Digital Imaging and Communications in Medicine (DICOM)
01 Jan 2012-
TL;DR: The SR SOP Classes allow users to link text and other data to particular images and/or waveforms and to store the coordinates of findings so that users can see exactly what is being described in a report.
Abstract: Foreword This Supplement to the DICOM Standard introduces the SR SOP Classes for transmission and storage of documents that describe or refer to any number of images or waveforms or to the specific features that they contain. The SR SOP Classes fully support conventional free text reports and provide the capability 4 to record structured information that enhances the precision, clarity and value of clinical documents. The SR SOP Classes allow users to link text and other data to particular images and/or waveforms and to store the coordinates of findings so that users can see exactly what is being described in a report. In addition, users can label, index and retrieve clinically-relevant information using codes. SR SOP Classes 8 can be used in a variety of clinical contexts. For example:-in CT or MRI to convey the interpretation text, to record the DICOM identifiers of selected images and to denote the spatial coordinates of significant findings;-in ultrasound to transmit measurements; and 12-in cardiac catheterization laboratories to record a procedure log that time-stamps and describes significant measurements and interventions and link together all of the related images, waveforms, interpretation reports and related information into a convenient unit-record.
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TL;DR: The capabilities and physics models implemented inside the FLUKA code are briefly described, with emphasis on hadronic interaction as discussed by the authors, and examples of the performances of the code are presented including basic (thin target) and complex benchmarks, and radiation detector specific applications.
521 citations
TL;DR: This work proposes a model named OmniPHR, a distributed model to integrate PHRs, and demonstrates the feasibility of the model in maintaining health records distributed in an architecture model that promotes a unified view of PHR with elasticity and scalability of the solution.
282 citations
TL;DR: The data format imzML was developed to allow the flexible and efficient exchange of MS imaging data between different instruments and data analysis software.
261 citations
TL;DR: Following a standardized protocol, the evaluation of UWF FA for NPA and NPI is reproducible and both parameters are correlated highly with the presence of PPLs and DR severity.
177 citations
TL;DR: A demystifying overview of the major file formats currently used in medical imaging: Analyze, Neuroimaging Informatics Technology Initiative, Minc, and Digital Imaging and Communications in Medicine (Dicom) is presented.
Abstract: Image file format is often a confusing aspect for someone wishing to process medical images. This article presents a demystifying overview of the major file formats currently used in medical imaging: Analyze, Neuroimaging Informatics Technology Initiative (Nifti), Minc, and Digital Imaging and Communications in Medicine (Dicom). Concepts common to all file formats, such as pixel depth, photometric interpretation, metadata, and pixel data, are first presented. Then, the characteristics and strengths of the various formats are discussed. The review concludes with some predictive considerations about the future trends in medical image file formats.
171 citations
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Book•
01 Jan 1978TL;DR: In this article, the authors compare two straight line regression models and conclude that the Straight Line Regression Equation does not measure the strength of the Straight-line Relationship, but instead is a measure of the relationship between two straight lines.
Abstract: 1. CONCEPTS AND EXAMPLES OF RESEARCH. Concepts. Examples. Concluding Remarks. References. 2. CLASSIFICATION OF VARIABLES AND THE CHOICE OF ANALYSIS. Classification of Variables. Overlapping of Classification Schemes. Choice of Analysis. References. 3. BASIC STATISTICS: A REVIEW. Preview. Descriptive Statistics. Random Variables and Distributions. Sampling Distributions of t, ?O2, and F. Statistical Inference: Estimation. Statistical Inference: Hypothesis Testing. Error Rate, Power, and Sample Size. Problems. References. 4. INTRODUCTION TO REGRESSION ANALYSIS. Preview. Association versus Causality. Statistical versus Deterministic Models. Concluding Remarks. References. 5. STRAIGHT-LINE REGRESSION ANALYSIS. Preview. Regression with a Single Independent Variable. Mathematical Properties of a Straight Line. Statistical Assumptions for a Straight-line Model. Determining the Best-fitting Straight Line. Measure of the Quality of the Straight-line Fit and Estimate ?a2. Inferences About the Slope and Intercept. Interpretations of Tests for Slope and Intercept. Inferences About the Regression Line ?YY|X = ?O0 + ?O1X . Prediction of a New Value of Y at X0. Problems. References. 