Patient Access to Health Data: A Review of Philosophic and Healthcare Issues

In 2012 Australia created a national Personal Controlled Electronic Health Record (PCEHR) known as "My Health Record" (MHR). However, MHR has seen low patient utilization. Debate regarding MHR has centered on utility and moral issues (e.g. data privacy). We conducted a narrative review to assess patient perception and clinical utility of PCEHRs worldwide. Results show patient and clinician support for PCEHRs but little evidence of improved outcomes and patient concerns regarding data providence.

Designing an Interprofessional Online Course to Foster Learning Health Systems

The Learning Health Systems (LHS) framework demonstrates the potential for iterative interrogation of health data in real time and implementation of insights into practice. Yet, the lack of appropriately skilled workforce results in an inability to leverage existing data to design innovative solutions. We developed a tailored professional development program to foster a skilled workforce. The short course is wholly online, for interdisciplinary professionals working in the digital health arena. To transform healthcare systems, the workforce needs an understanding of LHS principles, data driven approaches, and the need for diversly skilled learning communities that can tackle these complex problems together.

A Narrative Review of Sociodemographic Disparities in Relation to PEHR Access

Personal electronic health records (PEHRs) enable patients access to their own medical records. Differences in access and use of PEHRs may create health disparities. We conducted a narrative literature review regarding the effects of race, language preference, education, income, and homelessness on PEHR usage as well as PEHRs content, particularly stigmatizing language. Of 3177 citations found, 75 articles were relevant. Patient race, language, income, and education predicted PEHR use, which could potentially exacerbate health disparities.

Analyzing the Spread of Informatics with PubMed

We analyzed PubMed citations since 1988 to explore the dissemination of medical/health informatics concepts between countries and across medical domains. We extracted countries from the PubMed author affiliation field to identify and analyze the top 10 informatics publishing countries. We found that the informatics publications are becoming more similar over time and that the rate of exchange across countries has increased with the introduction of e-publishing. Nonetheless, with the exception of machine learning, the impact of core informatics concepts on mainstream medicine and radiology publications remains small.

WITHDRAWN: Designing a professional development online short course to foster Learning Healthcare Systems

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). This article has been withdrawn at the request of the editor and publisher. The publisher regrets that an error occurred which led to the premature publication of this paper. This error bears no reflection on the article or its authors. The publisher apologizes to the authors and the readers for this unfortunate error.

Multi-energy reconstructions, central electron temperature measurements, and early detection of the birth and growth of runaway electrons using a versatile soft x-ray pinhole camera at MST

A multi-energy soft x-ray pinhole camera has been designed, built, and deployed at the Madison Symmetric Torus to aid the study of particle and thermal transport, as well as MHD stability physics. This novel imaging diagnostic technique employs a pixelated x-ray detector in which the lower energy threshold for photon detection can be adjusted independently on each pixel. The detector of choice is a PILATUS3 100 K with a 450 μm thick silicon sensor and nearly 100 000 pixels sensitive to photon energies between 1.6 and 30 keV. An ensemble of cubic spline smoothing functions has been applied to the line-integrated data for each time-frame and energy-range, obtaining a reduced standard-deviation when compared to that dominated by photon-noise. The multi-energy local emissivity profiles are obtained from a 1D matrix-based Abel-inversion procedure. Central values of T_e can be obtained by modeling the slope of the continuum radiation from ratios of the inverted radial emissivity profiles over multiple energy ranges with no a priori assumptions of plasma profiles, magnetic field reconstruction constraints, high-density limitations, or need of shot-to-shot reproducibility. In tokamak plasmas, a novel application has recently been tested for early detection, 1D imaging, and study of the birth, exponential growth, and saturation of runaway electrons at energies comparable to 100 × T_e,0; thus, early results are also presented.

The International Neuromodulation Registry: An Informatics Framework Supporting Cohort Discovery and Analysis

Background

Neuromodulation therapies, such as deep brain stimulation (DBS), spinal cord stimulation (SCS), responsive neurostimulation (RNS), transcranial magnetic stimulation (TMS), transcranial direct stimulation (tDCS), and vagus nerve stimulation (VNS) are used to treat neurological and psychiatric conditions for patients who have failed to benefit from other treatment approaches. Although generally effective, seemingly similar cases often have very different levels of effectiveness. While there is ongoing interest in developing predictors, it can be difficult to aggregate the necessary data from limited cohorts of patients at individual treatment centers.

Objective

In order to increase the predictive power in neuromodulation studies, we created an informatics platform called the International Neuromodulation Registry (INR). The INR platform has a data flow process that will allow researchers to pool data across multiple centers to enable population health research.

Methods

This custom informatics platform has a Neo4j graph database and includes a harmonization process that allows data from different studies to be aggregated and compared. Users of the INR can download deidentified patient imaging, patient demographic data, device settings, and medical rating scales. The INR supports complex network analysis and patient timeline visualization.

Results

The INR currently houses and allows visualization of deidentified imaging and clinical data from hundreds of patients with a wide range of diagnoses and neuromodulation therapies.

Conclusion

Ultimately, we believe that widespread adoption of the INR platform will improve population health research in neuromodulation therapy.

Compartment and hub definitions tune metabolic networks for metabolomic interpretations

BACKGROUND

Metabolic networks represent all chemical reactions that occur between molecular metabolites in an organism's cells. They offer biological context in which to integrate, analyze, and interpret omic measurements, but their large scale and extensive connectivity present unique challenges. While it is practical to simplify these networks by placing constraints on compartments and hubs, it is unclear how these simplifications alter the structure of metabolic networks and the interpretation of metabolomic experiments.

RESULTS

We curated and adapted the latest systemic model of human metabolism and developed customizable tools to define metabolic networks with and without compartmentalization in subcellular organelles and with or without inclusion of prolific metabolite hubs. Compartmentalization made networks larger, less dense, and more modular, whereas hubs made networks larger, more dense, and less modular. When present, these hubs also dominated shortest paths in the network, yet their exclusion exposed the subtler prominence of other metabolites that are typically more relevant to metabolomic experiments. We applied the non-compartmental network without metabolite hubs in a retrospective, exploratory analysis of metabolomic measurements from 5 studies on human tissues. Network clusters identified individual reactions that might experience differential regulation between experimental conditions, several of which were not apparent in the original publications.

