Informatics in radiology: An open-source and open-access cancer biomedical informatics grid annotation and image markup template builder.

In a routine clinical environment or clinical trial, a case report form or structured reporting template can be used to quickly generate uniform and consistent reports. Annotation and image markup (AIM), a project supported by the National Cancer Institute's cancer biomedical informatics grid, can be used to collect information for a case report form or structured reporting template. AIM is designed to store, in a single information source, (a) the description of pixel data with use of markups or graphical drawings placed on the image, (b) calculation results (which may or may not be directly related to the markups), and (c) supplemental information. To facilitate the creation of AIM annotations with data entry templates, an AIM template schema and an open-source template creation application were developed to assist clinicians, image researchers, and designers of clinical trials to quickly create a set of data collection items, thereby ultimately making image information more readily accessible.

Informatics in radiology: an information model of the DICOM standard.

The Digital Imaging and Communications in Medicine (DICOM) Standard is a key foundational technology for radiology. However, its complexity creates challenges for information system developers because the current DICOM specification requires human interpretation and is subject to nonstandard implementation. To address this problem, a formally sound and computationally accessible information model of the DICOM Standard was created. The DICOM Standard was modeled as an ontology, a machine-accessible and human-interpretable representation that may be viewed and manipulated by information-modeling tools. The DICOM Ontology includes a real-world model and a DICOM entity model. The real-world model describes patients, studies, images, and other features of medical imaging. The DICOM entity model describes connections between real-world entities and the classes that model the corresponding DICOM information entities. The DICOM Ontology was created to support the Cancer Biomedical Informatics Grid (caBIG) initiative, and it may be extended to encompass the entire DICOM Standard and serve as a foundation of medical imaging systems for research and patient care.

Mapping LIDC, RadLex™, and lung nodule image features.

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.

Automatic extraction of concepts to extend RadLex.

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.

The caBIG annotation and image Markup project.

Image annotation and markup are at the core of medical interpretation in both the clinical and the research setting. Digital medical images are managed with the DICOM standard format. While DICOM contains a large amount of meta-data about whom, where, and how the image was acquired, DICOM says little about the content or meaning of the pixel data. An image annotation is the explanatory or descriptive information about the pixel data of an image that is generated by a human or machine observer. An image markup is the graphical symbols placed over the image to depict an annotation. While DICOM is the standard for medical image acquisition, manipulation, transmission, storage, and display, there are no standards for image annotation and markup. Many systems expect annotation to be reported verbally, while markups are stored in graphical overlays or proprietary formats. This makes it difficult to extract and compute with both of them. The goal of the Annotation and Image Markup (AIM) project is to develop a mechanism, for modeling, capturing, and serializing image annotation and markup data that can be adopted as a standard by the medical imaging community. The AIM project produces both human- and machine-readable artifacts. This paper describes the AIM information model, schemas, software libraries, and tools so as to prepare researchers and developers for their use of AIM.

Toward best practices in radiology reporting.

The goals and current efforts of the Radiological Society of North America Radiology Reporting Committee are described. The committee's charter provides an opportunity to improve the organization, content, readability, and usefulness of the radiology report and to advance the efficiency and effectiveness of the reporting process.

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.

RadMonitor: radiology operations data mining in real time.

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.

Informatics in radiology (infoRAD): integrating MIRC-compliant semiautomated teaching files into PACS work flow.

A Medical Image Resource Center (MIRC)-compliant teaching file system was created that can be integrated into a picture archiving and communication system (PACS) environment. This system models the three-step process necessary for efficient teaching file creation in a PACS environment: (a) identifying and transferring a case quickly and easily during primary interpretation, (b) editing and authoring the case outside of primary interpretation time, and (c) publishing the case locally and via MIRC standard-based mechanisms. Images from interesting cases are e-mailed to the teaching file system from either the PACS workstation or the radiologist's personal computer. Notes and clinical information may be included in the e-mail text to prompt the recollection of case details. Images are automatically extracted from the e-mail and sent to an image repository, and text fields are captured in a database. The World Wide Web-based authoring component provides tools for final authoring of cases and for the manipulation of existing cases. Authors designate access levels for each case, which is then made available locally and, potentially, to the entire MIRC-compliant community. Although this application has not yet been implemented as a departmental solution, it promises to improve and streamline medical education and promote better patient care.

A semantic image annotation model to enable integrative translational research.

Integrating and relating images with clinical and molecular data is a crucial activity in translational research, but challenging because the information in images is not explicit in standard computer-accessible formats. We have developed an ontology-based representation of the semantic contents of radiology images called AIM (Annotation and Image Markup). AIM specifies the quantitative and qualitative content that researchers extract from images. The AIM ontology enables semantic image annotation and markup, specifying the entities and relations necessary to describe images. AIM annotations, represented as instances in the ontology, enable key use cases for images in translational research such as disease status assessment, query, and inter-observer variation analysis. AIM will enable ontology-based query and mining of images, and integration of images with data in other ontology-annotated bioinformatics databases. Our ultimate goal is to enable researchers to link images with related scientific data so they can learn the biological and physiological significance of the image content.

