PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of digimagwww.springer.comThis JournalToc AlertsSubmit OnlineOpen Choice
 
J Digit Imaging. Dec 2004; 17(4): 235–243.
Published online Nov 25, 2004. doi:  10.1007/s10278-004-1027-1
PMCID: PMC3047184
Addressing the Coming Radiology Crisis—The Society for Computer Applications in Radiology Transforming the Radiological Interpretation Process (TRIP™) Initiative
Katherine P. Andriole, PhDcorresponding author and and the SCAR TRIP™ Subcommittee: Richard L. Morin, PhD, Chair, TRIP™ Subcommittee, Ronald L. Arenson, MD, John A. Carrino, MD, MPH, Bradley J. Erickson, MD, PhD, Steven C. Horii, MD, David W. Piraino, MD, Bruce I. Reiner, MD, J. Anthony Seibert, PhD, Eliot Siegel, MD
Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02120
corresponding authorCorresponding author.
Received July 20, 2004; Accepted August 13, 2004.
The Society for Computer Applications in Radiology (SCAR) Transforming the Radiological Interpretation Process (TRIP™) Initiative aims to spearhead research, education, and discovery of innovative solutions to address the problem of information and image data overload. The initiative will foster interdisciplinary research on technological, environmental and human factors to better manage and exploit the massive amounts of data. TRIP™ will focus on the following basic objectives: improving the efficiency of interpretation of large data sets, improving the timeliness and effectiveness of communication, and decreasing medical errors. The ultimate goal of the initiative is to improve the quality and safety of patient care. Interdisciplinary research into several broad areas will be necessary to make progress in managing the ever-increasing volume of data. The six concepts involved are human perception, image processing and computer-aided detection (CAD), visualization, navigation and usability, databases and integration, and evaluation and validation of methods and performance. The result of this transformation will affect several key processes in radiology, including image interpretation; communication of imaging results; workflow and efficiency within the health care enterprise; diagnostic accuracy and a reduction in medical errors; and, ultimately, the overall quality of care.
Keywords: large data sets, radiological image interpretation paradigm
THE SOCIETY FOR COMPUTER APPLICATIONS IN RADIOLOGY (SCAR) TRIP™ Initiative grew out of discussions at a SCAR Research and Development (R&D) Committee retreat on July 12, 2002. Members began examining the increasing problem of the number of images making up current medical studies, the number of studies associated with each patient, and the number of patients seen per day in current electronic radiology practices. The discussions concluded that a paradigm shift in the radiological interpretation process will be necessary to carry out medical imaging in the health care environments of the future. At this retreat, the Medical Image Interpretation Paradigm Shift Subcommittee was formed by the R&D Committee to examine this issue of information and image data overload, and to provide a forum for discussion as well as an organizational infrastructure to seek solutions to this impending crisis. At a second retreat, held in February of 2003, activities of the effort were refined, and the initiative was renamed Transforming the Radiological Interpretation Process (TRIP™).
The SCAR TRIP™ Initiative aims to spearhead research, education, and discovery of innovative solutions to address the problem of information and image data overload. The initiative will foster interdisciplinary research on technological, environmental and human factors to better manage and exploit the massive amounts of data. The TRIP™ Initiative will focus on the following basic objectives:
  • Improving the efficiency of interpretation of large data sets;
  • Improving the timeliness and effectiveness of communication; and
  • Decreasing medical errors.
The ultimate goal of the initiative is to improve the quality and safety of patient care.
It is the fundamental belief of the SCAR leadership that the current impending crisis in image data overload provides not only a problematic challenge but a wonderful opportunity to change the radiological interpretation process, improving both the quality of patient care and the efficiency of future radiologists and their electronic practice.
This article is intended to outline the historical background of today’s information overload, assess the literature that addresses challenges involved in finding solutions, provide specific indications of areas in which these solutions may lie, and briefly describe SCAR’s efforts at identifying both immediate and long-term answers to the most pressing questions that will face radiology in the coming decades.
