Describe the important components to the PACS system and how it’s use has impacted comparison studies?

Chapter 16: Medical Imaging Informatics

Robert Hoyt MD

John Grizzard MD

Learning Objectives

After reviewing the presentation, viewers should be able to:

Describe the history behind digital radiology and the creation of picture archiving and communication systems (PACS)

Itemize the benefits of digital radiology to clinicians, patients and hospitals

List the challenges facing the adoption of picture archiving and communication systems

Describe the difference between computed and digital radiology

Outline the field of medical imaging informatics

Describe new imaging technologies such as Web PACS and mobile imaging viewers

Definitions
Medical Imaging Informatics: “study and application of processes of information and communications technology for the acquisition, manipulation, analysis and distribution of medical image data”
Society for Imaging Informatics in Medicine (SIIM)
Picture Archiving and Communication Systems (PACS): medical imaging technology which provides economical storage of, and convenient access to, images from multiple modalities

Introduction
Medical Imaging Informatics:
Could belong to Biomedical informatics or Radiology
Study of imaging, acquisition, storage, interpretation and sharing to improve patient care
Imaging data moves throughout medical enterprise and interacts with EHRs, voice recognition dictation systems, computer-aided diagnosis software, health information organizations, etc.
Important to have knowledge of workflow, networks, security, data quality, hardware and software

Digital imaging
Started in the 1970s
First filmless hospital occurred in 1999
Transitioning to PACS
Similar to photography (film to digital)
Introduction of computed tomography, ultrasound, and magnetic resonance imaging that all became digital
Eliminated need for film processing and storage rooms
Images could be viewed at a remote location
Advantages: cost savings, storage, retrieval

Transition to Filmless Radiology
Extensive initial costs
Printing remained for referring physicians
Use of film scanners for digital viewing
Proprietary imaging formats
Later, DICOM (DICOM = Digital Imaging and Communications in Medicine) standard was created
Upgrade conventional radiology rooms for CT, ultrasound, and MRI for the digital world
Computer-based image archiving

Transition to Filmless Radiology
Integrate PACS with EHRs, hospital information systems (HISs) and radiology information systems (RISs)
Veterans Health Administration launched a teleradiology network in 2007 to provide radiology coverage to all of its regions
Faster processors, higher capacity disk drives, higher resolution monitors, more robust hospital information systems, better servers and faster network speeds were necessary for the change to digital imaging

Picture Archiving and Communication Systems (PACS)
Full PACS
Images are processed from ultrasonography (US), magnetic resonance imaging (MRI), positron emission tomography (PET), computed tomography (CT), routine radiography and endoscopy
Mini-PACS
More limited and processes images from only one or two modalities

PACS Key Components

PACS Key Components
Digital acquisition devices: sources of images, such as digital angiography, fluoroscopy, mammography, CT, MRI , ultrasound scanners
Network: ties PACS components together
Database server: high speed and robust central computer to process information
Archival server: responsible for storing images and enables short term (fast retrieval) and long term (slower retrieval) storage
Radiology Information system (RIS): system that maintains patient demographics, scheduling, billing information and interpretations
Workstation: software and hardware to access PACS and replaces standard light box or view box
Teleradiology: ability to remotely view images at a location distant from the site of origin

Types of Digital Detectors
Computed radiography (CR)
After x-ray exposure to a special cassette, a laser reader scans the image and converts it to a digital image. The image is erased on the cassette so it can be used repeatedly
Digital radiography (DR)
Does not require an intermediate step of laser scanning

Typical PACS Workflow
Patient is identified in hospital information system (HIS)
An order is created that is sent to the radiology information system (RIS) via an HL7 protocol
Orders will go to imaging device via the DICOM protocol
Image is created in DICOM format and sent to the PACS server
Images are stored in image archive
Radiologist is notified of a pending study
Study is then read at a computer workstation using high-resolution monitors and viewing software available from a variety of different vendors
Comparison can be made to prior studies
Diagnostic report is generated by the radiologist, often using voice recognition software
Report is then stored on the PACS server

Web Based PACS
Reduce need for duplicate studies, and allow more rapid diagnosis and treatment
DICOM imaging format could be an impediment to use of the World Wide Web
Not browser compatible: Usually this entails downloading a small application (thin client) from the PACS vendor that enables the remote viewing station to act like a modified PACS workstation
Alternate potential solution: “zero-footprint” Web viewer where DICOM images are pre-converted to GIF
Legacy PACS compared to Web PACS in next slide

