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.

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.

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.

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.

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.

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.

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.

Temporal and spatial variabilities of atmospheric polychlorinated biphenyls (PCBs), organochlorine (OC) pesticides and polycyclic aromatic hydrocarbons (PAHs) in the Canadian Arctic: results from a decade of monitoring.

The Northern Contaminants Program (NCP) baseline monitoring project was established in 1992 to monitor for persistent organic pollutants (POPs) in Arctic air. Under this project, weekly samples of air were collected at four Canadian and two Russian arctic sites, namely Alert, Nunavut; Tagish, Yukon; Little Fox Lake, Yukon; Kinngait, Nunavut; Dunai Island, Russia and Amderma, Russia. Selected POPs, including polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and organochlorine (OC) pesticides, were analyzed in both the gas and particulate phases. This paper summarizes results obtained from this project in the past 5 years. Temporal trends were developed for atmospheric PCBs and OCs observed at Alert using a digital filtration (DF) technique. It was found that trends developed with 5 years of data (1993-1997) did not differ significantly from those determined with 7 years of data (1993-1999). This implies that with the DF technique, long-term trends can still be developed with less than 10 years of data. An acceleration in decline of OC and PCB air concentrations was noted in 1999 for some compounds, although the reason is unknown. Monitoring efforts must continue to assess the effect of this decline on the long-term trends of POPs in the Canadian Arctic. Occasional high trans-/cis-chlordane ratios and heptachlor air concentrations measured at Alert between 1995 and 1997 suggests sporadic fresh usage of chlordane-based pesticides. However, significant decreasing trends of chlordanes along with their chemical signatures has provided evidence that emission of old soil residues is replacing new usage as an important source to the atmosphere. Measurements of OC air concentrations conducted at Kinngait in 1994-1995 and 2000-2001 indicated faster OC removal at this location than at Alert. This may be attributed to the proximity of Kinngait to temperate regions where both biotic and abiotic degradation rates are faster. The PAH concentrations observed at Alert mimic those at mid-latitudes and are consistent with long-range transport to the Arctic, particularly for the lighter PAHs. A decline in particulate PAH was observed, similar to atmospheric sulphate aerosol and can be attributed to the collapse of industrial activity in the former Soviet Union between 1991 and 1995. Spatial comparisons of OC seasonality at Alert, Tagish, Dunai and Kinngait show elevated air concentrations of some compounds in spring. However, elevated spring concentrations were observed for different compounds at different sites. Potential causes are discussed. Further investigation in the atmospheric flow pattern in spring which is responsible for the transport of POPs into the Arctic is required. OC and PCB air concentrations at Alert were found to be influenced by two climate variation patterns, the North Atlantic Oscillation (NAO) and the Pacific North American (PNA) pattern. Planetary atmospheric patterns must be taken into account in the global prediction and modelling of POPs in the future.

Modern and historical fluxes of halogenated organic contaminants to a lake in the Canadian arctic, as determined from annually laminated sediment cores.

Two annually laminated cores collected from Lake DV09 on Devon Island in May 1999 were dated using 210Pb and 137Cs, and analyzed for a variety of halogenated organic contaminants (HOCs), including polychlorinated biphenyls (PCBs), organochlorine pesticides, short-chain polychlorinated n-alkanes (sPCAs), polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs), and polybrominated diphenyl ethers (PBDEs). Dry weight HOC concentrations in Lake DV09 sediments were generally similar to other remote Arctic lakes. Maximum HOC fluxes often agreed well with production maxima, although many compound groups exhibited maxima at or near the sediment surface, much later than peak production. The lower than expected HOC concentrations in older sediment slices may be due to anaerobic degradation and possibly to dilution resulting from a temporary increase in sedimentation rate observed between the mid-1960s and 1970s. Indeed, temporal trends were more readily apparent for those compound classes when anaerobic metabolites were also analyzed, such as for DDT and toxaphene. However, it is postulated here for the first time that the maximum or increasing HOC surface fluxes observed for many of the major compound classes in DV09 sediments may be influenced by climate variation and the resulting increase in algal primary productivity which could drive an increasing rate of HOC scavenging from the water column. Both the fraction (F(TC)) and enantiomer fraction (EF) of trans-chlordane (TC) decreased significantly between 1957 and 1997, suggesting that recent inputs to the lake are from weathered chlordane sources. PCDD/Fs showed a change in sources from pentachlorophenol (PeCP) in the 1950s and 1960s to combustion sources into the 1990s. Improvements in combustion technology may be responsible for the reducing the proportion of TCDF relative to OCDD in the most recent slice.

Persistent organic pollutants and mercury in marine biota of the Canadian Arctic: an overview of spatial and temporal trends.