6. THE CORRELATION COEFFICIENT AND STRAIGHT-LINE REGRESSION ANALYSIS. Definition of r. r as a Measure of Association. The Bivariate Normal Distribution. r and the Strength of the Straight-line Relationship. What r Does Not Measure. Tests of Hypotheses and Confidence Intervals for the Correlation Coefficient. Testing for the Equality of Two Correlations. Problems. References. 7. THE ANALYSIS-OF-VARIANCE TABLE. Preview. The ANOVA Table for Straight-line Regression. Problems. 8. MULTIPLE REGRESSION ANALYSIS: GENERAL CONSIDERATIONS. Preview. Multiple Regression Models. Graphical Look at the Problem. Assumptions of Multiple Regression. Determining the Best Estimate of the Multiple Regression Equation. The ANOVA Table for Multiple Regression. Numerical Examples. Problems. References. 9. TESTING HYPOTHESES IN MULTIPLE REGRESSION. Preview. Test for Significant Overall Regression. Partial F Test. Multiple Partial F Test. Strategies for Using Partial F Tests. Tests Involving the Intercept. Problems. References. 10. CORRELATIONS: MULTIPLE, PARTIAL, AND MULTIPLE PARTIAL. Preview. Correlation Matrix. Multiple Correlation Coefficient. Relationship of RY|X1, X2, !KXk to the Multivariate Normal Distribution. Partial Correlation Coefficient. Alternative Representation of the Regression Model. Multiple Partial Correlation. Concluding Remarks. Problems. References. 11. CONFOUNDING AND INTERACTION IN REGRESSION. Preview. Overview. Interaction in Regression. Confounding in Regression. Summary and Conclusions. Problems. References. 12. DUMMY VARIABLES IN REGRESSION. Preview. Definitions. Rule for Defining Dummy Variables. Comparing Two Straight-line Regression Equations: An Example. Questions for Comparing Two Straight Lines. Methods of Comparing Two Straight Lines. Method I: Using Separate Regression Fits to Compare Two Straight Lines. Method II: Using a Single Regression Equation to Compare Two Straight Lines. Comparison of Methods I and II. Testing Strategies and Interpretation: Comparing Two Straight Lines. Other Dummy Variable Models. Comparing Four Regression Equations. Comparing Several Regression Equations Involving Two Nominal Variables. Problems. References. 13. ANALYSIS OF COVARIANCE AND OTHER METHODS FOR ADJUSTING CONTINUOUS DATA. Preview. Adjustment Problem. Analysis of Covariance. Assumption of Parallelism: A Potential Drawback. Analysis of Covariance: Several Groups and Several Covariates. Comments and Cautions. Summary Problems. Reference. 14. REGRESSION DIAGNOSTICS. Preview. Simple Approaches to Diagnosing Problems in Data. Residual Analysis: Detecting Outliers and Violations of Model Assumptions. Strategies of Analysis. Collinearity. Scaling Problems. Diagnostics Example. An Important Caution. Problems. References. 15. POLYNOMIAL REGRESSION. Preview. Polynomial Models. Least-squares Procedure for Fitting a Parabola. ANOVA Table for Second-order Polynomial Regression. Inferences Associated with Second-order Polynomial Regression. Example Requiring a Second-order Model. Fitting and Testing Higher-order Model. Lack-of-fit Tests. Orthogonal Polynomials. Strategies for Choosing a Polynomial Model. Problems. 16. SELECTING THE BEST REGRESSION EQUATION. Preview. Steps in Selecting the Best Regression Equation. Step 1: Specifying the Maximum Model. Step 2: Specifying a Criterion for Selecting a Model. Step 3: Specifying a Strategy for Selecting Variables. Step 4: Conducting the Analysis. Step 5: Evaluating Reliability with Split Samples. Example Analysis of Actual Data. Issues in Selecting the Most Valid Model. Problems. References. 17. ONE-WAY ANALYSIS OF VARIANCE. Preview. One-way ANOVA: The Problem, Assumptions, and Data Configuration. for One-way Fixed-effects ANOVA. Regression Model for Fixed-effects One-way ANOVA Fixed-effects Model for One-way ANOVA. Random-effects Model for One-way ANOVA. -comparison Procedures for Fixed-effects One-way ANOVA. a Multiple-comparison Technique. Orthogonal Contrasts and Partitioning an ANOVA Sum of Squares. Problems. References. 18. RANDOMIZED BLOCKS: SPECIAL CASE OF TWO-WAY ANOVA. Preview. Equivalent Analysis of a Matched-pairs Experiment. Principle of Blocking. Analysis of a Randomized-blocks Experiment. ANOVA Table for a Randomized-blocks Experiment. Models for a Randomized-blocks Experiment. Fixed-effects ANOVA Model for a Randomized-blocks Experiment. Problems. References. 19. TWO-WAY ANOVA WITH EQUAL CELL NUMBERS. Preview. Using a Table of Cell Means. General Methodology. F Tests for Two-way ANOVA. Regression Model for Fixed-effects Two-way ANOVA. Interactions in Two-way ANOVA. Random- and Mixed-effects Two-way ANOVA Models. Problems. References. 20. TWO-WAY ANOVA WITH UNEQUAL CELL NUMBERS. Preview. Problem with Unequal Cell Numbers: Nonorthogonality. Regression Approach for Unequal Cell Sample Sizes. Higher-way ANOVA. Problems. References. 21. THE METHOD OF MAXIMUM LIKELIHOOD. Preview. The Principle of Maximum Likelihood. Statistical Inference Using Maximum Likelihood. Summary. Problems. 