Legacy PACS Web PACS
Only available on computers with proper software installed Available anywhere with internet access
Upgrades must be manually installed Upgrades are done centrally or are not necessary
Multiple user interfaces One user interface
Difficult to integrate with health information exchanges Easy to integrate with health information exchanges
Difficult to link to multiple EHRs Easier to link to EHRs
Labor intensive for PACS administrator for maintenance and training Much less labor intensive for maintenance and training
Could involve multiple operating systems One operating system
Less likely to be standards-based Utilizes JPEG compression, DICOM, HL7 and IHE profiles

Legacy vs. Web PACS

PACS and Mobile Technology
Until 2011, FDA prohibited physicians from using radiology images displayed on mobile devices to make an official diagnoses
Mobile MIM: Includes a VueMe version for patients
ResolutionMD Mobile is a medical imaging diagnostic application for radiologists. Their server-based software application allows physicians immediate access to the display, reports, and analysis of patient images such as CT and MR, stored within any healthcare facility, and to submit a clinical diagnosis via their medical devices
OsiriX Mobile DICOM Viewer is a free PACS open source viewer for the MAC operating system

PACS for a Hospital Desktop Features
Zoom-in feature for close-up detail
Ability to rotate images in any direction
Text button to see the report
Mark-up tool that does the following to the image: Adds text, measures the size and ratios of objects
Measures angles
Measures the square area of a mass or region
Adds an arrow
Right click on the image and short cut tools appear
Export an image to any of the following destinations: Teaching file,
CD-ROM, hard drive, USB drive, save to clipboard or Create a video

PACS Advantages
Replaces a standard x-ray film archive
Allows for remote viewing and reporting
Expedites the incorporation of medical images into an electronic health record
Images can be archived and transported on portable media, e.g. USB drive and Apple’s iPhone
Other specialties that generate images may join PACS such as cardiologists
PACS can be web-based and use “service oriented architecture”
Unlike conventional x-rays, digital films have a zoom feature and can be manipulated in innumerable ways
Improves productivity by allowing multiple clinicians to view the same image from different locations
Rapid retrieval of digital images for interpretation and comparison with previous studies

PACS Advantages
Fewer “lost films”
Reports are more likely to accompany the digital image
Radiologists can view an image back and forth like a movie, known as “stack mode”
Quicker reporting back to the requesting clinician
Digital imaging allows for computer aided detection (CAD)
Increased productivity

PACS Disadvantages
Cost: Open source and “rental PACS” are alternatives
New legislation cutting reimbursement rates
Expense and complexity to integrate with hospital and radiology information systems and EHRs
Lack of interoperability with other PACSs
Bandwidth limits may require network upgrades
Different vendors may use different DICOM tags to label films
Viewing digital images a little slower than routine x-ray films
Workstations may require upgrades if high resolution monitors are necessary

Moving Forward
Stage 2 Meaningful Use required both eligible professionals and hospitals to incorporate (or make accessible) through their electronic health records more than 10% of images ordered
Trend towards web based PACS
PACS is greatly accepted by clinicians

Fellowship in imaging informatics
Certificate in imaging informatics. Requirements can be found in the textbook
Imaging Informatics Education

PACS is the logical progression from x-ray films to digital imaging due to multiple new technologies
Medical Imaging Informatics will study the impact and significance of all facets of digital imaging
PACS is very popular among clinicians, patients and hospitals, but cost remains an issue
Web PACS offers more image interoperability options
Conclusions

Chapter 15: Information Retrieval from Medical Knowledge Resources

WILLIAM R. HERSH

Learning Objectives

After viewing this presentation, viewers should be able to:

Enumerate the basic biomedical and health knowledge resources in books, journals, electronic databases, and other sources

Describe the major approaches used to indexing knowledge-based content

Apply advanced searching techniques to the major biomedical and health knowledge resources

Discuss the major results of information retrieval evaluation studies

Describe future directions for research in information retrieval

Introduction
Information Retrieval (IR), sometimes called search, concerns the acquisition, organization, and searching of knowledge-based information, which is usually defined as information derived and organized from obser­vational or experimental research
Although IR in biomedicine traditionally concentrated on the retrieval of text from the biomedical liter­ature, the study has expanded to include newer types of media that include images, video, chemical structures, gene and protein sequences, and a wide range of other digital media of relevance to biomedical education, research, and patient care

Introduction
The overall goal of the IR process is to find content that meets a person’s information needs
Components of information retrieval systems

IR tends to focus on knowledge-based information
Knowledge-based information categories:
Primary knowl­edge–based information (also called primary literature) is original research that appears in journals, books, reports, and other sources
Secondary knowledge–based information consists of the writing that reviews, condenses, and/or synthesizes the primary literature. The most com­mon examples of this type of literature are books, monographs, and review articles in journals and other publications
Knowledge Based Information