CONCLUSIONS

Exclusion of specific metabolite hubs exposes modularity in both compartmental and non-compartmental metabolic networks, improving detection of relevant clusters in omic measurements. Better computational detection of metabolic network clusters in large data sets has potential to identify differential regulation of individual genes, transcripts, and proteins.

Feasibility of Natural Language Processing-Assisted Auditing of Critical Findings in Chest Radiology

OBJECTIVE

Time-sensitive communication of critical imaging findings like pneumothorax or pulmonary embolism to referring physicians is essential for patient safety. The definitive communication is the radiology free-text report. Quality assurance initiatives require that institutions audit these communications, a time-intensive manual task. We propose using a rule-based natural language processing system to improve the process for auditing critical findings communications.

METHODS

We present a pilot assessment of the feasibility of using an automated critical finding identification system to assist quality assurance teams' evaluation of critical findings communication compliance. Our assessment is based on chest imaging reports. Critical findings are identified in radiology reports using pyConTextNLP, an open source Python implementation of the ConText algorithm.

RESULTS

In our test set, there were 75 reports with critical findings and 591 reports without critical findings. pyConTextNLP correctly identified 69 of the positive cases with 8 false-positives for a sensitivity of 0.92 and a specificity of 0.99.

DISCUSSION

Natural language processing can provide valuable assistance to auditing critical findings communications.

Dependence of Perpendicular Viscosity on Magnetic Fluctuations in a Stochastic Topology

In a magnetically confined plasma with a stochastic magnetic field, the dependence of the perpendicular viscosity on the magnetic fluctuation amplitude is measured for the first time. With a controlled, ∼ tenfold variation in the fluctuation amplitude, the viscosity increases ∼100-fold, exhibiting the same fluctuation-amplitude-squared dependence as the predicted rate of stochastic field line diffusion. The absolute value of the viscosity is well predicted by a model based on momentum transport in a stochastic field, the first in-depth test of this model.

Deep Learning to Classify Radiology Free-Text Reports

Purpose To evaluate the performance of a deep learning convolutional neural network (CNN) model compared with a traditional natural language processing (NLP) model in extracting pulmonary embolism (PE) findings from thoracic computed tomography (CT) reports from two institutions. Materials and Methods Contrast material-enhanced CT examinations of the chest performed between January 1, 1998, and January 1, 2016, were selected. Annotations by two human radiologists were made for three categories: the presence, chronicity, and location of PE. Classification of performance of a CNN model with an unsupervised learning algorithm for obtaining vector representations of words was compared with the open-source application PeFinder. Sensitivity, specificity, accuracy, and F1 scores for both the CNN model and PeFinder in the internal and external validation sets were determined. Results The CNN model demonstrated an accuracy of 99% and an area under the curve value of 0.97. For internal validation report data, the CNN model had a statistically significant larger F1 score (0.938) than did PeFinder (0.867) when classifying findings as either PE positive or PE negative, but no significant difference in sensitivity, specificity, or accuracy was found. For external validation report data, no statistical difference between the performance of the CNN model and PeFinder was found. Conclusion A deep learning CNN model can classify radiology free-text reports with accuracy equivalent to or beyond that of an existing traditional NLP model. ^© RSNA, 2017 Online supplemental material is available for this article.

Extracting a stroke phenotype risk factor from Veteran Health Administration clinical reports: an information content analysis

BACKGROUND

In the United States, 795,000 people suffer strokes each year; 10-15 % of these strokes can be attributed to stenosis caused by plaque in the carotid artery, a major stroke phenotype risk factor. Studies comparing treatments for the management of asymptomatic carotid stenosis are challenging for at least two reasons: 1) administrative billing codes (i.e., Current Procedural Terminology (CPT) codes) that identify carotid images do not denote which neurovascular arteries are affected and 2) the majority of the image reports are negative for carotid stenosis. Studies that rely on manual chart abstraction can be labor-intensive, expensive, and time-consuming. Natural Language Processing (NLP) can expedite the process of manual chart abstraction by automatically filtering reports with no/insignificant carotid stenosis findings and flagging reports with significant carotid stenosis findings; thus, potentially reducing effort, costs, and time.

METHODS

In this pilot study, we conducted an information content analysis of carotid stenosis mentions in terms of their report location (Sections), report formats (structures) and linguistic descriptions (expressions) from Veteran Health Administration free-text reports. We assessed an NLP algorithm, pyConText's, ability to discern reports with significant carotid stenosis findings from reports with no/insignificant carotid stenosis findings given these three document composition factors for two report types: radiology (RAD) and text integration utility (TIU) notes.

RESULTS

We observed that most carotid mentions are recorded in prose using categorical expressions, within the Findings and Impression sections for RAD reports and within neither of these designated sections for TIU notes. For RAD reports, pyConText performed with high sensitivity (88 %), specificity (84 %), and negative predictive value (95 %) and reasonable positive predictive value (70 %). For TIU notes, pyConText performed with high specificity (87 %) and negative predictive value (92 %), reasonable sensitivity (73 %), and moderate positive predictive value (58 %). pyConText performed with the highest sensitivity processing the full report rather than the Findings or Impressions independently.

CONCLUSION

We conclude that pyConText can reduce chart review efforts by filtering reports with no/insignificant carotid stenosis findings and flagging reports with significant carotid stenosis findings from the Veteran Health Administration electronic health record, and hence has utility for expediting a comparative effectiveness study of treatment strategies for stroke prevention.

