Combination Of Imaging Data Research Articles

Certainty of Stroke Diagnosis: Incremental Benefit with CT Perfusion over Noncontrast CT and CT Angiography

To systematically evaluate the diagnostic benefits and inter- and intraobserver reliability of an incremental computed tomographic (CT) protocol in the confirmation of clinically suspected stroke, with combined imaging and clinical data as the reference standard. Institutional review board approval was obtained, and participants gave informed consent. A total of 191 patients (mean age, 67 years +/- 16 [standard deviation]; 105 men) with strokelike symptoms of no more than 3 hours duration were recruited. Blinded review was performed by four readers with limited stroke imaging experience. Diagnostic confidence was recorded on a five-point scale. Logistic regression analysis was used to calculate the difference between the real and observed diagnoses, adjusting for confidence. Predictive effects of observed diagnostic performance and confidence score were quantified with the entropy r(2) value. Sensitivity, specificity, and confidence intervals were calculated while accounting for multiple reader assessments. Receiver operating characteristic (ROC) analyses, including area under the ROC curve, were conducted for three modalities in combination with confidence score. Inter- and intraobserver agreement was established with the Cohen kappa statistic. The final diagnosis was infarct in 64% of the patients, transient ischemic attack in 18%, and stroke mimic in 17%. Large-vessel occlusion occurred in 70% of the patients with an infarct. Sensitivity for stroke determination with noncontrast CT, CT angiography, and CT perfusion increased by 12.4% over that with noncontrast CT and CT angiography and by 18.2% over that with only noncontrast CT for a confidence level of 4 or higher. The incremental protocol was more likely to enable confirmation of clinical stroke diagnosis (odds ratio, 13.3) than was noncontrast CT and CT angiography (odds ratio, 6.4) or noncontrast CT alone (odds ratio, 3.3), The area under the ROC curve was 0.67 for the combination of noncontrast CT and confidence score, 0.72 for the combination of CT angiography and confidence score, and 0.81 for the combination of CT perfusion and confidence score. Inter- and intraobserver agreement increased with progressive sequence use. An incremental stroke protocol that includes CT perfusion increases diagnostic performance for stroke diagnosis and inter- and intraobserver agreement.

Virtual Fly Brain: An image-linked ontology of the Drosophila Brain

Event Abstract Back to Event Virtual Fly Brain: An image-linked ontology of the Drosophila Brain David Osumi-Sutherland1*, Mark Longair2 and J. Douglas Armstrong2 1 University of Cambridge, United Kingdom 2 University of Edinburgh, School of Informatics, United Kingdom Introduction:Drosophila neuro-anatomical data is scattered across a large, diverse literature dating back over 75 years and a growing number of community databases. Lack of a standardized nomenclature for neuro-anatomy makes comparison and searching this growing data-set extremely arduous. Common criteria for classification of neurons include: which tracts their axons fasciculate in; which neuropil domains they innervate; their morphology; their function; their circuit position; what neurotransmitter they release; their lineage. Large amounts of data pertaining to these classifications exist, but have not been standardized or integrated in any queriable resource.A recent standardization effort [1] has produced a segmented, 3D model of gross Drosophila brain anatomy annotated with a controlled vocabulary. We are formalizing this to produce an image-linked ontology that will provide a substrate for defining neuronal types in according to where fasciculation and innervation patterns. Neuronal types are also classified in the ontology according to neurotransmitter released, lineage and function. The resulting ontology provides both a vocabulary for annotation and a means for integrative queries of neurobiological data. Results: General mereological relationships (part-hood, connectedness) are useful in defining relations between neuronal classes and gross anatomy, but are not sufficient to capture biologically important details (e.g.- fasciculation, innervation). We are developing relations to capture this information. For example: fasciculates_with - relation between a neuron and an axonal or dendritic bundle that its axon or dendrite is part of (implies mereological overlap [2])innervates - relation between a neuron and a structure in which it has a synapse (implies mereological overlap [2]).We are also developing methods to record neurotransmitter released (classified according to the chemical ontology, CHEBI) and function (classified according to gene ontology biological process terms) and systems for asserted classification of neurons according to morphology and circuit position. We are recording lineage using existing relations. Future Aims:In collaboration with FlyBase, the combined ontology and image data will provide the basis for an web-based, image-linked query system for Drosophila neuro-anatomical data and related genetic data. INCF-09-133-edit INCF-09-133

Validity of a closed‐form diffusion solution in approximation for reflectance imaging with an oblique beam of arbitrary profile