HISTORICAL PERSPECTIVE:
Image overload may be the single biggest challenge to effective, state-of-the-art practice in the delivery of consistent and well-planned radiological services in health care today. Although this appears to be a relatively recent phenomenon, resulting from the intersection of computing power and rapidly developing clinical modalities in the 1980 s and 1990 s, the problem is not entirely new. Those who see image management as a very recent difficulty err by more than a century. When Wilhelm Röntgen’s announcement of the “amazing new ray that could see through living human flesh” was sent out across Europe and North America in the first weeks of 1896, physicists, physicians, and even amateur photographers began experimenting with ways to capture the results on permanent images. More quickly than any new technique then or since, roentgenology (later radiology) would become an established part of health care. By 1898, many hospitals had fledgling x-ray departments, and specialized journals soon appeared to address topics of concern in the developing field. These included problems with referring physicians, expanding areas of focus within the body, new imaging techniques, and the difficulties of image management: the same topics that dominate today’s radiology literature. These problems today, however, will require very different, innovative interdisciplinary imaging informatics solutions.
Among the earliest and most serious problems with managing the new roentgenographic images were mass and volume. The thick glass plates on which images were developed were heavy, and their processing required a volatile mix of chemicals. Although the earliest images “spoke for themselves”—foreign bodies, fractures, and even novelty shots—more complex requests often required five or more “views.” X-ray departments and freestanding “x-ray clinics” soon found themselves overrun with requests from physicians who were fascinated by the new technology and its potential to answer longstanding questions and open new lines of inquiry in their disciplines. When every image required 10 or 15 minutes to develop and often as long to interpret, it is clear why the number-one complaint in many x-ray departments at the turn of the 20th century was too many images, too little time.
One answer to the problem was fluoroscopy—the original “real-time” imaging. It offered not only the opportunity to render diagnoses on the spot but also afforded radiologists and referring physicians the chance to “look around”—a new luxury in medicine. But this new way of “navigating” caused its own problems. Although fluoroscopy offered immediacy, referring physicians, surgeons preparing for procedures, and sometimes even patients themselves all wanted permanent images—in part because it was already clear that what one person “saw” on an x-ray image might not be what the next practitioner saw.
By the beginning of World War I and the advent of film to replace the cumbersome glass plates, all the elements of the present-day image overload challenges were in place and were openly discussed in the literature: how to solve variations in human perception, how to effectively and reliably process images, how to “navigate” through the data (with the human body as a master set of potential images), how to define quality, how to store the already massive accumulations of images and reports, how to disseminate this information to others both in and outside the hospital, how to integrate this data on an ongoing basis into patient care and research, and how to reconcile the finite human resources in the radiology department with a workload that seemed to be escalating exponentially.
Throughout the 20th century, radiologists wrestled with and met these challenges in creative ways. However, their tactics went almost exclusively into putting more personnel power and funds into film processing and interpretation. Only rarely were technological developments in radiological capabilities accompanied by innovations that provided even short-term solutions to the problems of growing numbers of images, procedures, and patients.
By the 1930 s, a host of new contrast studies and a rapidly expanding range of surgical procedures had become routine, even in rural hospitals. A new generation of radiologists found themselves fighting the insistence of many hospital administrators that the growing image overload could be dealt with simply by allowing nonphysicians or the referring specialists themselves to read the films. This invitation to cede all of the specialty’s turf was avoided by perhaps the most unexpected factor in radiology’s great success in the second half of the century: the effects of World War II.
Battlefield trauma and routine military care during the war demanded more imaging specialists than the United States could provide. Young physicians who had intended to specialize in internal medicine or pediatrics found themselves assigned to the Army’s short course in roentgenology. After the war, many were “grandfathered” into certification, and the ranks of practicing radiologists in the U.S. nearly doubled in the 1950 s. In effect, the requirements of mass and volume in image management were met with mass and volume in radiologists.