Automated generation of directed graphs from vascular segmentations

Automated feature extraction from medical images is an important task in imaging informatics. We describe a graph-based technique for automatically identifying vascular substructures within a vascular tree segmentation. We illustrate our technique using vascular segmentations from computed tomography pulmonary angiography images. The segmentations were acquired in a semi-automated fashion using existing segmentation tools. A 3D parallel thinning algorithm was used to generate the vascular skeleton and then graph-based techniques were used to transform the skeleton to a directed graph with bifurcations and endpoints as nodes in the graph. Machine-learning classifiers were used to automatically prune false vascular structures from the directed graph. Semantic labeling of portions of the graph with pulmonary anatomy (pulmonary trunk and left and right pulmonary arteries) was achieved with high accuracy (percent correct⩾0.97). Least-squares cubic splines of the centerline paths between nodes were computed and were used to extract morphological features of the vascular tree. The graphs were used to automatically obtain diameter measurements that had high correlation (r⩾0.77) with manual measurements made from the same arteries.

Cue-based assertion classification for Swedish clinical text--developing a lexicon for pyConTextSwe

OBJECTIVE

The ability of a cue-based system to accurately assert whether a disorder is affirmed, negated, or uncertain is dependent, in part, on its cue lexicon. In this paper, we continue our study of porting an assertion system (pyConTextNLP) from English to Swedish (pyConTextSwe) by creating an optimized assertion lexicon for clinical Swedish.

METHODS AND MATERIAL

We integrated cues from four external lexicons, along with generated inflections and combinations. We used subsets of a clinical corpus in Swedish. We applied four assertion classes (definite existence, probable existence, probable negated existence and definite negated existence) and two binary classes (existence yes/no and uncertainty yes/no) to pyConTextSwe. We compared pyConTextSwe's performance with and without the added cues on a development set, and improved the lexicon further after an error analysis. On a separate evaluation set, we calculated the system's final performance.

RESULTS

Following integration steps, we added 454 cues to pyConTextSwe. The optimized lexicon developed after an error analysis resulted in statistically significant improvements on the development set (83% F-score, overall). The system's final F-scores on an evaluation set were 81% (overall). For the individual assertion classes, F-score results were 88% (definite existence), 81% (probable existence), 55% (probable negated existence), and 63% (definite negated existence). For the binary classifications existence yes/no and uncertainty yes/no, final system performance was 97%/87% and 78%/86% F-score, respectively.

CONCLUSIONS

We have successfully ported pyConTextNLP to Swedish (pyConTextSwe). We have created an extensive and useful assertion lexicon for Swedish clinical text, which could form a valuable resource for similar studies, and which is publicly available.

Kinetic stress and intrinsic flow in a toroidal plasma

A new mechanism for intrinsic plasma flow has been experimentally identified in a toroidal plasma. For reversed field pinch plasmas with a few percent β (ratio of plasma pressure to magnetic pressure), measurements show that parallel pressure fluctuations correlated with magnetic fluctuations create a kinetic stress that can affect momentum balance and the evolution of intrinsic plasma flow. This implies kinetic effects are important for flow generation and sustainment.

Extending the NegEx lexicon for multiple languages

We translated an existing English negation lexicon (NegEx) to Swedish, French, and German and compared the lexicon on corpora from each language. We observed Zipf's law for all languages, i.e., a few phrases occur a large number of times, and a large number of phrases occur fewer times. Negation triggers "no" and "not" were common for all languages; however, other triggers varied considerably. The lexicon is available in OWL and RDF format and can be extended to other languages. We discuss the challenges in translating negation triggers to other languages and issues in representing multilingual lexical knowledge.

A semi-automated quantification of pulmonary artery dimensions in computed tomography angiography images

Quantifying vascular dimensions may provide a non-invasive means of diagnosing a variety of vascular diseases, including pulmonary hypertension, a progressive, potentially fatal disease that results in the remodeling of the pulmonary vasculature. Currently the gold standard for diagnosis of pulmonary hypertension is through right heart catheterization, an invasive and costly procedure. Since pulmonary hypertension is associated with the remodeling of the pulmonary arteries, quantifying vascular geometry as depicted in tomographic imaging may provide a non-invasive diagnostic technique. In this work we explore a semi-automated method for quantifying pulmonary vascular geometry with the intention of using such measurements in the future for diagnosing pulmonary hypertension.

Role of nonlinear coupling and density fluctuations in magnetic-fluctuation-induced particle transport

Three-wave nonlinear coupling among spatial Fourier modes of density and magnetic fluctuations is directly measured in a magnetically confined toroidal plasma. Density fluctuations are observed to gain (lose) energy from (to) either equilibrium or fluctuating fields depending on the mode number. Experiments indicate that nonlinear interactions alter the phase relation between density and magnetic fluctuations, leading to strong particle transport.

Classical impurity ion confinement in a toroidal magnetized fusion plasma

High-resolution measurements of impurity ion dynamics provide first-time evidence of classical ion confinement in a toroidal, magnetically confined plasma. The density profile evolution of fully stripped carbon is measured in MST reversed-field pinch plasmas with reduced magnetic turbulence to assess Coulomb-collisional transport without the neoclassical enhancement from particle drift effects. The impurity density profile evolves to a hollow shape, consistent with the temperature screening mechanism of classical transport. Corroborating methane pellet injection experiments expose the sensitivity of the impurity particle confinement time to the residual magnetic fluctuation amplitude.

iDASH: integrating data for analysis, anonymization, and sharing

iDASH (integrating data for analysis, anonymization, and sharing) is the newest National Center for Biomedical Computing funded by the NIH. It focuses on algorithms and tools for sharing data in a privacy-preserving manner. Foundational privacy technology research performed within iDASH is coupled with innovative engineering for collaborative tool development and data-sharing capabilities in a private Health Insurance Portability and Accountability Act (HIPAA)-certified cloud. Driving Biological Projects, which span different biological levels (from molecules to individuals to populations) and focus on various health conditions, help guide research and development within this Center. Furthermore, training and dissemination efforts connect the Center with its stakeholders and educate data owners and data consumers on how to share and use clinical and biological data. Through these various mechanisms, iDASH implements its goal of providing biomedical and behavioral researchers with access to data, software, and a high-performance computing environment, thus enabling them to generate and test new hypotheses.