Virtually all scientific journals are published electronically
Not only is there the increased con­venience of redistributing articles, but research has found that freely available on the Web have a higher likelihood of being cited by other papers than those that are not (Bork 2012)
Printing and mailing, tasks no longer needed in electronic publishing, comprised a sig­nificant part of the “added value” from publishers of journals. There is still however value added by publishers, such as hiring and managing editorial staff to produce the journals and managing the peer review process
Publication of Knowledge-Based Information

The basic principle of open access publishing is that authors and/or their institutions pay the cost of production of manuscripts up front after they are accepted through a peer review process. After the paper is published, it becomes freely available on the Web. Since most research is usually funded by grants, the cost of open access publishing should be included in grant budgets. The uptake of publishers adhering to the open access model has been modest, with the most prominent being Biomed Central (BMC, www.biomedcentral.com ) and the Public Library of Science ( PLoS, www.plos.org )
Publishing Costs and Open Access

Information content is classified in four categories:
Bibliographic: the best-known and most widely used biomedical bibliographic database is MEDLINE, which contains bibliographic references to all the biomedical articles, editorials, and letters to the editors in approximately 5,000 scientific journals
Full-text content: a large component of this content con­sists of the online versions of books and periodicals. As already noted, most traditionally paper-based medical literature, from textbooks to journals, is now available electronically
Content

Annotated content: these resources are usually not stored as freestanding Web pages but instead are often housed in database management systems
Aggregated content: Aggregated content has been developed for all types of users from consumers to clinicians to scientists. Probably the largest aggregated consumer information resource is MedlinePlus ( http://medlineplus.gov ) from the NLM. MedlinePlus includes all the types of content previously described, aggregated for easy access to a given topic
Content

Indexing is the process of assigning metadata to content to facilitate its retrieval. Most modern commercial content is indexed in two ways:
Manual indexing—where human indexers, usually using a controlled terminology, assign indexing terms and attributes to documents, often following a specific protocol
Automated indexing—where computers make the indexing assignments, usually lim­ited to breaking out each word in the document (or part of the document) as an indexing term
Indexing

A controlled terminology contains a set of terms that can be applied to a task, such as indexing
When the terminology defines the terms, it is usually called a vocabulary
When the terminology contains variants or synonyms of terms, it is also called a thesaurus
Controlled Terminologies

A controlled terminology usually contains a list of terms that are the canonical repre­sentations of the concepts. If it is a thesaurus, it contains relationships between terms, which typically fall into three categories:
Hierarchical—terms that are broader or narrower. The hierarchical organization not only provides an overview of the structure of a thesaurus but also can be used to enhance searching (e.g., MeSH tree explosions that add terms from an entire portion of the hierarchy to augment a search)
Synonym—terms that are synonyms, allowing the indexer or searcher to express a concept in different words
Related—terms that are not synonymous or hierarchical but are somehow otherwise related. These usually remind the searcher of different but related terms that may enhance a search
Controlled Terminologies

The MeSH terminology is used to manually index most of the databases produced by the NLM
The latest version contains over 26,000 subject headings
MeSH contains the three types of relationships described in the previous slide:
Hierarchical—MeSH is organized hierarchically into 16 trees, such as Diseases, Organisms, and Chemicals and Drugs
Synonym—MeSH contains a vast number of entry terms, which are synonyms of the headings
Related—terms that may be useful for searchers to add to their searches when appro­priate are suggested for many headings
Medical Subject Headings (MeSH)

The MeSH terminology files, their associated data, and their supporting documentation are available on the NLM’s MeSH Web site http://www.nlm.nih.gov/mesh
Medical Subject Headings (MeSH)
“Slice” through MeSH hierarchy

Manual indexing is most commonly done for bibliographic and annotated content, although it is sometimes for other types of content as well
While most Web content is indexed automatically (see next slide), one approach to manual indexing has been to apply metadata to Web pages and sites, exemplified by the Dublin Core Metadata Initiative (DCMI, www.dublincore.org )
Manuel Indexing

The goal of the DCMI has been to develop a set of standard data elements that creators of Web resources can use to apply metadata to their content
DCMI standard has been approved by the National Information Standards Organization and the International Organization of Standards specification has 15 defined elements and sample elements include:
DC.title – name given to the resource
DC.creator – person or organization primarily responsible for creating the intellectual content of the resource
DC.subject – topic of the resource
DC.description – a textual description of the content of the resource
DC.publisher – entity responsible for making the resource available in its present form
DC.date – date associated with the creation or availability of the resource
Manuel Indexing