In the ensuing decades, new factors would be added to the difficult balance between volume of studies and available personnel. New modalities, including ultrasound, computed tomography, and nuclear medicine would change the meaning of “training” in the field of medical imaging. The modalities themselves changed rapidly, and each required specific sets of interpretive skills. To some extent, the difficulties caused by this growing diversity of ways to look at the human body were resolved by specialization. Ultrasound specialists did not interpret thyroid scans; nuclear medicine specialists did not interpret mammograms, and so on.
The advent of the microchip—both in imaging and information processing technologies—changed the landscape of medical imaging forever. The problems of mass and volume skyrocketed, with new technologies that could provide hundreds of views as easily as one. Today new computer-enabled technologies that can image anatomy and function down to the cellular level have blurred the boundaries among imaging disciplines and between radiology and other image-intensive fields such as cellular biology, biochemistry, and pathology.
An informal study of the number of images acquired in the Department of Radiology at the Mayo Clinic Jacksonville, FL, determined that roughly 1,500 images were generated and stored per day in 1994. In that same practice in 2002, an average of 16,000 images were acquired each day. By extrapolating these volumes to the year 2006 with a similar increase in the number of images per study, the estimates conclude that approximately 80,000 images will be acquired per day. Assuming a radiologist can view approximately one image per second, the number of images generated in the future will require 22.2 hours per day to interpret using today’s practice strategies. Clearly an image interpretation paradigm shift will be necessary for radiologists to perform their work effectively, efficiently, and accurately.
The aeronautics industry uses the term “beyond the limits” to describe the point at which the computerized elements of a plane are too complex for simple human control: the point at which the computer and the human must find a new way to work effectively to handle the vast amounts of data being processed. New technologies have taken the tools of medical imaging “beyond the limits.” Radiologists today sit at the controls of their increasingly “difficult-to-fly” workstations facing a challenge in imaging and information overload that transcends the traditional quick fixes of more personnel or more time.
We find ourselves, after more than 100 years of dealing with image overload in a variety of ways, at a turning point. Technology will not slow down—either in the range of innovations that provide medical promise or in the amount of data that we must sift through to make difficult and informed decisions. The challenge is to develop a completely new paradigm for looking at information and image data overload—a process that will be as revolutionary in our own time as Röntgen’s way of looking into the living human body was a century ago. It is a challenge that will require creative thinkers from a number of fields, a synergistic effort that the Society for Computer Applications in Radiology is calling Transforming the Radiological Interpretation Process, or TRIP™. At the end of this TRIP™—which may prove long and demanding—is the promise of a new way of approaching the growing amounts of information available to us and of keeping pace with innovations that promise new horizons in medical imaging and positive healthcare outcomes.
A search of the recent literature was performed to assess the availability and quality of published materials on the effects of growing amounts of image data on interpretation, management, and the general delivery of timely radiological diagnoses, as well as to determine whether viable solutions to these challenges had been instituted or proposed. The results of the search were both disappointing and revealing in that the topic of growing amounts of image data was frequently discussed, but rarely accompanied by proposed solutions. A number of concepts stood out, however, pointing toward specific questions that will need to be addressed in finding 21st-century solutions to the coming crisis in medical image management. These broad topics include research in human perception of images, design of radiology workstations, and enhancing visualization through image processing.
Image Perception
The interplay among the radiologist’s eye, the inherent knowledge or context obtained through training and experience, the physical circumstances of the viewing, and the quality of the image have long been recognized as sources of tremendous variability in diagnostic image interpretation. In 1960, Tuddenham1 summarized for the referring physician readership of the New York State Medical Journal a range of potential problems associated with variability in image perception and interpretation. By the late 1980 s and into the 1990 s, a number of observers recognized that newly introduced algorithms for contrast enhancement, edge sharpening, noise smoothing, and dynamic range manipulation could provide additional information that could augment the interpretation process.2, 3, 4, 5, 6, 7
Computerized enhancements were being incorporated into a range of imaging modalities. But the methods lacked consistency across modalities and among manufacturers, meaning that radiologists often faced a changing landscape that might or might not suit their actual clinical needs. More importantly, little forethought was given to precisely which properties of radiological images were most useful in their interpretation or how these properties could be enhanced to improve diagnostic accuracy. How, for example, might color be added without distorting anatomical features? With these uncertainties left unaddressed, many radiologists gave a less-than-enthusiastic reception to image enhancement innovations.
