saturation effects, and isotropic apodization) offer the potential for improved image quality and reduced radiation dose on the O-arm system. Further gains are possible with MBIR, including improved soft-tissue visualization, low-dose imaging protocols, and extension to methods that naturally incorporate prior information of patient anatomy and/or surgical instrumentation.

Advanced Image Registration and Reconstruction using the O-Arm System: Dose Reduction, Image Quality, and Guidance using Known-Component Models.

PURPOSE: Model-based image registration and reconstruction offer strong potential for improved safety and precision in image-guided interventions. Advantages include reduced radiation dose, improved soft-tissue visibility (detection of complications), and accurate guidance with/without a dedicated navigation system. This work reports the development and performance of such methods on an O-arm system for intraoperative imaging and translates them to first clinical studies. METHODS: Two novel methodologies predicate the work: (1) Known-Component Registration (KC-Reg) for 3D localization of the patient and interventional devices from 2D radiographs; and (2) Penalized-Likelihood reconstruction (PLH) for improved 3D image quality and dose reduction. A thorough assessment of geometric stability, dosimetry, and image quality was performed to define algorithm parameters for imaging and guidance protocols. Laboratory studies included: evaluation of KC-Reg in localization of spine screws delivered in cadaver; and PLH performance in contrast, noise, and resolution in phantoms/cadaver compared to filtered backprojection (FBP). RESULTS: KC-Reg was shown to successfully register screw implants within ~1 mm based on as few as 3 radiographs. PLH was shown to improve soft-tissue visibility (61% improvement in CNR) compared to FBP at matched resolution. Cadaver studies verified the selection of algorithm parameters and the methods were successfully translated to clinical studies under an IRB protocol. CONCLUSIONS: Model-based registration and reconstruction approaches were shown to reduce dose and provide improved visualization of anatomy and surgical instrumentation. Immediate future work will focus on further integration of KC-Reg and PLH for Known-Component Reconstruction (KC-Recon) to provide high-quality intraoperative imaging in the presence of dense instrumentation.

Deletion of Aquaporin-4 Curtails Extracellular Glutamate Elevation in Cortical Spreading Depression in Awake Mice.

Cortical spreading depression (CSD) is a phenomenon that challenges the homeostatic mechanisms on which normal brain function so critically depends. Analyzing the sequence of events in CSD holds the potential of providing new insight in the physiological processes underlying normal brain function as well as the pathophysiology of neurological conditions characterized by ionic dyshomeostasis. Here, we have studied the sequential progression of CSD in awake wild-type mice and in mice lacking aquaporin-4 (AQP4) or inositol 1,4,5-triphosphate type 2 receptor (IP3R2). By the use of a novel combination of genetically encoded sensors that a novel combination - an unprecedented temporal and spatial resolution, we show that CSD leads to brisk Ca2+ signals in astrocytes and that the duration of these Ca2+ signals is shortened in the absence of AQP4 but not in the absence of IP3R2. The decrease of the astrocytic, AQP4-dependent Ca2+ signals, coincides in time and space with a decrease in the duration of extracellular glutamate overflow but not with the initial peak of the glutamate release suggesting that in CSD, extracellular glutamate accumulation is extended through AQP4-dependent glutamate release from astrocytes. The present data point to a salient glial contribution to CSD and identify AQP4 as a new target for therapy.

Congener specific determination of polychlorinated naphthalenes in sediment and biota by gas chromatography high resolution mass spectrometry.