Bifurcation to 3D helical magnetic equilibrium in an axisymmetric toroidal device

We report the first direct measurement of the internal magnetic field structure associated with a 3D helical equilibrium generated spontaneously in the core of an axisymmetric toroidal plasma containment device. Magnetohydrodynamic equilibrium bifurcation occurs in a reversed-field pinch when the innermost resonant magnetic perturbation grows to a large amplitude, reaching up to 8% of the mean field strength. Magnetic topology evolution is determined by measuring the Faraday effect, revealing that, as the perturbation grows, toroidal symmetry is broken and a helical equilibrium is established.

Anisotropic ion heating and tail generation during tearing mode magnetic reconnection in a high-temperature plasma

Complementary measurements of ion energy distributions in a magnetically confined high-temperature plasma show that magnetic reconnection results in both anisotropic ion heating and the generation of suprathermal ions. The anisotropy, observed in the C(+6) impurity ions, is such that the temperature perpendicular to the magnetic field is larger than the temperature parallel to the magnetic field. The suprathermal tail appears in the majority ion distribution and is well described by a power law to energies 10 times the thermal energy. These observations may offer insight into the energization process.

Document-level classification of CT pulmonary angiography reports based on an extension of the ConText algorithm

In this paper we describe an application called peFinder for document-level classification of CT pulmonary angiography reports. peFinder is based on a generalized version of the ConText algorithm, a simple text processing algorithm for identifying features in clinical report documents. peFinder was used to answer questions about the disease state (pulmonary emboli present or absent), the certainty state of the diagnosis (uncertainty present or absent), the temporal state of an identified pulmonary embolus (acute or chronic), and the technical quality state of the exam (diagnostic or not diagnostic). Gold standard answers for each question were determined from the consensus classifications of three human annotators. peFinder results were compared to naive Bayes' classifiers using unigrams and bigrams. The sensitivities (and positive predictive values) for peFinder were 0.98(0.83), 0.86(0.96), 0.94(0.93), and 0.60(0.90) for disease state, quality state, certainty state, and temporal state respectively, compared to 0.68(0.77), 0.67(0.87), 0.62(0.82), and 0.04(0.25) for the naive Bayes' classifier using unigrams, and 0.75(0.79), 0.52(0.69), 0.59(0.84), and 0.04(0.25) for the naive Bayes' classifier using bigrams.

Automated ancillary cancer history classification for mesothelioma patients from free-text clinical reports

BACKGROUND

Clinical records are often unstructured, free-text documents that create information extraction challenges and costs. Healthcare delivery and research organizations, such as the National Mesothelioma Virtual Bank, require the aggregation of both structured and unstructured data types. Natural language processing offers techniques for automatically extracting information from unstructured, free-text documents.

METHODS

Five hundred and eight history and physical reports from mesothelioma patients were split into development (208) and test sets (300). A reference standard was developed and each report was annotated by experts with regard to the patient's personal history of ancillary cancer and family history of any cancer. The Hx application was developed to process reports, extract relevant features, perform reference resolution and classify them with regard to cancer history. Two methods, Dynamic-Window and ConText, for extracting information were evaluated. Hx's classification responses using each of the two methods were measured against the reference standard. The average Cohen's weighted kappa served as the human benchmark in evaluating the system.

RESULTS

Hx had a high overall accuracy, with each method, scoring 96.2%. F-measures using the Dynamic-Window and ConText methods were 91.8% and 91.6%, which were comparable to the human benchmark of 92.8%. For the personal history classification, Dynamic-Window scored highest with 89.2% and for the family history classification, ConText scored highest with 97.6%, in which both methods were comparable to the human benchmark of 88.3% and 97.2%, respectively.

CONCLUSION

We evaluated an automated application's performance in classifying a mesothelioma patient's personal and family history of cancer from clinical reports. To do so, the Hx application must process reports, identify cancer concepts, distinguish the known mesothelioma from ancillary cancers, recognize negation, perform reference resolution and determine the experiencer. Results indicated that both information extraction methods tested were dependant on the domain-specific lexicon and negation extraction. We showed that the more general method, ConText, performed as well as our task-specific method. Although Dynamic- Window could be modified to retrieve other concepts, ConText is more robust and performs better on inconclusive concepts. Hx could greatly improve and expedite the process of extracting data from free-text, clinical records for a variety of research or healthcare delivery organizations.

A multistage approach to improve performance of computer-aided detection of pulmonary embolisms depicted on CT images: preliminary investigation

This study developed a computer-aided detection (CAD) scheme for pulmonary embolism (PE) detection and investigated several approaches to improve CAD performance. In the study, 20 computed tomography examinations with various lung diseases were selected, which include 44 verified PE lesions. The proposed CAD scheme consists of five basic steps: 1) lung segmentation; 2) PE candidate extraction using an intensity mask and tobogganing region growing; 3) PE candidate feature extraction; 4) false-positive (FP) reduction using an artificial neural network (ANN); and 5) a multifeature-based k-nearest neighbor for positive/negative classification. In this study, we also investigated the following additional methods to improve CAD performance: 1) grouping 2-D detected features into a single 3-D object; 2) selecting features with a genetic algorithm (GA); and 3) limiting the number of allowed suspicious lesions to be cued in one examination. The results showed that 1) CAD scheme using tobogganing, an ANN, and grouping method achieved the maximum detection sensitivity of 79.2%; 2) the maximum scoring method achieved the superior performance over other scoring fusion methods; 3) GA was able to delete "redundant" features and further improve CAD performance; and 4) limiting the maximum number of cued lesions in an examination reduced FP rate by 5.3 times. Combining these approaches, CAD scheme achieved 63.2% detection sensitivity with 18.4 FP lesions per examination. The study suggested that performance of CAD schemes for PE detection depends on many factors that include 1) optimizing the 2-D region grouping and scoring methods; 2) selecting the optimal feature set; and 3) limiting the number of allowed cueing lesions per examination.

Mass-dependent ion heating during magnetic reconnection in a laboratory plasma

Noncollisional ion heating in laboratory and astrophysical plasmas and the mechanism of conversion of magnetic energy to ion thermal energy are not well understood. In the Madison Symmetric Torus reversed-field pinch experiment, ions are heated rapidly during impulsive reconnection, attaining temperatures exceeding hundreds of eV, often well in excess of the electron temperature. The energy budget of the ion heating and its mass scaling in hydrogen, deuterium, and helium plasmas were determined by measuring the fraction of the released magnetic energy converted to ion thermal energy. The fraction ranges from about 10%-30% and increases approximately as the square root of the ion mass. A simple model based on stochastic ion heating is proposed that is consistent with the experimental data.