This review summarizes and synthesizes the significant amount of data which was generated on mercury (Hg) and persistent organic pollutants (POPs) in Canadian Arctic marine biota since the first Canadian Arctic Contaminants Assessment Report (CACAR) was published in 1997. This recent body of work has led to a better understanding of the current levels and spatial and temporal trends of contaminants in biota, including the marine food species that northern peoples traditionally consume. Compared to other circumpolar countries, concentrations of many organochlorines (OCs) in Canadian Arctic marine biota are generally lower than in the European Arctic and eastern Greenland but are higher than in Alaska, whereas Hg concentrations are substantially higher in Canada than elsewhere. Spatial coverage of OCs in ringed seals, beluga and seabirds remains a strength of the Arctic contaminant data set for Canada. Concentrations of OCs in marine mammals and seabirds remain fairly consistent across the Canadian Arctic although subtle differences from west to east and south to north are found in the proportions of various chemicals. The most significant development since 1997 is improvement in the temporal trend data sets, thanks to the use of archived tissue samples from the 1970s and 1980s, long-term studies using archeological material, as well as the continuation of sampling. These data cover a range of species and chemicals and also include retrospective studies on new chemicals such as polybrominated diphenyl ethers. There is solid evidence in a few species (beluga, polar bear, blue mussels) that Hg at some locations has significantly increased from pre-industrial times to the present; however, the temporal trends of Hg over the past 20-30 years are inconsistent. Some animal populations exhibited significant increases in Hg whereas others did not. Therefore, it is currently not possible to determine if anthropogenic Hg is generally increasing in Canadian Arctic biota. It is also not yet possible to evaluate whether the recent Hg increases observed in some biota may be due solely to increased anthropogenic inputs or are in part the product of environmental change, e.g., climate warming. Concentrations of most "legacy" OCs (PCBs, DDT, etc.) significantly declined in Canadian Arctic biota from the 1970s to the late 1990s, and today are generally less than half the levels of the 1970s, particularly in seabirds and ringed seals. Chlorobenzenes and endosulfan were among the few OCs to show increases during this period while summation operatorHCH remained relatively constant in most species. A suite of new-use chemicals previously unreported in Arctic biota (e.g., polybrominated diphenyl ethers (PBDEs), short chain chlorinated paraffins (SCCPs), polychlorinated naphthalenes (PCNs), perfluoro-octane sulfonic acid (PFOS) and perfluorocarboxylic acids (PFCAs)) has recently been found, but there is insufficient information to assess species differences, spatial patterns or food web dynamics for these compounds. Concentrations of these new chemicals are generally lower than legacy OCs, but there is concern because some are rapidly increasing in concentration (e.g., PBDEs), while others such as PFOS have unique toxicological properties, and some were not expected to be found in the Arctic because of their supposedly low potential for long-range transport. Continuing temporal monitoring of POPs and Hg in a variety of marine biota must be a priority.

Simplicity versus likelihood in visual perception: from surprisals to precisals.

The likelihood principle states that the visual system prefers the most likely interpretation of a stimulus, whereas the simplicity principle states that it prefers the most simple interpretation. This study investigates how close these seemingly very different principles are by combining findings from classical, algorithmic, and structural information theory. It is argued that, in visual perception, the two principles are perhaps very different with respect to the viewpoint-independent aspects of perception but probably very close with respect to the viewpoint-dependent aspects which, moreover, seem decisive in everyday perception. This implies that either principle may have guided the evolution of visual systems and that the simplicity paradigm may provide perception models with the necessary quantitative specifications of the often plausible but also intuitive ideas provided by the likelihood paradigm.

Goodness of visual regularities: a nontransformational approach.

Until recently, the transformational approach provided the only available formal analysis of visual regularities like repetition and mirror symmetry. This theoretical study presents a new analysis, based on the recently developed concept of holographic regularity. This concept applies to the intrinsic character of regularity and specifies the unique formal status of perceptually relevant regularities. The crucial point is that the two analyses imply the same structure for repetition but a different structure for mirror symmetry. Transformationally, mirror symmetry is an all-or-nothing property, whereas holographically, it is a graded property. This difference pervades the understanding of both perfect regularities and perturbed regularities. Whereas the transformational approach explains hardly any goodness phenomenon, the holographic approach explains a wide variety of goodness phenomena in a coherent way that is ecologically plausible as well.

Competing global and local completions in visual occlusion.

In visual occlusion 2 amodal-completion tendencies occur frequently. One tendency leads toward the simplest completed shape (a global completion) and the other to a shape for which the completion itself is as simple as possible (a local completion). Two experimental paradigms were used to test the strengths of these completion tendencies: a drawing task and a simultaneous matching task. The experimental results support the notion that the preference for either a global or a local completion is the consequence of a competition between interpretations. Finally, the authors discuss how the preference for a completion can be predicted by a model that is based on a quantification of both global and local aspects.

Burn rehabilitation-a team approach.

The team concept in the treatment of burned patients is an effective approach in caring for the physical, psychological, and social needs of the patient. Through the initiation of early rehabilitation services, long-term problems can be prevented and a quicker return to a meaningful life style is possible.

Burn rehabilitation: dimensions of the problem.

Recent improvements in the mortality rates of burned patients are attributed to the expansion of specialized burn centers. Satellite treatment areas with trained personnel need to be developed, located away from the burn center, and efforts should be made to increase the number of patient programs at existing burn centers. In addition, studies need to be conducted on cost consequences, hypertrophic scarring, peripheral and central nervous system involvement, musculoskeletal changes, sleep deprivation, and the comparison of various treatment techniques to determine what protocols provide the best treatment outcome.