Computing human image annotation.

An image annotation is the explanatory or descriptive information about the pixel data of an image that is generated by a human (or machine) observer. An image markup is the graphical symbols placed over the image to depict an annotation. In the majority of current, clinical and research imaging practice, markup is captured in proprietary formats and annotations are referenced only in free text radiology reports. This makes these annotations difficult to query, retrieve and compute upon, hampering their integration into other data mining and analysis efforts. This paper describes the National Cancer Institute's Cancer Biomedical Informatics Grid's (caBIG) Annotation and Image Markup (AIM) project, focusing on how to use AIM to query for annotations. The AIM project delivers an information model for image annotation and markup. The model uses controlled terminologies for important concepts. All of the classes and attributes of the model have been harmonized with the other models and common data elements in use at the National Cancer Institute. The project also delivers XML schemata necessary to instantiate AIMs in XML as well as a software application for translating AIM XML into DICOM S/R and HL7 CDA. Large collections of AIM annotations can be built and then queried as Grid or Web services. Using the tools of the AIM project, image annotations and their markup can be captured and stored in human and machine readable formats. This enables the inclusion of human image observation and inference as part of larger data mining and analysis activities.

Automatic segmentation of clinical texts.

Clinical narratives, such as radiology and pathology reports, are commonly available in electronic form. However, they are also commonly entered and stored as free text. Knowledge of the structure of clinical narratives is necessary for enhancing the productivity of healthcare departments and facilitating research. This study attempts to automatically segment medical reports into semantic sections. Our goal is to develop a robust and scalable medical report segmentation system requiring minimum user input for efficient retrieval and extraction of information from free-text clinical narratives. Hand-crafted rules were used to automatically identify a high-confidence training set. This automatically created training dataset was later used to develop metrics and an algorithm that determines the semantic structure of the medical reports. A word-vector cosine similarity metric combined with several heuristics was used to classify each report sentence into one of several pre-defined semantic sections. This baseline algorithm achieved 79% accuracy. A Support Vector Machine (SVM) classifier trained on additional formatting and contextual features was able to achieve 90% accuracy. Plans for future work include developing a configurable system that could accommodate various medical report formatting and content standards.

BRISC-an open source pulmonary nodule image retrieval framework.

We have created a content-based image retrieval framework for computed tomography images of pulmonary nodules. When presented with a nodule image, the system retrieves images of similar nodules from a collection prepared by the Lung Image Database Consortium (LIDC). The system (1) extracts images of individual nodules from the LIDC collection based on LIDC expert annotations, (2) stores the extracted data in a flat XML database, (3) calculates a set of quantitative descriptors for each nodule that provide a high-level characterization of its texture, and (4) uses various measures to determine the similarity of two nodules and perform queries on a selected query nodule. Using our framework, we compared three feature extraction methods: Haralick co-occurrence, Gabor filters, and Markov random fields. Gabor and Markov descriptors perform better at retrieving similar nodules than do Haralick co-occurrence techniques, with best retrieval precisions in excess of 88%. Because the software we have developed and the reference images are both open source and publicly available they may be incorporated into both commercial and academic imaging workstations and extended by others in their research.

An ontology for PACS integration.

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.

Comparison of human observer performance of contrast-detail detection across multiple liquid crystal displays.

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.

Blogging your PACS.

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.

Validating DICOM content in a remote storage model.

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.

Integrating the healthcare enterprise: a primer. Part 6: the fellowship of IHE: year 4 additions and extensions.

Recently, one of my friends, a computer wizard, paid me a visit. As we were talking, I mentioned that I had recently installed a picture archiving and communication system and a radiology information system. I told him how happy I was with the system and showed him a compact disk (CD) from it. To my surprise, he threw it into my microwave oven and turned it on. Instantly I got very upset, because the System had become precious to me, but he said, "Do not worry, it is unharmed." After a few minutes, he took the CD out, gave it to me and said, "Take a close look at it." To my surprise, the CD was quite cold to hold and it seemed to be heavier than before. At first, I could not see anything, but on the inner edge of the central hole, I saw an inscription, an inscription finer than anything I had ever seen before. The inscription shone piercingly bright, and yet remote, as if out of a great depth: 12413AEB2ED4FA5E6F7D78E78BEDE8209450920F923A40Eel0E50CC98D444AA08E324. "I cannot understand the fiery letters," I said in a timid voice. "No, but I can," he said. "The letters are Hex, of an ancient mode, but the language is that of DICOM, which I shall not utter here. But in common English, this is what it says: Two integration profiles to schedule work flow. Five for radiology with room to grow. One for the bacon to bring it home. One for HIPAA all alone. And one for results for those who would know. One technical framework in which to find them. One technical framework to guide them. One technical framework to bring them all. And in the Connect-a-thon bind them. In the Land of Lincoln where no shadows are. We continue the saga of the fellowship of the IHE: clinicians, radiologists, informaticians, administrators, technologists, imaging system vendors, and non-imaging system vendors, as they begin their year 4 transactions.