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issn:1618-727
1.  Mapping LIDC, RadLex™, and Lung Nodule Image Features 
Journal of Digital Imaging  2010;24(2):256-270.
Ideally, an image should be reported and interpreted in the same way (e.g., the same perceived likelihood of malignancy) or similarly by any two radiologists; however, as much research has demonstrated, this is not often the case. Various efforts have made an attempt at tackling the problem of reducing the variability in radiologists’ interpretations of images. The Lung Image Database Consortium (LIDC) has provided a database of lung nodule images and associated radiologist ratings in an effort to provide images to aid in the analysis of computer-aided tools. Likewise, the Radiological Society of North America has developed a radiological lexicon called RadLex. As such, the goal of this paper is to investigate the feasibility of associating LIDC characteristics and terminology with RadLex terminology. If matches between LIDC characteristics and RadLex terms are found, probabilistic models based on image features may be used as decision-based rules to predict if an image or lung nodule could be characterized or classified as an associated RadLex term. The results of this study were matches for 25 (74%) out of 34 LIDC terms in RadLex. This suggests that LIDC characteristics and associated rating terminology may be better conceptualized or reduced to produce even more matches with RadLex. Ultimately, the goal is to identify and establish a more standardized rating system and terminology to reduce the subjective variability between radiologist annotations. A standardized rating system can then be utilized by future researchers to develop automatic annotation models and tools for computer-aided decision systems.
doi:10.1007/s10278-010-9285-6
PMCID: PMC3056962  PMID: 20390436
Chest CT; digital imaging; image data; image interpretation; imaging informatics; lung; radiographic image interpretation; computer-assisted; reporting; RadLex; semantic; LIDC
2.  Automatic Extraction of Concepts to Extend RadLex 
Journal of Digital Imaging  2010;24(1):165-169.
RadLex™, the Radiology Lexicon, is a controlled vocabulary of terms used in radiology. It was developed by the Radiological Society of North America in recognition of a lack of coverage of these radiology concepts by other lexicons. There are still additional concepts, particularly those related to imaging observations and imaging observation characteristics, that could be added to the lexicon. We used a free and open source software system to extract these terms from the medical literature. The system retrieved relevant articles from the PubMed repository and passed them through modules in the Apache Unstructured Information Management Architecture. Image observations and image observation characteristics were identified through a seven-step process. The system was run on a corpus of 1,128 journal articles. The system generated lists of 624 imaging observations and 444 imaging observation characteristics. Three domain experts evaluated the top 100 terms in each list and determined a precision of 52% and 26%, respectively, for identification of image observations and image observation characteristics. We conclude that candidate terms for inclusion in standardized lexicons may be extracted automatically from the peer-reviewed literature. These terms can then be reviewed for curation into the lexicon.
doi:10.1007/s10278-010-9334-1
PMCID: PMC3046782  PMID: 20838847
Natural language processing; algorithms; open source
3.  RadMonitor: Radiology Operations Data Mining in Real Time 
Journal of Digital Imaging  2007;21(3):257-268.
This paper describes the web-based visualization interface of RadMonitor, a platform-independent web application designed to help manage the complexity of information flow within a health care enterprise. The system eavesdrops on Health Layer 7 traffic and parses statistical operational information into a database. The information is then presented to the user as a treemap—a graphical visualization scheme that simplifies the display of hierarchical information. While RadMonitor has been implemented for the purpose of analyzing radiology operations, its XML backend allows it to be reused for virtually any other hierarchical data set.
doi:10.1007/s10278-007-9033-8
PMCID: PMC3043836  PMID: 17534683
Data mining; operations analysis,; treemap visualization
4.  Should Radiology IT be Owned by the Chief Information Officer? 
Considerable debate within the medical community has focused on the optimal location of information technology (IT) support groups on the organizational chart. The challenge has been to marry local accountability and physician acceptance of IT with the benefits gained by the economies of scale achieved by centralized knowledge and system best practices. In the picture archiving and communication systems (PACS) industry, a slight shift has recently occurred toward centralized control. Radiology departments, however, have begun to realize that no physicians in any other discipline are as dependent on IT as radiologists are on their PACS. The potential strengths and weaknesses of centralized control of the PACS is the topic of discussion for this month’s Point/Counterpoint.
doi:10.1007/s10278-009-9196-6
PMCID: PMC3043688  PMID: 19387740
Hospital Information Systems (HIS); information management; PACS; PACS management; radiology department; hospital
5.  An Ontology for PACS Integration 
Journal of Digital Imaging  2006;19(4):316-327.