Reducing morphological variability of the cervical carotid artery in serial magnetic resonance imaging using a head and neck immobilization device

PURPOSE

To evaluate how well a head and neck immobilization device performed in reducing lumen morphology variability in repeated MR imaging of the carotid artery.

MATERIALS AND METHODS

Quantitative measures of lumen and plaque characteristics may be important for longitudinal management of carotid atherosclerotic disease. However, quantitative measurements of the carotid artery are limited by their dependence on patient positioning, which can be quite variable. We created a head and neck immobilization device to reduce the variability of patient positioning during MR imaging of the carotid artery. In this article we describe the design and use of the immobilization device and assess how well its use reduced variability in vascular orientation and measurements of the carotid lumen cross-sectional area. Evaluation was based on 15 subjects who were repeatedly imaged without the immobilization device and 14 subjects who were repeatedly imaged with the device.

RESULTS

Use of the immobilization device decreased the orientation variability from 9.1 degrees to 5.3 degrees (P = 0.0006) and the variability (defined as the standard deviation divided by the mean) of the cross-sectional area decreased from 0.24 to 0.18 (P = 0.04).

CONCLUSION

Using the immobilization device effectively reduces variability in repeated imaging of the carotid arteries.

Coil sensitivity estimation for optimal SNR reconstruction and intensity inhomogeneity correction in phased array MR imaging

Magnetic resonance (MR) images can be acquired by multiple receiver coil systems to improve signal-to-noise ratio (SNR) and to decrease acquisition time. The optimal SNR images can be reconstructed from the coil data when the coil sensitivities are known. In typical MR imaging studies, the information about coil sensitivity profiles is not available. In such cases the sum-of-squares (SoS) reconstruction algorithm is usually applied. The intensity of the SoS reconstructed image is modulated by a spatially variable function due to the non-uniformity of coil sensitivities. Additionally, the SoS images also have sub-optimal SNR and bias in image intensity. All these effects might introduce errors when quantitative analysis and/or tissue segmentation are performed on the SoS reconstructed images. In this paper, we present an iterative algorithm for coil sensitivity estimation and demonstrate its applicability for optimal SNR reconstruction and intensity inhomogeneity correction in phased array MR imaging.

Registering liver pathological images with prior in vivo CT/MRI data

Liver transplantation affords a unique opportunity to assess and improve radiological imaging of the liver, as the full explanted liver is available for review and comparison. Quantitative comparison between the explanted liver and in vivo images acquired prior to transplantation requires accurate registration of images of the explanted liver to the radiological images. However, this registration problem is challenging because the orientation change and the deformation magnitude between the two image sets exceed the level assumed for most registration algorithms. This paper suggests a two-step registration process to overcome the difficulty: to first align the orientation of 3D liver models built from two sets of image data using maximum volume overlap as their similarity measurement, and second to deform one model to match the other. The key contribution of this paper is that it utilizes the global volumetric information and the asymmetry property of the liver model to determinately provide a simple and reliable initial point for further deformable model based registration. Our experimental data demonstrate the effectiveness of this approach.

Automatic detection of three-dimensional vascular tree centerlines and bifurcations in high-resolution magnetic resonance angiography

OBJECTIVES

We sought to develop a simple and robust algorithm capable of automatically detecting centerlines and bifurcations of a three-dimensional (3D) vascular bed.

MATERIALS AND METHODS

After necessary preprocessing, an appropriate cost function is computed for all vessel voxels and Dijkstra's minimum-cost-path algorithm is implemented. By back tracing all the minimum-cost paths, centerlines and bifurcation are detected. The detected paths are then split into segments between adjacent nodes (bifurcations or vessel end-points) and smoothed by curve fitting.

RESULTS

Application of the algorithm to both simulated 3D vessels and 3D magnetic resonance angiography (MRA) images of an actual intracranial arterial tree produced well-centered vessel skeletons. Quantitative assessment of the algorithm was performed. For the simulated data, the root mean square error for centerline detection is about half a voxel. For the human intracranial MRA data, the sensitivity, positive predictive value (PPV), and accuracy of bifurcation detection were calculated for different cost functions. The best case gave a sensitivity of 91.4%, a PPV of 91.4%, and an RMS error of 1.7 voxels.

CONCLUSIONS

To the extent that imperfections are eliminated from the segmented image, the algorithm is effective and robust in automatic and accurate detection of centerlines and bifurcations. The cost function and algorithm used are demonstrated to be an improvement over similar algorithms in the literature.

3D multi-scale vessel enhancement filtering based on curvature measurements: application to time-of-flight MRA

In this paper we evaluate the use of voxel intensity curvature measurements to enhance vessels in 3D MRA images. We compare a multi-scale discrete kernel filter (MaxCurve) to the Hessian matrix based filter proposed by Frangi and co-workers. The MaxCurve filter is based on the maximum difference between the negative curvature computed along orthogonal lines defined by a 3x3x3 kernel. Filter performance is assessed using measures of vessel and background separation (contrast and the area under the ROC curve). Filter parameters are optimized using a training set of four typical time-of-flight MRA images and tested on a separate set of ten MRA images with the same acquisition parameters. The filters tended to provide good MIP image contrast enhancement. The filters are applied to MRA images acquired with different parameters and field strengths indicating potential usefulness for a variety of images. Overall the discrete kernel and Hessian matrix filter performed quite similarly.