Enthusiasts who had hoped that artificial intelligence would provide computerized consultants to assist with difficult diagnostic problems and computer vision systems to detect abnormalities in complex images were disappointed at the lack of advancement or acceptance of such tools at the clinical level. A few individuals proposed the development of less demanding computer aids that build on both human and machine capabilities.8,9
In the mid-1990 s, Zonneveld and colleagues published a series of articles that surveyed the past decade’s developments in three-dimensional imaging,10, 11, 12 noting that what was at the time primarily a research tool was sure to gain clinical acceptance. The group identified three key areas that should be assessed before the number of images and the variety of ways to display them became unmanageable. The areas included image analysis, surgical navigation, and stereoscopic and volumetric display.
In 1995, H.L. Kundel13 proposed that the only solution to the snowballing problems of image perception in a changing digital landscape was to think proactively about the information needed to find a solution. He suggested five priorities for research in the area:
  • Develop psychophysical models for the detection of abnormalities in medical images;
  • Improve understanding of the mechanisms of perception as they apply to medical images;
  • Develop aids for enhancing perception by use of approaches that provide interactions between vision and display;
  • Study perceptually acceptable alternatives to sequential slices for viewing images from cross-sectional imaging examinations; and
  • Perform methodological research aimed at improving the evaluation of medical imaging systems, and alternatives to standard methods for measuring observer performance.
Although these and similar recommendations have been widely discussed14,15 and a number of research studies have been completed, few researchers have gone on to propose methods by which the data derived can be used to support new approaches to digital image perception.
E. A. Krupinski has written extensively on the topic of image perception as it is affected and enhanced by digital image acquisition and associated computer driven softcopy displays.16, 17, 18 Her research into the topic has emphasized the need for perceptually based standards for image quality based on quantifiable data that define the optimal presentation parameters (e.g., size, luminance levels, and spatial and contrast resolution). Her work has also confirmed some of the problems inherent in current computerized images, including decreased viewing time with specially processed images and decreased use of functions such as window and level, zoom, and other tools. She has pointed out that, although image enhancement and computer-aided diagnosis show great promise, they must be accompanied by experience-based standards and user-targeted innovations, such as linearized perception models that will improve workflow and increase diagnostic accuracy.
Workstation Design
One way of addressing the challenges posed by image overload is to look at the physical interface between the radiologist and the image and associated data—that is, the workstation. A considerable body of literature exists, much of it based on the experience of specific groups of radiologists in academic and private practice settings. In 1990, Beard19 laid the groundwork for many ensuing discussions of workstation design when he described a study performed at the University of North Carolina. That study directly addressed issues of navigation, evaluation techniques, and the physical displays themselves.
In the same year, Arenson and colleagues considered the psychophysics, physics, and engineering of the radiology workstation20 with a focus on the fundamental perception of contrast in detail and on human perception of findings in medical images. The authors also discussed possible effects of workstation variability on physician workflow and imaging practice. Other authors have reviewed the existing literature and studies on interactive workstation configuration and assessed the general requirements for spatial resolution, contrast resolution, image processing, user software, and architecture for a number of modalities.21
Image Processing
The use of digital image processing to optimize the appearance of images has received extensive coverage in the literature, particularly as applied to images in other fields outside medicine. A number of studies applied specifically to medical imaging evaluated the accuracy of interpretation using images processed by different irreversible compression techniques. Although several of these point to specific solutions, such as wavelet compression to reduce file size and optimize data delivery and storage, most investigators also agree that the central problem of radiology data volume is that it continues to grow faster than storage space and network speed capabilities,22,23 and that ultimately image processing and analysis will be needed to optimize the delivery and appearance of the image for medical diagnosis.