An isotope dilution congener-specific method for the determination of the most abundant and most toxic polychlorinated naphthalenes (PCNs) was developed using gas chromatography with high resolution mass spectrometry (GC-HRMS). The method was used to determine the concentration of 24 target congeners and total PCN concentrations in fish and sediment samples. Tissue samples were extracted using pressurized liquid extraction (PLE) and sediment samples were extracted using Soxhlet extraction. Sample extracts were cleaned up using either a manual two-stage open column procedure or an automated FMS Power Prep System with multi-analyte and multi-sample capability using a three-column cleanup procedure. Sediment extracts were cleaned up with a dual open column cleanup technique involving the use of both a multi-layered silica (silver nitrate/acid/base/neutral silica) column followed by column containing carbon-activated silica. Fish tissue extracts were cleaned up on the automated system involving the use of a high capacity ABN (acid/base/neutral column), carbon celite column, and a basic alumina column. The method is capable of producing instrument detection limits (IDLs) between 0.06 and 0.13pg for each PCN (on column), with method detection limits (MDLs) for the fish extracts ranging from 1.3 to 3.4pg/g (wet weight) and 0.46 to 1.2pg/g (dry weight) for sediments. The average accuracy of 34 spiked fish samples analysed over a period of several months was 100% with a precision (%RSD) of 12%. Similarly, the average accuracy for 28 spiked sediment samples was 104% with a precision (%RSD) of 12%. The application of the method to environmental samples was demonstrated through the analysis of sediment and fish samples obtained from Lake Ontario, Canada. The method is used both for the determination of 24 PCNs and to perform non-targeted screening for the remaining 51 PCN congeners, which are included in the total PCN quantification result. It is currently one of the most comprehensive and accurate congener-specific methods available and was developed from the existing techniques used for the determination of polychlorinated dioxins and furans to produce high quality data with only minor modifications in the clean-up procedure. It can therefore be readily adopted by other laboratories performing dioxin and POP analyses.

Dynamics of Ionic Shifts in Cortical Spreading Depression.

Cortical spreading depression is a slowly propagating wave of near-complete depolarization of brain cells followed by temporary suppression of neuronal activity. Accumulating evidence indicates that cortical spreading depression underlies the migraine aura and that similar waves promote tissue damage in stroke, trauma, and hemorrhage. Cortical spreading depression is characterized by neuronal swelling, profound elevation of extracellular potassium and glutamate, multiphasic blood flow changes, and drop in tissue oxygen tension. The slow speed of the cortical spreading depression wave implies that it is mediated by diffusion of a chemical substance, yet the identity of this substance and the pathway it follows are unknown. Intercellular spread between gap junction-coupled neurons or glial cells and interstitial diffusion of K(+) or glutamate have been proposed. Here we use extracellular direct current potential recordings, K(+)-sensitive microelectrodes, and 2-photon imaging with ultrasensitive Ca(2+) and glutamate fluorescent probes to elucidate the spatiotemporal dynamics of ionic shifts associated with the propagation of cortical spreading depression in the visual cortex of adult living mice. Our data argue against intercellular spread of Ca(2+) carrying the cortical spreading depression wavefront and are in favor of interstitial K(+) diffusion, rather than glutamate diffusion, as the leading event in cortical spreading depression.

Stimulation-evoked Ca2+ signals in astrocytic processes at hippocampal CA3-CA1 synapses of adult mice are modulated by glutamate and ATP.

To date, it has been difficult to reveal physiological Ca(2+) events occurring within the fine astrocytic processes of mature animals. The objective of the study was to explore whether neuronal activity evokes astrocytic Ca(2+) signals at glutamatergic synapses of adult mice. We stimulated the Schaffer collateral/commissural fibers in acute hippocampal slices from adult mice transduced with the genetically encoded Ca(2+) indicator GCaMP5E driven by the glial fibrillary acidic protein promoter. Two-photon imaging revealed global stimulation-evoked astrocytic Ca(2+) signals with distinct latencies, rise rates, and amplitudes in fine processes and somata. Specifically, the Ca(2+) signals in the processes were faster and of higher amplitude than those in the somata. A combination of P2 purinergic and group I/II metabotropic glutamate receptor (mGluR) antagonists reduced the amplitude of the Ca(2+) transients by 30-40% in both astrocytic compartments. Blockage of the mGluRs alone only modestly reduced the magnitude of the stimulation-evoked Ca(2+) signals in processes and failed to affect the somatic Ca(2+) response. Local application of group I or I/II mGluR agonists or adenosine triphosphate (ATP) elicited global astrocytic Ca(2+) signals that mimicked the stimulation-evoked astrocytic Ca(2+) responses. We conclude that stimulation-evoked Ca(2+) signals in astrocytic processes at CA3-CA1 synapses of adult mice (1) differ from those in astrocytic somata and (2) are modulated by glutamate and ATP.

[Emergency room management of multiple trauma : ATLS® and S3 guidelines]. / Schockraummanagement Polytrauma : ATLS® und S3-Leitlinie.