Registration of MR and CT images of the liver: comparison of voxel similarity and surface based registration algorithms

The purpose of this work was to determine the feasibility and efficacy of retrospective registration of MR and CT images of the liver. The open-source ITK Insight Software package developed by the National Library of Medicine (USA) contains a multi-resolution, voxel-similarity-based registration algorithm which we selected as our baseline registration method. For comparison we implemented a multi-scale surface fitting technique based on the head-and-hat algorithm. Registration accuracy was assessed using the mean displacement of automatically selected point landmarks. The ITK voxel-similarity-based registration algorithm performed better than the surface-based approach with mean misregistration in the range of 7.7-8.4 mm for CT-CT registration, 8.2 mm for MR-MR registration, and 14.0-18.9 mm for MR-CT registration compared to mean misregistration from the surface-based technique in the range of 9.6-11.1 mm for CT-CT registration, 9.2-12.4 mm for MR-MR registration, and 15.2-19.0 mm for MR-CT registration.

NMR measurements of the diffusional permeability of water in cultured colonic epithelial cancer cells

The water residence time and diffusional water permeability in colonic epithelial T84 cancer cells was measured using (1)H NMR spectroscopy; the values estimated were 35.2+/-2.8 ms and (7.4+/-0.6)x10(-3)cms(-1), respectively. Water permeability was inhibited to approximately 10% of its original value by the mercurial diuretic, p-chloromercuribenzenesulfonate (PCMBS; 1mM), and fully restored by dithiothreitol (DTT; 1mM). The permeability was also inhibited reversibly to approximately 55%, by extracellular glibenclamide (1mM), an inhibitor of some ATP-binding cassette (ABC) transporters, including the cystic fibrosis transmembrane conductance regulator (CFTR). Addition of the phosphodiesterase inhibitor, 3-isobutyl-1-methylxanthine (IMBX; 0.1-1mM) and the adenylate cyclase activator, forskolin (0.1-1mM) did not alter water permeability. It is concluded that in T84 cells water diffuses through the membrane lipid bilayer and via channels that are inhibited by PCMBS, including the channels that are known to be inhibited by glibenclamide.

Lung Cancer Screening: Simulations of Effects of Imperfect Detection on Temporal Dynamics

PURPOSE

To use a mathematic model to demonstrate effects of imperfect detection on temporal dynamics of radiologic lung cancer screening.

MATERIALS AND METHODS

Monte Carlo simulations of lung cancer screening programs were performed in subjects at high risk for developing cancer. The effects of detection probabilities, symptomatic presentation of tumors, tumor volume doubling time, and time between screenings were examined. Computed tomography (CT) and chest radiography models were used.

RESULTS

For imperfect detection probabilities, the percentage of subjects with cancers detected with repeated screenings decreased to a steady-state value. The transition period was the period during which screenings were performed and detection rates decreased. At steady-state repeat screening, the proportion of subjects with cancers diagnosed at screening or by means of symptomatic presentation was determined by the annual probability of developing cancer and not by the sensitivity of the screening modality. The sensitivity of the screening technique did affect detected cancer size, number of interval cancers, and total number of cancers observed. CT was used to detect more total cancers over the course of the screening program and cancers with a smaller average size; moreover, fewer interval cancers were observed with CT screening than with chest radiography screening.

CONCLUSION

Lung cancer screening with imperfect detection has a transition period between baseline screening and steady-state behavior of annual screenings. Advantages of CT screening include a decrease in the average cancer size at detection, a decrease in the number of observed interval cancers, and an increase in the total number of cancers observed. Steady-state behavior indicates that long-term trials of screening may not be necessary.

31P MAS-NMR of human erythrocytes: independence of cell volume from angular velocity

31P magic angle spinning NMR (MAS-NMR) spectra were obtained from suspensions of human red blood cells (RBCs) that contained the cell-volume-sensitive probe molecule, dimethyl methylphosphonate (DMMP). A mathematical representation of the spectral-peak shape, including the separation and width-at-half-height in the 31P NMR spectra, as a function of rotor speed, enabled us to explore the extent to which a change in cell volume would be reflected in the spectra if it occurred. We concluded that a fractional volume change in excess of 3% would have been detected by our experiments. Thus, the experiments indicated that the mean cell volume did not change by this amount even at the highest spinning rate of 7 kHz. The mean cell volume and intracellular 31P line-width were independent of the packing density of the cells and of the initial cell volume. The relationship of these conclusions to other non-NMR studies of pressure effects on cells is noted.

Intracranial vessel segmentation from time-of-flight MRA using pre-processing of the MIP Z-buffer: accuracy of the ZBS algorithm

We evaluate the accuracy of a vascular segmentation algorithm which uses continuity in the maximum intensity projection (MIP) depth Z-buffer as a pre-processing step to generate a list of 3D seed points for further segmentation. We refer to the algorithm as Z-buffer segmentation (ZBS). The pre-processing of the MIP Z-buffer is based on smoothness measured using the minimum chi-square value of a least square fit. Points in the Z-buffer with chi-square values below a selected threshold are used as seed points for 3D region growing. The ZBS algorithm couples spatial continuity information with intensity information to create a simple yet accurate segmentation algorithm. We examine the dependence of the segmentation on various parameters of the algorithm. Performance is assessed in terms of the inclusion/exclusion of vessel/background voxels in the segmentation of intracranial time-of-flight MRA images. The evaluation is based on 490,256 voxels from 14 patients which were classified by an observer. ZBS performance was compared to simple thresholding and to segmentation based on vessel enhancement filtering. The ZBS segmentation was only weakly dependent on the parameters of the initial MIP image generation, indicating the robustness of this approach. Region growing based on Z-buffer generated seeds was advantageous compared to simple thresholding. The ZBS algorithm provided segmentation accuracies similar to that obtained with the vessel enhancement filter. The ZBS performance was notably better than the filter based segmentation for aneurysms where the assumptions of the filter were violated. As currently implemented the algorithm slightly under-segments the intracranial vasculature.

Observations of multiple magnetic islands in the core of a reversed field pinch

We describe in this Letter the first measurement of multiple islands in the core of a reversed field pinch (RFP). These islands appear with current profile modification leading to magnetic fluctuation reduction in the Madison symmetric torus RFP. Magnetic island widths decrease to an unprecedented level, reducing the overlap of adjacent islands and allowing distinct islands to appear. The structures are observed in multichord measurements of soft-x-ray emissivity. The soft-x-ray data is validated with Poincaré reconstructions of the magnetic field structure in the core.