In addition, increasing image detail and contrast at acquisition is reaching a point of diminishing returns. Many radiological abnormalities are in fact recorded on the image with today’s technologies but are not perceived by the observer. Processing must be applied to optimize recognition of these abnormalities. Further, most medical imaging technologies today take into account only one of a variety of attributes by which objects can be recognized in an image, attributes such as grayscale, color, texture, relative motion, and depth. Image displays using multiple simultaneous attributes should be evaluated for their potential effectiveness.
Reviewing the Review
This review of the literature continues to raise several philosophical questions that ultimately will affect the future practice of medical imaging. How will the nature of the human–machine interface change to address the coming crisis in image data overload? Will more routine chores be relinquished to the computer, allowing the physician to perform the more difficult tasks that require judgment and comprehension? Might the radiologist’s workstation of the future incorporate simple computer-based aids to help the radiologist read more effectively and better manage the increasingly complex choices of imaging parameters? Will workstations support adequate monitoring and supervision of machine reading? How will imaging informatics concepts be incorporated to support the radiologist’s expanding functions in teaching and research? Radiology needs answers to these questions to deal effectively with the coming crisis.
In June 2003, at its annual meeting in Boston, SCAR launched the TRIP™ Initiative, the first multi-disciplinary effort to address the issues raised by the information and image data overload in healthcare. This initiative is based on the belief that radiology must undergo a paradigm shift in its image interpretation and management processes to deal with the burgeoning medical image data sets acquired by digital imaging devices. This transformation must occur to improve the efficiency and accuracy of medical imaging services provided in today’s health care environment and those of the future. SCAR aims to foster interdisciplinary informatics research in the areas of technological, environmental, and human factors that affect medical imaging and the crisis of data overload.
Medical images are different from images acquired and used in other fields, and the solutions to data overload in other applications may not be transferable to the clinical environment. Medical images arise from and are used in a special and challenging environment that requires urgent delivery and accuracy of results. Safety limitations and restrictions on data acquisition, a high cost of error, and tremendous variability in human data within and between individuals all make working with medical images very complex. Moreover, it is difficult to validate performance in the clinical arena, and the relationship between human perception and the art of practicing medicine is poorly understood. Medicine has cultural and practical barriers against adopting new technologies in general, particularly when it is difficult to see the clinical impact at the onset. And the interdisciplinary nature of the solution may create additional hurdles that will slow progress.
However, several enabling technologies encourage a solution to these problems at the present time. Computing and networking capabilities provide real-time processing, increased bandwidth, and ubiquitous access to computing resources. Visualization hardware and software technologies now allow realistic three-dimensional (3-D) rendering of image representations using color and motion. Digital imaging modalities today can acquire true 3-D data sets with isotropic voxels, thus improving 3-D display representations. Finally, graphical user interfaces are becoming more intuitive, although much more development remains to be done.
The following sections outline the specific challenges in research that medical imaging experts must confront in the near future if both immediate and ongoing solutions are to be applied to the coming crisis in radiology.
Interdisciplinary research into several broad areas will be necessary to make progress in managing the ever-increasing volume of data. The six concepts involved include: human perception, image processing and computer-aided detection (CAD), visualization, navigation and usability, databases and integration, and evaluation and validation of methods and performance.
  • Human perception research will be required to develop a standard for image quality as well as for display. Objective methodologies for optimal image presentation and criteria from which to determine optimal presentation parameters will need to be developed based on diagnostic performance measures. Psychophysical models for detection of abnormalities will need to be defined by understanding what is desired by an image observer, what properties of radiological images are most useful in their interpretation, and how these properties can be enhanced to improve accuracy of interpretation.
  • Image processing and computer-aided detection research will be needed to develop computer aids for feature perception and to design the radiology workstation of the future, focusing on the human–machine interface. Future radiology display applications will have to implement computer aids, such as cueing, overlays, and annotation, into a broadly supportive workstation. Assists, including decision support, simple reminder techniques to help avert errors of omission, data mining capabilities, and access to reference libraries, will be incorporated. Human–machine systems for image-based diagnosis will need to take advantage of both human and machine capabilities, creating a system that as a whole will be greater than the sum of its parts.