In automated indexing, the indexing is done by a computer
We will focus on the auto­mated indexing used in operational IR systems, namely the indexing of documents by the words they contain
Word indexing is typically done by defining all consecutive alphanumeric sequences between white space (which consists of spaces, punctuation, carriage returns, and other non-alphanumeric characters) as words. Systems must take particular care to apply the same process to documents and the user’s query, especially with characters such as hyphens and apostrophes
Automated Indexing

A commonly used approach for term weighting is TF*IDF weighting, which com­bines the inverse document frequency (IDF) and term frequency (TF).
The usual formula is:
Automated Indexing

Synonymy—different words may have the same meaning, such as high and elevated. This problem may extend to the level of phrases with no words in common, such as the synonyms hypertension and high blood pressure
Polysemy—the same word may have different meanings or senses. For example, the word lead can refer to an element or to a part of an electrocardiogram machine
Content—words in a document may not reflect its focus. For example, an article describing hypertension may make mention in passing to other concepts, such as congestive heart failure (CHF) that are not the focus of the article
Context—words take on meaning based on other words around them
Morphology—words can have suffixes that do not change the underlying meaning, such as indicators of plurals, various participles, adjectival forms of nouns, and nominalized forms of adjectives
Granularity—queries and documents may describe concepts at different levels of a hier­archy. A query for antibiotics for treatment of a specific infection returns documents that only contain specific antibiotics
Automated Indexing Limitations

Exact-Match Retrieval- In exact-match searching, the IR system gives the user all documents that exactly match the criteria specified in the search statement(s). This type of searching is often called Boolean searching
Retrieval
Boolean operators

Partial-Match Retrieval-Although partial-match searching was conceptualized very early, it did not see wide­spread use in IR systems until the advent of Web search engines in the 1990s
The most common approach to document ranking in partial-match searching is to give each a score based on the sum of the weights of terms common to the document and query
Retrieval

There are many different retrieval interfaces, with some of the features reflecting the content or structure of the underlying database
PubMed is the system at NLM that searches MEDLINE and other bibliographic databases
Retrieval Systems

There has been a great deal of research over the years devoted to evaluation of IR sys­tems.
One of those frameworks organized evaluation around six questions that someone advocating the use of IR systems might ask (Hersh 1998):
Was the system used?
For what was the system used?
Were the users satisfied?
How well did they use the system?
What factors were associated with successful or unsuccessful use of the system?
Did the system have an impact?
Evaluation

There are many ways to evaluate the performance of IR systems, the most widely used of which are the relevance-based measures of recall and precision
Recall is the pro­portion of relevant documents retrieved from the database:

In other words, recall answers the question, for a given search, what fraction of all the relevant documents have been obtained from the database?

System-Oriented Evaluation

Precision is the proportion of relevant documents retrieved in the search:

This measure answers the question, for a search, what fraction of the retrieved docu­ments are relevant?
One problem that arises when one is comparing systems that use ranking versus those that do not is that non-ranking systems, typically using Boolean searching, tend to retrieve a fixed set of documents and as a result have fixed points of recall and preci­sion
System-Oriented Evaluation

A number of user-oriented evaluations have been performed over the years looking at users of biomedical information. Most of these studies have focused on clinicians
For example, Hersh et al studied in 1995 using the task-oriented approach compared Boolean ver­sus natural language searching in the textbook Scientific American Medicine
There are more studies listed in the textbook Chapter 15
User-Oriented Evaluation

Research taking place in several areas related to IR include:
Information extraction and text mining—usually through the use of natural language processing (NLP) to extract facts and knowledge from text
Summarization—Providing automated extracts or abstracts summarizing the content of longer documents
Question-answering—Going beyond retrieval of documents to providing actual answers to questions, as exemplified by the IBM Corp. Watson system, which is being applied to medicine (Ferrucci 2010)

Future Directions

There are many biomedical and health knowledge resources online available in bibliographic databases, journals and other full-text resources, Web sites, and other sources
Bibliographic content is likely to be indexed using controlled vocabularies assigned by humans
Full-text and other resources are likely to be indexed via extraction of words
The major approaches to searching biomedical and health knowledge resources include exact-match searching using sets and Boolean operators and partial-match searching on words using relevance ranking
System-oriented evaluation studies tend to focus on performance of search systems and usually involvement measurement of the relevance-based measures of recall and precision
User-oriented evaluation studies tend to compare users and their abilities to complete tasks using retrieval systems
Conclusions