Determination of optical parameters of turbid media from reflectance image data is an important class of inverse problems due to its potential for noninvasive characterization of materials and biological tissues, which demands rapid modeling tools to generate calculated images. We treat the problem of reflectance imaging with homogeneous semi-infinite turbid media as a boundary-value problem of diffusion type in the P1 approximation to the radiative transfer equation. A closed-form solution has been obtained for an oblique incident beam of arbitrary profile and its accuracy has been examined against a Monte Carlo method and measured data. We find that the diffusion solution provides a sufficiently accurate tool to rapidly calculate reflectance images for samples of large or moderate scattering albedo illuminated by a beam of arbitrary profile as long as the anisotropy factor remains less than 0.7 and single scattering albedo larger than 0.8. The closed-form solution can thus be used as a part of a forward modeling toolbox to determine optical parameters from reflectance image data in combination with other method such as the Monte Carlo simulation.

Biomechanical functional and sensory modelling of the gastrointestinal tract

The aim of this review is to describe the biomechanical, functional and sensory modelling work that can be used to integrate the physiological, anatomical and medical knowledge of the gastrointestinal (GI) system. The computational modelling in the GI tract was designed, implemented and evaluated using a series of information and communication technologies-based tools. These tools modelled the morphometry, biomechanics, functions and sensory aspects of the human GI tract. The research presented in this review is based on the virtual physiological human concept that pursues a holistic approach to representation of the human body. Such computational modelling combines imaging data, GI physiology, the gut-brain axis, geometrical and biomechanical reconstruction, and computer graphics for mechanical, electronic and pain analysis. The developed modelling will aid research and ensure that medical professionals benefit through the provision of relevant and precise information about a patient's condition. It will also improve the accuracy and efficiency of the medical procedures that could result in reduced cost for diagnosis and treatment.

The Experience of Pay for Performance in English Family Practice: A Qualitative Study

We conducted an in-depth exploration of family physicians' and nurses' beliefs and concerns about changes to the family health care service as a result of the new pay-for-performance scheme in the United Kingdom (Quality and Outcomes Framework [QOF]). Using a semistructured interview format, we interviewed 21 family doctors and 20 nurses in 22 nationally representative practices across England between February and August 2007. Participants believed the financial incentives had been sufficient to change behavior and to achieve targets. The findings suggest that it is not necessary to align targets to professional priorities and values to obtain behavior change, although doing so enhances enthusiasm and understanding. Participants agreed that the aims of the pay-for-performance scheme had been met in terms of improvements in disease-specific processes of patient care and physician income, as well as improved data capture. It also led to unintended effects, such as the emergence of a dual QOF-patient agenda within consultations, potential deskilling of doctors as a result of the enhanced role for nurses in managing long-term conditions, a decline in personal/relational continuity of care between doctors and patients, resentment by team members not benefiting financially from payments, and concerns about an ongoing culture of performance monitoring in the United Kingdom. The QOF scheme may have achieved its declared objectives of improving disease-specific processes of patient care through the achievement of clinical and organizational targets and increased physician income, but our findings suggest that it has changed the dynamic between doctors and nurses and the nature of the practitioner-patient consultation.

Concurrent multimodality image segmentation by active contours for radiotherapy treatment planninga)

Multimodality imaging information is regularly used now in radiotherapy treatment planning for cancer patients. The authors are investigating methods to take advantage of all the imaging information available for joint target registration and segmentation, including multimodality images or multiple image sets from the same modality. In particular, the authors have developed variational methods based on multivalued level set deformable models for simultaneous 2D or 3D segmentation of multimodality images consisting of combinations of coregistered PET, CT, or MR data sets. The combined information is integrated to define the overall biophysical structure volume. The authors demonstrate the methods on three patient data sets, including a nonsmall cell lung cancer case with PET/CT, a cervix cancer case with PET/CT, and a prostate patient case with CT and MRI. CT, PET, and MR phantom data were also used for quantitative validation of the proposed multimodality segmentation approach. The corresponding Dice similarity coefficient (DSC) was 0.90 +/- 0.02 (p < 0.0001) with an estimated target volume error of 1.28 +/- 1.23% volume. Preliminary results indicate that concurrent multimodality segmentation methods can provide a feasible and accurate framework for combining imaging data from different modalities and are potentially useful tools for the delineation of biophysical structure volumes in radiotherapy treatment planning.