An ontology describes a set of classes and the relationships among them. We explored the use of an ontology to integrate picture archiving and communication systems (PACS) with other information systems in the clinical enterprise. We created an ontological model of thoracic radiology that contained knowledge of anatomy, imaging procedures, and performed procedure steps. We explored the use of the model in two use cases: (1) to determine examination completeness and (2) to identify reference (comparison) images obtained in the same imaging projection. The model incorporated a total of 138 classes, including radiology orderables, procedures, procedure steps, imaging modalities, patient positions, and imaging planes. Radiological knowledge was encoded as relationships among these classes. The ontology successfully met the information requirements of the two use-case scenarios. Ontologies can represent radiological and clinical knowledge to integrate PACS with the clinical enterprise and to support the radiology interpretation process.
doi:10.1007/s10278-006-0627-3
PMCID: PMC3045159  PMID: 16763933
Ontologies; semantic models; knowledge representation; knowledge sharing and reuse; PACS; systems integration; workflow; Protégé; Web Ontology Language (OWL); Transforming the Radiologic Interpretation Process (TRIP)
6.  Blogging Your PACS 
Journal of Digital Imaging  2005;18(4):326-332.
Acquiring, implementing, and maintaining a picture archiving and communication system (PACS) is an enduring and complex endeavor. A large-scale project such as this requires efficient and effective communication among a large number of stakeholders, sharing of complex documentation, recording ideas, experiences, and events such as meetings, and project milestones to succeed. Often, mass-market technologies designed for other purposes can be used to solve specific complex problems in healthcare. In this case, we wanted to explore the role of popular weblogging or “blogging” software to meet our needs. We reviewed a number of well-known blog software packages and evaluated them based on a set of criteria. We looked at simplicity of installation, configuration, and management. We also wanted an intuitive, Web-based interface for end-users, low cost of ownership, use of open source software, and a secure forum for all PACS team members. We chose and implemented the Invision Power Board for two purposes: local PACS administrative purposes and for a national PACS users' group discussion. We conclude that off the shelf, state-of-the-art, mass-market software such as that used for the currently very popular purpose of weblogging or “blogging” can be very useful in managing the variety of communications necessary for the successful implementation of PACS.
doi:10.1007/s10278-005-7548-4
PMCID: PMC3046726  PMID: 16132484
PACS; documentation; project planning; knowledge base; weblog
7.  Comparison of Human Observer Performance of Contrast-Detail Detection Across Multiple Liquid Crystal Displays 
Journal of Digital Imaging  2005;18(1):66-77.
Appropriate selection of a display subsystem requires balancing the optimization of its physical parameters with clinical setting and cost. Recent advances in Liquid Crystal Display (LCD) technology warrant a rigorous evaluation of both the specialized and the mass market displays for clinical radiology. This article outlines step two in the evaluation of a novel 9.2 million pixel IBM AMLCD panel. Prior to these experiments, the panel was calibrated according to the DICOM Part 14 standard, using both a gray-scale and a pseudo-gray scale lookup table. The specific aim of this study is to compare human, contrast-detail perception on different computer display subsystems. The subsystems that we looked at included 3- and 5-million pixel “medical-grade” monochrome LCDs and a 9.2-million pixel color LCD. We found that the observer response was similar for these three display configurations.
doi:10.1007/s10278-004-1035-1
PMCID: PMC3047211  PMID: 15645331
LCD; phantom; contrast-detail; observer; perception
8.  Validating DICOM Content in a Remote Storage Model 
Journal of Digital Imaging  2005;18(1):37-41.
Verifying the integrity of DICOM files transmitted between separate archives (eg, storage service providers, network attached storage, or storage area networks) is of critical importance. The software application described in this article retrieves a specified number of DICOM studies from two different DICOM storage applications; the primary picture archiving and communication system (PACS) and an off-site long-term archive. The system includes a query/retrieve (Q/R) module, storage service class provider (SCP), a DICOM comparison module, and a graphical user interface. The system checks the two studies for DICOM 3.0 compliance and then verifies that the DICOM data elements and pixel data are identical. Discrepancies in the two data sets are recorded with the data elements (tag number, value representation, value length, and value field) and pixel data (pixel value and pixel location) in question. The system can be operated automatically, in batch mode, and manually to meet a wide variety of use cases. We ran this program on a 15% statistical sample of 50,000 studies (7500 studies examined). We found 2 pixel data mismatches (resolved on retransmission) and 831 header element mismatches. We subsequently ran the program against a smaller batch of 1000 studies, identifying no pixel data mismatches and 958 header element mismatches. Although we did not find significant issues in our limited study, given other incidents that we have experienced when moving images between systems, we conclude that it is vital to maintain an ongoing, automatic, systematic validation of DICOM transfers so as to be proactive in preventing possibly catastrophic data loss.
doi:10.1007/s10278-004-1034-2
PMCID: PMC3047214  PMID: 15645332
DICOM; file verification and comparison; PACS
9.  Reviews in Radiology Informatics: Establishing a Core Informatics Curriculum 
Journal of Digital Imaging  2004;17(4):244-248.