Trauma management in the emergency room is an important part of the treatment chain of the severely injured. Important decisions with respect to diagnostics and treatment must be made under time pressure. Successful trauma management in the emergency room requires a hospital tailored treatment protocol. This written protocol needs consent from all participating disciplines and must be known by all members of the resuscitation team. The ATLS® and the recently published clinical practice guidelines on multiple trauma can be of help in order to establish or update such protocols. In order to continuously evaluate and improve performance in the emergency room local quality circles are needed that truly follow that aim. Important factors are reliability of agreement between the different disciplines and continuous communication of results to the team members. In order to be successful such quality circles need people that care.

The use of femto-second lasers to trigger powerful explosions of gold nanorods to destroy cancer cells.

Gold nanorods (AuNRs) with an aspect ratio of 3-4 exhibit large cross sections for single and multi photon light absorption processes in the near infrared region due to surface plasmon resonances. 800 nm laser pulses with the 150 fsec pulse duration (fs laser) can trigger explosions of AuNRs. The fs laser pulses at 20 W/mm(2) equivalent continuous wave (cw) power density blasted AuNRs in QGY human carcinoma cells as confirmed using transmission electron microscopy, while a cw laser at the same power density and dose did not. Cell survival studies further demonstrated that the cw laser at a dose of 15 J/mm(2) resulted in the death of 15% of AuNRs-loaded cells, probably due to a photothermal effect, while the fs laser at only 1.5 J/mm(2) killed more than 90% of AuNRs-loaded cells, indicating that the fs laser-triggered explosions of intracellular AuNRs are powerful enough to instantaneously kill tumour cells.

[Recombinant factor VIIa for the treatment of exsanguinating trauma patients: a matched-pair analysis from the Trauma Registry of the German Society for Trauma Surgery]. / Rekombinanter Faktor VIIa in der Hämorrhagiebehandlung des Schwerstverletzten: Eine Matched-pair-Analyse anhand des TraumaRegisters der Deutschen Gesellschaft für Unfallchirurgie.

BACKGROUND: The aim of the study was to assess whether the use of recombinant factor VIIa (rFVIIa) in trauma patients was associated with improved outcome. PATIENTS AND METHODS: Patients documented in the TraumaRegistry of the German Society for Trauma Surgery (primary admissions; Injury Severity Score, ISS ≥ 9) who received rFVIIa in the first 6 hours upon admission (rFVIIa +) were matched with patients that had not received rFVIIa (rFVIIa-). RESULTS: The matching comparison yielded two identical groups with 100 patients each (rFVIIa+: average age 40.6 ± 18.5 years, ISS 47.1 ± 16.7 versus rFVIIa-: 40.1 ± 19.1 years, ISS 45.1 ± 15.6). Patients were administered an average of 18.3 ± 13.1 (rFVIIa+) versus 19.5 ± 14.0 (rFVIIa-) red blood cell units (p = 0.55) and 15.2 ± 13.7 (rFVIIa+) versus 15.0 ± 13.1 (rFVIIa-) units of fresh frozen plasma (p = 0.92). Thromboembolisms occurred in 5% (rFVIIa+) versus 2% (rFVIIa-) (p = 0.44), multiple organ failure (MOF) in 82% versus 62% (p = 0.003) and hospital mortality was 48% versus 43% (p = 0.57), respectively. CONCLUSION: The early use of rFVIIa in severely injured patients was not associated with either lower transfusion requirements or with mortality reduction but with increased MOF.

Analysis of optical properties of the mouse cranium--implications for in vivo multi photon laser scanning microscopy.

Trans-cranial imaging is the least invasive method for optical in vivo studies of structures in the mouse brain and has found wide application over the last few years. An important issue is how and to what extent the cranium and the tissue between the cranium and the focal point detract from the quality of the recorded images. Here we address this issue by recording transmission images in wild type mice at five wavelengths in the visible and near-infrared spectrum. The recorded laser scanning microscopic images were analyzed pixel by pixel in order to quantify the light attenuation and shading as function of the location of the focal point relative to the cranium. Additional images demonstrate the effects of the mouse crania on the images of fluorescent microspheres in the low micrometer range. The results of this study demonstrate that light attenuation by the cranium, though with typical losses of less than 20% of the incident light, induces shading effects during the imaging process. Geometrical shapes and sizes in the images of the recorded objects may differ substantially depending on whether they have been recorded trans-cranially or not. This is true even for comparatively large structures such as cell somata. Our results call for a more realistic appraisal of the potential of the trans-cranial imaging approach, particularly when it comes to absolute measurements of sizes and shapes of small objects. As trans-cranial imaging has found wide use in contemporary research it is important that the results be interpreted with due caution.