22. LOGISTIC REGRESSION ANALYSIS. Preview. The Logistic Model. Estimating the Odds Ratio Using Logistic Regression. A Numerical Example of Logistic Regression. Theoretical Considerations. An Example of Conditional ML Estimation Involving Pair-matched Data with Unmatched Covariates. Summary. Problems. References. 23. POLYTOMOUS AND ORDINAL LOGISTIC REGRESSION. Preview. Why Not Use Binary Regression? An Example of Polytomous Logistic Regression: One Predictor, Three Outcome Categories. An Example: Extending the Polytomous Logistic Model to Several Predictors. Ordinal Logistic Regression: Overview. A "Simple" Hypothetical Example: Three Ordinal Categories and One Dichotomous Exposure Variable. Ordinal Logistic Regression Example Using Real Data with Four Ordinal Categories and Three Predictor Variables. Summary. Problems. References. 24. POISSON REGRESSION ANALYSIS. Preview. The Poisson Distribution. Example of Poisson Regression. Poisson Regression: General Considerations. Measures of Goodness of Fit. Continuation of Skin Cancer Data Example. A Second Illustration of Poisson Regression Analysis. Summary. Problems. References. 25. ANALYSIS OF CORRELATED DATA PART 1: THE GENERAL LINEAR MIXED MODEL. Preview. Examples. General Linear Mixed Model Approach. Example: Study of Effects of an Air Polluion Episode on FEV1 Levels. Summary!XAnalysis of Correlated Data: Part 1. Problems. References. 26. ANALYSIS OF CORRELATED DATA PART 2: RANDOM EFFECTS AND OTHER ISSUES. Preview. Random Effects Revisited. Results for Random Effects Models Applied to Air Pollution Study Data. Second Example!XAnalysis of Posture Measurement Data. Recommendations about Choice of Correlation Structure. Analysis of Data for Discrete Outcomes. Problems. References. 27. SAMPLE SIZE PLANNING FOR LINEAR AND LOGISTIC REGRESSION AND ANALYSIS OF VARIANCE. Preview. Review: Sample Size Calculations for Comparisons of Means and Proportions. Sample Size Planning for Linear Regression. Sample Size Planning for Logistic Regression. Power and Sample Size Determination for Linear Models: A General Approach. Sample Size Determination for Matched Case-control Studies with a Dichotomous Outcome. Practical Considerations and Cautions. Problems. References. Appendix A. Appendix B. Appendix C. Solutions to Exercises. Index.
9,433 citations
Patent•
02 Mar 1998
TL;DR: In this paper, a method and apparatus for constructing a virtual microscope slide comprised of digitally scanned images from a microscope specimen is described, which is arranged in a tiled format convenient for viewing without a microscope, and for transferring the tiled images for viewing by another at a remote location.
Abstract: A method and apparatus are disclosed for constructing a virtual microscope slide comprised of digitally scanned images from a microscope specimen. The digitally scanned images are arranged in a tiled format convenient for viewing without a microscope, and for transferring the tiled images for viewing by another at a remote location. Several original microscope views at a low magnification are digitized and stored as digitized images coherently seamed together to provide an overall virtual, macro image of the specimen at a lower resolution. Several original microscope views at higher magnifications are digitized and stored as digitized images coherently seamed together to provide virtual micro images at higher resolution. A data structure is formed with these virtual macro and micro digitized images along with their mapping coordinates. Preferably, a generic viewing program is also provided in the data structure that allows remote users to manipulate and interpret the tiled images on the user's monitor. Also, the data structure is formed with significantly compressed data so as to be transmitted over low bandwidth channels, such as the Internet, without loss of resolution that would interfere with the analysis at a remotely-located pathologist receiving the data structure over the Internet. The preferred interactive program allows the pathologist to scroll and view neighboring image areas of interest. A marker on the macro image indicates to the user the location of the micro image and assists the user in selecting areas from the macro image to be viewed at higher resolution and magnification.
293 citations
01 Jun 1999
TL;DR: This document describes a protocol for adding cryptographic signature and encryption services to MIME data.
Abstract: This document describes a protocol for adding cryptographic signature
and encryption services to MIME data. [STANDARDS-TRACK]
254 citations
TL;DR: Tissue micro-arrays evaluated could substitute for whole sections in correlative studies performed by the Radiation Therapy Oncology Group to demonstrate excellent agreement for p53 between slides and arrays.
95 citations
01 Dec 1995
TL;DR: The Multipart/Related content-type provides a common mechanism for representing objects that are aggregates of related MIME body parts.
Abstract: The Multipart/Related content-type provides a common mechanism for representing objects that are aggregates of related MIME body parts. This document defines the Multipart/Related content-type and provides examples of its use.
63 citations