  • Visualization of medical data has been gradually transformed with the advent of digital imaging and the maturation of computer tools. Medical images were originally displayed in static form on film. This process migrated to dynamic display with softcopy viewing of digital images. Image manipulation, including modification of contrast and brightness, magnification, and different presentation formats became possible. Interpretation of cross-sectional modalities evolved from viewing images tiled across the monitor, to stack or cine modes, to linked stack mode for 3-D correspondence, all of which have now become routine visualization methods. Newer visualization techniques include multi-modality image fusion, maximum intensity projections, multiplanar reconstructions, 3-D surface and volume renderings, and virtual reality representations. Exploration of new visualization techniques, perhaps adapted from other fields such as the entertainment industry, could prove useful in medical imaging.
  • Navigation and usability tools in medical imaging include increasing use of 3-D and motion, virtual reality fly-throughs, and hand–eye cueing instruments. Hand-held devices will become more widespread for more efficient point-of-care delivery of information. Context-matching and voice-activated tools may also be further developed for the clinical arena.
  • Databases and integration advances will be essential and may require open standards, continued evolution of the American College of Radiology–National Electrical Manufacturers Association Digital Imaging and Communications in Medicine standard, and increased adoption of the framework Integrating the Healthcare Enterprise (IHE) in imaging systems. Greater acceptance of IHE concepts will facilitate the integration of hospital and radiology information systems with picture archiving and communication systems (PACS) as well as speech-recognition systems. Real-time image processing at the PACS display and Web-based systems will be possible through better integration and database richness.
  • Evaluation and validation will involve developing objective methodologies for radiological imaging as well as standard data sets for testing, comparison, and collaborative research.
For progress in the transformation of the radiological interpretation process to be made, the interplay of concepts from each of the aforementioned areas of research will need to be explored. The result of this transformation will affect several key processes in radiology, including image interpretation; communication of imaging results; workflow and efficiency within the health care enterprise; diagnostic accuracy and a reduction in medical errors; and, ultimately, the overall quality of care.
In an arena in which everyone agrees a crisis is approaching, SCAR leadership and members identified specific areas in which the Society will focus its efforts:
  • Defining the problem. Few data are available on the volume of images read by a “typical” imaging specialist, on the specific challenges that growing image loads bring to different modalities and subspecialities, or on likely changes in the near future.
  • Identifying desirable outcomes. Perhaps the greatest deficiency in dealing with computerized imaging has been the tendency of practitioners to react on an ad hoc basis to innovations rather than working collaboratively on techniques and tools that will provide useful and usable solutions to everyday clinical problems.
  • Building effective bridges among participants in digital imaging. End users in radiology have been separated from researchers, industry, and from those in other medical and nonmedical fields who are faced with similar data overload challenges.
  • Sponsoring and encouraging research that provides durable solutions. Only research that arises from a solid understanding of long-term needs of radiologists, patients, and information systems can hope to provide durable solutions to the coming crisis in radiology.
  • Communicating urgent issues and new results. No single information source currently exists in which the wide, multidisciplinary audience interested in these issues can communicate.
Each of these areas to be addressed will lead to specific action items to be identified in collaboration with other interested organizations and disciplines. A few directions for immediate action and research have been identified.
Through the TRIP™ initiative, SCAR will accomplish the following:
  • Continue to work with its members and with other radiology groups and associations to bring forward the problem and to facilitate exchanges of ideas among radiology researchers, endusers, and industry through workshops, forums, and special presentations.
  • Leverage the power and experience of individuals and professions in other fields (such as aerospace science, geology, and the entertainment industry) who are facing the same crisis in image data overload.
  • Support research that is forward thinking and that works to identify where radiology would like to be technologically in 10 or 20 years, rather than making short-term, serial adjustments on an ad hoc basis with each new change in every modality.
  • Continue to work with other groups to develop or augment existing standards that are relevant, that can be implemented across a wide range of practice, and that are flexible enough to remain relevant in the rapidly changing digital environment, such as the ACR-NEMA DICOM Standard and the IHE Framework.