Automated lung segmentation in X-ray computed tomography: development and evaluation of a heuristic threshold-based scheme

RATIONALE AND OBJECTIVES

To develop and evaluate a reliable, fully-automated lung segmentation scheme for application in X-ray computed tomography.

MATERIALS AND METHODS

The automated scheme was heuristically developed using a slice-based, pixel-value threshold and two sets of classification rules. Features used in the rules include size, circularity, and location. The segmentation scheme operates slice-by-slice and performs three key operations: (1) image preprocessing to remove background pixels, (2) computation and application of a pixel-value threshold to identify lung tissue, and (3) refinement of the initial segmented regions to prune incorrectly detected airways and separate fused right and left lungs.

RESULTS

The performance of the automated segmentation scheme was evaluated using 101 computed tomography cases (91 thick slice, 10 thin slice scans). The 91 thick cases were pre- and post-surgery from 50 patients and were not independent. The automated scheme successfully segmented 94.0% of the 2,969 thick slice images and 97.6% of the 1,161 thin slice images. The mean difference of the total lung volumes calculated by the automated scheme and functional residual capacity plus 60% inspiratory capacity was -24.7 +/- 508.1 mL. The mean differences of the total lung volumes calculated by the automated scheme and an established, commonly used semi-automated scheme were 95.2 +/- 52.5 mL and -27.7 +/- 66.9 mL for the thick and thin slice cases, respectively.

CONCLUSION

This simple, fully-automated lung segmentation scheme provides an objective tool to facilitate lung segmentation from computed tomography scans.

Diagnostic fidelity of the Z-buffer segmentation algorithm: preliminary assessment based on intracranial aneurysm detection

We have developed an algorithm known as the Z-buffer segmentation (ZBS) algorithm for segmenting vascular structures from 3D MRA images. Previously we evaluated the accuracy of the ZBS algorithm on a voxel level in terms of inclusion and exclusion of vascular and background voxels. In this paper we evaluate the diagnostic fidelity of the ZBS algorithm. By diagnostic fidelity we mean that the data preserves the structural information necessary for diagnostic evaluation. This evaluation is necessary to establish the potential usefulness of the segmentation for improved image display, or whether the segmented data could form the basis of a computerized analysis tool. We assessed diagnostic fidelity by measuring how well human observers could detect aneurysms in the segmented data sets. ZBS segmentation of 30 MRA cases containing 29 aneurysms was performed. Image display used densitometric reprojections with shaded surface highlighting that were generated from the segmented data. Three neuroradiologists independently reviewed the generated ZBS images for aneurysms. The observers had 80% sensitivity (90% for aneurysms larger than 2mm) with 0.13 false positives per image. Good agreement with the gold standard for describing aneurysm size and orientation was shown. These preliminary results suggest that the segmentation has diagnostic fidelity with the original data and may be useful for improved visualization or automated analysis of the vasculature.

Identifying respiratory findings in emergency department reports for biosurveillance using MetaMap

Clinical conditions described in patients' dictated reports are necessary for automated detection of patients with respiratory illnesses such as inhalational anthrax and pneumonia. We applied MetaMap to emergency department reports to extract a set of 71 clinical conditions relevant to detection of a lower respiratory outbreak. We indexed UMLS terms in emergency department reports with MetaMap, filtered the indexed output with a specialized lexicon of UMLS terms for the domain, and mapped the clinical conditions of interest to concepts in the lexicon. We compared MetaMap's ability to accurately identify the conditions against a physician's manual annotations and evaluated incorrectly indexed features to determine what additional processing is necessary. MetaMap identified the clinical conditions with a recall of 0.72 and a precision of 0.56. Necessary processing beyond MetaMap's indexing includes finding validation, temporal discrimination, anatomic location discrimination, finding-disease discrimination, and contextual inference. Successful identification of clinical conditions in an emergency department report with MetaMap requires processing techniques specific to the clinical question of interest.

Comparative NMR studies of diffusional water permeability of red blood cells from different species: XIV. Little Penguin, Eudyptula minor

As part of a programme of comparative measurements of diffusional water permeability (Pd) the red blood cells (RBC) from Little Penguin (Eudyptula minor) were studied. The cell dimensions were measured with light and electron microscopy, and by a newly described non-invasive technique, NMR q-space analysis. In view of its relative novelty for cell biologists, an overview of this technique is presented. The RBC revealed an ellipsoidal shape that is characteristic of avian RBC, with axis lengths ("diameters") estimated to be: a=16.0 microm; b=9.6 microm; c=5.0 microm. The values of P(d)were: 2.0 x 10(-3)cm s(-1)at 5 degrees C, 3.3 x 10(-3)cm s(-1)at 10 degrees C, 4.6 x 10(-3)cm s(-1)at 15 degrees C and approximately 5.4 x 10(-3)cm s(-1)at 20, 25, 30, 37 and 42 degrees C. There was a lack of inhibition of water permeability by p-chloromercuribenzensulfonate (PCMBS), the well-known inhibitor of RBC aquaporin. It was notable that in the temperature range 5-20 degrees C the NMR parameters, and hence the permeability, varied linearly as is found for other species, but at temperatures higher than 20 degrees C there was no temperature-dependence of Pd. Consequently, there was an obvious break at approximately 20 degrees C in the Arrhenius plot, of the mean residence life time of water inside the cells, 1/Te, versus temperature. For temperatures less than 20 degrees C the activation energy E(a,d) was 45.6 +/- 6.6 kJ/mol. For temperatures higher than 25 degrees C E(a,d) was zero. The lack of inhibition of water permeability by PCMBS and the very high value of E(a,d) for diffusive water exchange suggests that the water permeation occurs primarily via the membrane bilayer per se, i.e., there is no aquaporin in Little Penguin RBC. The discontinuity at approximately 20 degrees C in the Arrhenius plot is an interesting finding, not seen before in other species, and we suggest that it reflects a phase transition in the membrane lipids.

Creating a text classifier to detect radiology reports describing mediastinal findings associated with inhalational anthrax and other disorders

OBJECTIVE

The aim of this study was to create a classifier for automatic detection of chest radiograph reports consistent with the mediastinal findings of inhalational anthrax.