The advent of digital imaging and information management within the radiology department has prompted the growth of a new radiology subspecialty: Radiology Informatics. With appropriate training, radiologists can become leaders in Medical Informatics and guide the growth of this technology throughout the medical enterprise. Radiology Informatics fellowships, as well as radiology residency programs, provide inconsistent exposure to all the elements of this subspecialty, in part because of the lack of a common curriculum. The Society for Computer Applications in Radiology (SCAR) has developed a curriculum intended to guide training in Radiology Informatics. This article is the first in a series presented by SCAR and the Journal of Digital Imaging, titled “Reviews in Radiology Informatics.” The series is designed to sample from each of the major components in the Radiology Informatics Curriculum, to spark further interest in the field and provide content for informatics education.
doi:10.1007/s10278-004-1030-6
PMCID: PMC3047181  PMID: 15692866
Informatics; education; residents
10.  Effect of Viewing Angle on Luminance and Contrast for a Five-Million-Pixel Monochrome Display and a Nine-Million-Pixel Color Liquid Crystal Display  
Journal of Digital Imaging  2004;17(4):264-270.
Digital imaging systems used in radiology rely on electronic display devices to present images to human observers. Active-matrix liquid crystal displays (AMLCDs) continue to improve and are beginning to be considered for diagnostic image display. In spite of recent progress, AMLCDs are characterized by a change in luminance and contrast response with changes in viewing direction. In this article, we characterize high pixel density AMLCDs (a five-million-pixel monochrome display and a nine-million-pixel color display) in terms of the effect of viewing angle on their luminance and contrast response. We measured angular luminance profiles using a custom-made computer-controlled goniometric instrument and a conoscopic Fourier-optics instrument. We show the angular luminance response as a function of viewing angle, as well as the departure of the measured contrast from the desired response. Our findings indicate small differences between the five-million-pixel (5 MP) and the nine-million-pixel (9 MP) AMLCDs. The 9 MP shows lower variance in contrast with changes in viewing angle, whereas the 5 MP provides a slightly better GSDF compliance for off-normal viewing.
doi:10.1007/s10278-004-1021-7
PMCID: PMC3047188  PMID: 15692870
Active-matrix liquid crystal display (AMLCD); viewing angle; gray-scale display function (GSDF)
11.  Assessment of a Novel, High-Resolution, Color, AMLCD for Diagnostic Medical Image Display: Luminance Performance and DICOM Calibration 
Journal of Digital Imaging  2003;16(3):270-279.
This article documents the results of the first in a series of experiments designed to evaluate the suitability of a novel, high resolution, color, digital, liquid crystal display (LCD) panel for diagnostic quality, gray scale image display. The goal of this experiment was to measure the performance of the display, especially with respect to luminance. The panel evaluated was the IBM T221 22.2” backlit active matrix liquid crystal display (AMLCD) with native resolution of 3840 × 2400 pixels. Taking advantage of the color capabilities of the workstation, we were able to create a 256-entry grayscale calibration look-up table derived from a palette of 1786 nearly gray luminance values. We also constructed a 256-entry grayscale calibration look-up table derived from a palette of 256 true gray values for which the red, green, and blue values were equal. These calibrations will now be used in our evaluation of human contrast-detail perception on this LCD panel.
doi:10.1007/s10278-003-1718-z
PMCID: PMC3045256  PMID: 14669065
PACS; image display; AMLCD evaluation; DICOM Part 14 calibration
12.  Research and teaching access to a large clinical picture archiving and communication system 
Journal of Digital Imaging  2001;14(Suppl 1):121-124.
Purpose: To identify practical issues surrounding delivering digital images from picture archiving and communication systems (PACS) for research and teaching purposes. The complexity of Digital Imaging and Communications in Medicine (DICOM) access methods, security, patient confidentiality, PACS database integrity, portability, and scalability are discussed. A software prototype designed to resolve these issues is described.System Architecture: A six-component, three-tier, client server software application program supporting DICOM query/retrieve services was developed in the JAWA language. This software was interfaced to a large GE (Mt Prospect, IL) Medical Systems clinical PACS at Northwestern Memorial Hospital (NMH).Conclusion: Images can be delivered from a clinical PACS for research and teaching purposes. Concerns for security, patient confidentiality, integrity of the PACS database, and management of the transactions can be addressed. The described software is one such solution for achieving this goal.
doi:10.1007/BF03190313
PMCID: PMC3452680  PMID: 11442070

Results 1-12 (12)