Footnotes
A position paper from the SCAR TRIP™ Subcommittee of the SCAR Research and Development Committee, November, 2003.
1. Tuddenham WJ. Visual problems of roentgen interpretation. NY State J Med. 1960;60:1234–1238. [PubMed]
2. Hendee WR, Wells PN. Visual perception as an opportunity for radiologic research. Invest Radiol. 1989;24:575–576. [PubMed]
3. Cooper LA. Understanding complex images: perspectives from visual cognition. Invest Radiol. 1991;26:765–768. [PubMed]
4. MacCormac ER. Images: cultural, perceptual and computational. Invest Radiol. 1989;24:331–333. [PubMed]
5. Vannier MW, Rickman D. Multispectral and color-aided displays. Invest Radiol. 1989;24:88–91. [PubMed]
6. Seltzer SE, Cavanagh P, Judy PF, et al. Enhanced displays of medical images: evaluation of the effectiveness of color, motion, and contour for detecting and localizing liver lesions. Acad Radiol. 1995;2:748–755. [PubMed]
7. Vizy KN. The roles of film in an increasingly computerized world. Invest Radiol. 1989;24:503–506. [PubMed]
8. Ackerman L. Toward automated image analysis: future possibilities and historical perspective. Radiol Clin North Am. 1986;24:79–85. [PubMed]
9. Picket RM. Person-machine systems for image-based diagnosis. Invest Radiol. 1988;23:394–395. [PubMed]
10. Hemmy DC, Sonneveld FW, Lobregt S, Fukuta K. A decade of clinical three-dimensional imaging: a review. Part 1. Historical development. Invest Radiol. 1994;29:489–496. [PubMed]
11. Zonneveld FW, Fukuta K. A decade of clinical three-dimensional imaging: a review. Part 2: Clinical applications. Invest Radiol. 1994;29:574–589. [PubMed]
12. Zonneveld FW. A decade of clinical dimensional imaging: a review. Part 3: image analysis and interaction, display options, and physical models. Invest Radiol. 1994;29:716–725. [PubMed]
13. Kundel HL. Medical image perception. Acad Radiol. 1995;2(suppl 2):S108–S110. [PubMed]
14. Burgess A. Image quality, the ideal observer, and human performance of radiologic decision tasks. Acad Radiol. 1995;2:522–526. [PubMed]
15. Wang J, Langer S. A brief review of human perception factors in digital displays for picture archiving and communications systems. J Digit Imaging. 1997;10:158–168. [PMC free article] [PubMed]
16. Krupinski EA, Kundel HL, Judy PF, et al. Key issues for image perception research. Radiology. 1998;209:611–612. [PubMed]
17. Krupinski EA. Importance of perception research in medical imaging. Radiat Med. 2000;18:329–334. [PubMed]
18. Krupinski EA, Radvany M, Levy M, et al. Enhanced visualization processing: effect on workflow. Acad Radiol. 2001;8:1127–1133. doi: 10.1016/S1076-6332(03)80725-0. [PubMed] [Cross Ref]
19. Beard D. Designing a radiology work-station: a focus on navigation during the interpretation task. J Digit Imaging. 1990;3:152–163. doi: 10.1007/BF03167601. [PubMed] [Cross Ref]
20. Arenson RL, Chakraborty DP, Seshadri SB, et al. The digital imaging workstation. Radiology. 1990;176:315–330. [PubMed]
21. Dwyer SJ., III Work station for soft copy reading. Appl Radiol. 1998;.:..
22. Lee H, Frank MS, Rowberg AH, Choi HS, Kim Y. A new method for computed tomography image compression using adjacent slice data. Invest Radiol. 1993;28:678–685. [PubMed]
23. Erickson BJ, Manduca A, Palisson P, et al. Wavelet compression of medical images. Radiology. 1998;206:599–607. [PubMed]
Articles from Journal of Digital Imaging are provided here courtesy of
Springer