Distinct Mechanisms Mediate Visual Detection and Identification

A core organizing principle for studies of the brain is that distinct neural pathways mediate distinct behavioral tasks [1, 2]. When two related tasks are mediated by a common pathway, studies of one are likely to generalize to the other. Here, we test whether performance on two laboratory tasks that model object detection and identification are mediated by common mechanisms of visual adaptation. Although both tasks rely on the luminance pattern in images, their demands on visual processing are quite different. Object detection requires discriminating image luminance differences associated with the light reflected from adjacent objects. To encode these differences reliably, neurons adapt their limited dynamic range to prevailing viewing conditions [3-6]. Object identification, on the other hand, demands a fixed response to light reflected from an object independent of illumination [7]. We compared performance in discrimination and identification tasks for simulated surfaces. In striking contrast to studies with less structured contexts, we found clear evidence that distinct processes mediate judgments in the two tasks. These results challenge models that account for perceived lightness entirely through the action of image-encoding mechanisms.

Intracranial Aneurysms: Role of Multidetector CT Angiography in Diagnosis and Endovascular Therapy Planning

To evaluate the sensitivity of 16-detector row computed tomographic (CT) angiography in diagnosis of intracranial aneurysms and to determine whether multidetector CT angiography provides sufficient diagnostic information to guide endovascular treatment, with combined imaging and clinical data as the reference standard. Institutional review board approval and informed consent were obtained. Eighty-seven patients clinically suspected of having subarachnoid hemorrhage underwent multidetector CT angiography and digital subtraction angiography (DSA). Aneurysm detection with multidetector CT angiography and DSA was analyzed on a per-patient and a per-aneurysm basis. For each aneurysm deemed ruptured on multidetector CT angiograms, the same multidetector CT angiography data set was used to determine whether the aneurysm was suitable for endovascular coil placement or whether a neurosurgical procedure was preferable. Criteria were based on neck width in relation to aneurysm size and the presence of vessels originating from the aneurysm. Results were compared with actual treatment that had been performed in each aneurysm after full diagnostic work-up, including DSA. Sensitivity, specificity, and positive and negative predictive values for aneurysm presence were determined. The reference standard revealed 84 aneurysms in 63 patients. Multidetector CT angiography was used to correctly identify 62 of 63 patients with 80 of 84 aneurysms and to correctly rule out aneurysms in 24 patients. DSA was used to correctly identify 62 of 63 patients with 79 of 84 aneurysms and to correctly rule out aneurysms in 23 patients. Per patient, the sensitivity, specificity, and positive and negative predictive values, respectively, for presence of aneurysm(s) were 98%, 100%, 100%, and 96% for multidetector CT angiography and 98%, 100%, 98%, and 96% for DSA. Per aneurysm, the possibility of coil embolization was correctly assessed with multidetector CT angiography in 69 (93%) of 74 target aneurysms for acute occlusive treatment. Multidetector CT angiography offers high diagnostic accuracy-equivalent to that of DSA-in the detection of intracranial aneurysms. Also, the possibility of coil embolization can be reliably determined with multidetector CT angiography.

The NeuroGrid Stroke Exemplar Clinical Trial Protocol

Trials of new treatments for neurological disorders like stroke require imaging as part of the patient assessment, but need to be large enough to obtain reliable results if treatment effects are likely to be modest. However, multicentre trials use many different scanners in different hospitals and present complex problems for image data collection, interpretation and analysis and long-term secure archiving. NeuroGrid aims to develop and test grid technologies for collecting, analysing and interpreting, and secure archiving of neuroimaging data for large multicentre trials in common neurological and psychiatric disorders. A 3-year multicentre consortium of clinicians, neuroimaging centres and e-scientists are designing a Grid storage network, mechanisms for uploading, curating and retrieving image and metadata, combining image data from different scanners and an analysis tool box. Three clinical exemplars--stroke, dementia and psychosis--provide the data and 'real-world' clinical trial applications, and a set of specific and typical problems encountered with image data in multicentre trials for NeuroGrid to address. The stroke exemplar is using image data from two multicentre stroke trials: Third International Stroke Trial and Efficacy of Nitric Oxide in Stroke. The final product is intended to appear as an integrated capability consisting of services, both database and analyses, accessed through simple portals. These will include image submission, automated scan quality control, appropriate metadata linkage, streamlined image review and coding tools and long-term secure storage for future multicentre stroke trials.

The possible detection of high-redshift Type II QSOs in deep fields

The colours of high-redshift Type II quasi-stellar objects (QSOs) are synthesized from observations of moderate-redshift systems. It is shown that Type II QSOs are comparable to starbursts at matching the colours of z850-dropouts and i775-drops in the Hubble UltraDeep Field, and more naturally account for the bluest objects detected. Type II QSOs may also account for some of the i775-drops detected in the Great Observatories Origins Deep Survey (GOODS) fields. It is shown that by combining imaging data from the Hubble Space Telescope and the James Webb Space Telescope, it will be possible to clearly separate Type II QSOs from Type I QSOs and starbursts based on their colours. Similarly, it is shown that the United Kingdom Infrared Telescope (UKIRT) Infrared Deep Sky Survey (UKIDSS) ZYJ filters may be used to discriminate high-redshift Type II QSOs from other objects. If Type II QSOs are prevalent at high redshifts, then active galactic nuclei (AGNs) may be major contributors to the re-ionization of the intergalactic medium.