DESIGN

The authors used the Identify Patient Sets (IPS) system to create a key word classifier for detecting reports describing mediastinal findings consistent with anthrax and compared their performances on a test set of 79,032 chest radiograph reports.

MEASUREMENTS

Area under the ROC curve was the main outcome measure of the IPS classifier. Sensitivity and specificity of an initial IPS model were calculated based on an existing key word search and were compared against a Boolean version of the IPS classifier.

RESULTS

The IPS classifier received an area under the ROC curve of 0.677 (90% CI = 0.628 to 0.772) with a specificity of 0.99 and maximum sensitivity of 0.35. The initial IPS model attained a specificity of 1.0 and a sensitivity of 0.04.

CONCLUSION

The IPS system is a useful tool for helping domain experts create a statistical key word classifier for textual reports that is a potentially useful component in surveillance of radiographic findings suspicious for anthrax.

Observation of velocity-independent electron transport in the reversed field pinch

Confinement of runaway electrons has been observed for the first time in a reversed field pinch during improved-confinement plasmas in the Madison Symmetric Torus. Energy-resolved hard-x-ray flux measurements have been used to determine the velocity dependence of the electron diffusion coefficient, utilizing computational solutions of the Fokker-Planck transport equation. With improved-confinement, the fast electron diffusivity drops by 2 orders of magnitude and is independent of velocity. This suggests a change in the transport mechanism away from stochastic magnetic field diffusion.

The need for phase-encoding flow compensation in high-resolution intracranial magnetic resonance angiography

PURPOSE

To demonstrate that the time delay between phase and frequency encoding and the presence of pulsatile blood flow in high-resolution time-of-flight (TOF) imaging of the intracranial arteries (especially near the circle of Willis) can distort the appearance of blood vessels and result in a cross-hatch-appearing artifact in surrounding tissue.

MATERIALS AND METHODS

Two techniques to reduce the artifact, tri-directional flow compensation (3DFC) and elliptical-centric (EC) phase-encoding order, are investigated in five volunteer studies.

RESULTS

3DFC eliminates the pulsation-related artifacts and the vessel distortion. A residual amplitude variation artifact is observed. EC phase encoding nearly eliminates the pulsatile motion-related artifact, but it does not eliminate vessel distortion.

CONCLUSION

The combination of 3DFC and EC phase encoding appears to provide the greatest artifact reduction in the five volunteer studies performed.

Automated assessment of the composition of breast tissue revealed on tissue-thickness-corrected mammography

OBJECTIVE

Variations in the thickness of a compressed breast and the resulting variations in mammographic densities confound current automated procedures for estimating tissue composition of breasts from digitized mammograms. We sought to determine whether adjusting mammographic data for tissue thickness before estimating tissue composition could improve the accuracy of the tissue estimates.

MATERIALS AND METHODS

We developed methods for locally estimating breast thickness from mammograms and then adjusting pixel values so that the values correlated with the tissue composition over the breast area. In our technique, the pixel values are corrected for the nonlinearity of the combined characteristic curve from the film and film digitizer; the approximate relative thickness as a function of distance from the skin line is measured; and the pixel values are adjusted to reflect their distance from the skin line. To estimate tissue composition, we created a backpropagation neural network classifier from features extracted from the histogram of pixel values, after the data had been adjusted for characteristic curve and tissue thickness. We used a 10-fold cross-validation method to evaluate the neural network. The averaged scores of three radiologists were our gold standard.

RESULTS

The performance of the neural network was calculated as the percentage of correct classifications of images that were or were not corrected to reflect tissue thickness. With its parameters derived from the pixel-value histogram, the neural network based on corrected images performed better (71% accuracy) than that based on uncorrected images (67% accuracy) (p < 0.05).

CONCLUSION

Our results show that adjusting tissue thickness before estimating tissue composition improved the performance of our estimation procedure in reproducing the tissue composition values determined by radiologists.

Effects of p-chloromercuribenzene sulfonate on water transport across the marsupial erythrocyte membrane

The effects of exposure of red blood cells (RBC) of three species of marsupial to a mercury-containing sulfhydryl-modifying reagent, p-chloromercuribenzene sulfonate (PCMBS), on the water diffusional permeability ( P (d)) of their membranes were monitored by using an Mn(2+)-doping (1)H nuclear magnetic resonance (NMR) technique at 400 MHz. For koala ( Phascolarctos cinereus), RBC the maximal inhibition was reached at 37 degrees C in 60 min with 1 mmol.l(-1) PCMBS or in 15-30 min with 2 mmol. l(-1) PCMBS. In contrast, in the case of red kangaroo ( Macropus rufus) or swamp wallaby ( Wallabia bicolor) RBC, maximal inhibition required an incubation of 90 min at 37 degrees C with 2 mmol.l(-1) PCMBS. For the RBC of all three species the value of maximal inhibition was very high, being 50-70% when measured at 25 degrees C, 60-80% at 30 degrees C and 60-70% at 37 degrees C. The lowest values of P (d) appeared to be around 2 x 10(-3)-3 x 10(-3) cm.s(-1) in the temperature range of 25-37 degrees C. The mean value of the activation energy of water diffusion ( E (a,d)) was approximately 20-25 kJ.mol(-1) for control and approximately 40 kJ.mol(-1) for PCMBS-inhibited RBCs. These results show that marsupial RBC have a basal permeability to water similar to that previously reported for human RBC, but a higher value of the PCMBS-inhibitable water permeability. This indicates that the higher water permeability of marsupial RBC compared with human RBC is associated with a higher fraction of protein-mediated water permeability.

Measurement of the current-density profile and plasma dynamics in the reversed-field pinch

First measurements of the current-density profile in the core of a high-temperature reversed-field pinch are presented. The current-density profile is observed to peak during the sawtooth cycle and broaden promptly at the crash. This change in profile can be linked to magnetic relaxation and the dynamo which is predicted to drive antiparallel current in the plasma core. For high-confinement discharges, the dynamo is suppressed and the current-density profile is observed to strongly peak.