The Transformation of Cluster Galaxies at Intermediate Redshift

We combine imaging data from the Advanced Camera for Surveys (ACS) with VLT/FORS optical spectroscopy to study the properties of star-forming galaxies in the z = 0.837 cluster Cl 0152-1357. We have morphological information for 24 star-forming cluster galaxies, which range in morphology from late-type and irregular to compact early-type galaxies. We find that while most star-forming galaxies have r625 - i775 colors bluer than 1.0, eight are in the red cluster sequence. Among the star-forming cluster population, we find five compact early-type galaxies that have properties consistent with their identification as progenitors of dwarf elliptical galaxies. The spatial distribution of the star-forming cluster members is nonuniform. We find none within R ~ 500 Mpc of the cluster center, which is highly suggestive of an intracluster medium interaction. We derive star formation rates from [O II] λ3727 line fluxes and use these to compare the global star formation rate of Cl 0152-1357 to other clusters at low and intermediate redshifts. We find a tentative correlation between integrated star formation rates and TX, in the sense that hotter clusters have lower integrated star formation rates. Additional data from clusters with low X-ray temperatures are needed to confirm this trend. We do not find a significant correlation with redshift, suggesting that evolution is either weak or absent between z = 0.2 and 0.8.

Multimodal integration of EEG, MEG and fMRI data for the solution of the neuroimage puzzle

In this paper, advanced methods for the modeling of human cortical activity from combined high-resolution electroencephalography (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) data are presented. These methods include a subject's multicompartment head model (scalp, skull, dura mater, cortex) constructed from magnetic resonance images, multidipole source model and regularized linear inverse source estimates of cortical current density. Determination of the priors in the resolution of the linear inverse problem was performed with the use of information from the hemodynamic responses of the cortical areas as revealed by block-designed (strength of activated voxels) fMRI. Examples of the application of these methods to the estimation of the time varying cortical current density activity in selected region of interest (ROI) are presented for movement-related high-resolution EEG data.

Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function

Nowadays, several types of brain imaging device are available to provide images of the functional activity of the cerebral cortex based on hemodynamic, metabolic, or electromagnetic measurements. However, static images of brain regions activated during particular tasks do not convey the information of how these regions communicate with each other. In this study, advanced methods for the estimation of cortical connectivity from combined high-resolution electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data are presented. These methods include a subject's multicompartment head model (scalp, skull, dura mater, cortex) constructed from individual magnetic resonance images, multidipole source model, and regularized linear inverse source estimates of cortical current density. Determination of the priors in the resolution of the linear inverse problem was performed with the use of information from the hemodynamic responses of the cortical areas as revealed by block-designed (strength of activated voxels) fMRI. We estimate functional cortical connectivity by computing the directed transfer function (DTF) on the estimated cortical current density waveforms in regions of interest (ROIs) on the modeled cortical mantle. The proposed method was able to unveil the direction of the information flow between the cortical regions of interest, as it is directional in nature. Furthermore, this method allows to detect changes in the time course of information flow between cortical regions in different frequency bands. The reliability of these techniques was further demonstrated by elaboration of high-resolution EEG and fMRI signals collected during visually triggered finger movements in four healthy subjects. Connectivity patterns estimated for this task reveal an involvement of right parietal and bilateral premotor and prefrontal cortical areas. This cortical region involvement resembles that revealed in previous studies where visually triggered finger movements were analyzed with the use of separate EEG or fMRI measurements.

Estimation of the cortical connectivity during a finger-tapping movement with multimodal integration of EEG and fMRI recordings

In this paper, advanced methods for the estimation of cortical connectivity from combined high-resolution electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data are presented. We used a computational approach to the estimation of cortical connectivity by computing the Directed Transfer Function (DTF), a technique used to estimate the direction of the information flow between signals gathered from EEG sensors. The proposed method was able to depict the direction of the information flows between the cortical regions of interest, since it is directional in nature. An application of these technique to the real high-resolution EEG and fMRI signals gathered during visual finger-tapping movements in three normal healthy subjects is also provided.

Solving the “neuroimaging puzzle” with the multimodal integration of EEG and functional magnetic resonance recordings

In this paper, advanced methods for the modeling of human cortical activity from combined high-resolution electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data are reviewed. These methods include a subject's multicompartment head model (scalp, skull, dura mater, cortex) constructed from magnetic resonance images, multidipole source model, and regularized linear inverse source estimates. Determination of the priors in the resolution of the linear inverse problem was performed with the use of information from the hemodynamic responses of the cortical areas as revealed by block-designed fMRI.

Characterization of Time-Varying Properties and Regional Strains in Myocardial Ischemia

Mechanical dysfunction plays an important role in the pathological progress of myocardial ischemia and is directly related to the prognosis of the patient. The crucial index of myocardial mechanics, regional time-varying elastance, and stress, however, could not be measured directly in clinical practice. This review outlines the steps to deduct myocardial mechanical performance from medical imaging modalities and modeling. The first step is to reconstruct three-dimensional cardiac motion by combining image data and geometric model of the heart. SPAMM-MRI (spatial modulation of magnetization) image, because of its high spatial–temporal resolution and ability to track material point of moving myocardium, is an ideal image modality for such a task. Several geometry models focused on SPAMM-MRI data analysis are discussed in detail, including some that assume cardiac shapes and some that extract the motion information from the tagging and subsequent model building by interpolation. Once the geometry of the heart at different time point is established, the second step is to calculate motion index from it. While different indices have been proposed, such as endocardium excursion, heart wall thickening, cardiac torsion, regional myocardial strain has the advantage of less affected by global heart motion and neighborhood myocardium. It is a key index to bridge cardiac MRI image and regional myocardial mechanical model. Finally, the three-dimensional strain distribution is used to compute myocardium stress and marterial property. Animal experiment, however, has shown that myocardial elastance changes throughout the cardiac cycle. The use of time-varying elastance model instead of elastic solid continuum model can thus provide a more accurate description of regional myocardium mechanics.

Combination of signals from array coils using image-based estimation of coil sensitivity profiles.

It is well established that the optimal unbiased way to combine image data from array coils is a pixel-by-pixel sum of coil signals, with each signal weighted by the individual coil sensitivity at the location of the pixel. A pragmatic alternative combines the images from the coils as the square root of the sum of squares (SOS), which can reduce the signal-to-noise ratio (SNR) and introduce bias. This work describes how to replace coil sensitivity by an image-derived quantity that enables close to optimal signal combination up to a global intensity scaling. Typical scaling is by an individual coil sensitivity or a linear or SOS combination of the sensitivities of some or all of the coils in the array. The method decreases signal bias, improves SNR when coils have unequal noise levels, and can reduce image artifacts. It can produce phase-corrected data, which eliminates bias completely. In addition, the method allows images from arrays that include highly localized coils, such as a prostate coil and external pelvic array, to be combined with near-optimal SNR and an intensity modulation that makes them easier to view.

Anatomy of the patent ductus venosus in the cat

The biplanar mesenteric vein portovenograms of 10 cats with left divisional intrahepatic portosystemic shunts consistent with a patent ductus venosus (PDV) were reviewed. A corrosion cast of the hepatic portal vasculature was made post mortem from one individual that died post operatively following surgical attenuation of the shunting vessel. On the basis of the combined surgical, post mortem and imaging data, these left divisional shunts were found to have consistent anatomy, each having a straight vessel which drained into a venous ampulla before draining into the caudal vena cava at the level of the diaphragm. The left phrenic vein and left hepatic vein both entered the ampulla independently of the shunting vessel. The anatomical similarity between these findings in the cat and the PDV in the dog suggest that it is appropriate to describe this particular portosystemic shunt as a PDV.

FMRI Priors for the Linear Inverse Estimation of EEG Cortical Sources

In this paper, advanced methods for the modeling of human cortical activity from combined high-resolution electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data are reviewed. These methods include a subject's multicompartment head model (scalp, skull, dura mater, cortex) constructed from magnetic resonance images, multidipole source model, and regularized linear inverse source estimate based on boundary element mathematics. Furthermore, determination of the priors in the resolution of the linear inverse problem was performed with the use of information from the hemodynamic responses of the cortical areas as revealed by block-designed (strength of activated voxels) and event-related (coupling of activated voxels) fMRI. Linear inverse source estimates of cortical activity were regularized by taking into account the covariance of background EEG sensor noise. As an example, these methods were applied to EEG (128 electrodes) and fMRI data, which were recorded in separate sessions while normal subjects executed voluntary right one-digit movements.

Anatomy of the patent ductus venosus in the dog

The biplanar umbilical vein portovenograms of 49 newborn puppies and the biplanar mesenteric vein portovenograms, obtained during surgery, of 42 adult dogs with left divisional intrahepatic portosystemic shunts consistent with...