The purpose of this article is to describe a system we developed for importing images on compact discs (CDs) from external imaging departments into our clinical image viewing system, and to report on key metrics regarding veracity of information seen on the CDs. We recommend careful attention to the process of CD importation because of the error rate we have seen. We developed a system and process for importing images on CD into our EMR. The importation system scans the CD for digital imaging and communications in medicine (DICOM) images, and collects all patient information seen. That information is presented to the patient for verification. Once validated, the image data is copied into our clinical viewing system. The importation system includes facilities for collecting instances of incorrect data. About 90% of images are now exchanged between our healthcare enterprise and other entities via CD. Data for the wrong patient (e.g., the wrong CD) is seen in about 0.1% of cases, and a similar number of CDs have data for more than one patient on the CD(s) the patient bring to our facility. Most data are now exchanged via DICOM files. DICOM images burned onto CD media are now commonly used for image exchange. However, applications to import DICOM images are not enough. One must implement a process to assure high confidence that the data imported belongs to the patient you are importing.

Intracranial aneurysms represent a significant cause of morbidity and mortality. While the risk factors for aneurysm formation are known, the detection of aneurysms remains challenging. Magnetic resonance angiography (MRA) has recently emerged as a useful non-invasive method for aneurysm detection. However, even for experienced neuroradiologists, the sensitivity to small (<5 mm) aneurysms in MRA images is poor, on the order of 30~60% in recent, large series. We describe a fully automated computer-aided detection (CAD) scheme for detecting aneurysms on 3D time-of-flight (TOF) MRA images. The scheme locates points of interest (POIs) on individual MRA datasets by combining two complementary techniques. The first technique segments the intracranial arteries automatically and finds POIs from the segmented vessels. The second technique identifies POIs directly from the raw, unsegmented image dataset. This latter technique is useful in cases of incomplete segmentation. Following a series of feature calculations, a small fraction of POIs are retained as candidate aneurysms from the collected POIs according to predetermined rules. The CAD scheme was evaluated on 287 datasets containing 147 aneurysms that were verified with digital subtraction angiography, the accepted standard of reference for aneurysm detection. For two different operating points, the CAD scheme achieved a sensitivity of 80% (71% for aneurysms less than 5 mm) with three mean false positives per case, and 95% (91% for aneurysms less than 5 mm) with nine mean false positives per case. In conclusion, the CAD scheme showed good accuracy and may have application in improving the sensitivity of aneurysm detection on MR images.

In this study, we present preliminary data on the effect of automated 3D image alignment on the time to arrive at a decision about an imaging finding, the agreement of multiple of multiple observers, the prevalence of comparison examinations, and technical success rates for the image alignment algorithm. We found that automated image alignment reduced the average time to make a decision by 25% for cases where the structures are rigid, and when the scanning protocol is similar. For cases where these are not true, there is little or no benefit. In our practice, 54% of cases had prior examinations that could be automatically aligned. The overall benefit seen in our department for highly similar exams might be 20% for neuro and 10% for body; the benefit seen in other practices is likely to vary based on scanning practices and prevalence of prior examinations.

Conclusion

The requirements for a clinical viewing station have some similarities to requirements of an interpretation workstation, but have some important differences. The user base is a heterogeneous group, and the tasks the clinicians perform vary substantially, increasing the variation in the requirements. However, it is possible to determine a list of requirements that satisfy most clinicians under most circumstances. In addition to the standard image manipulation tools, we have found that integration with the other clinical tools is essential to maintaining or increasing their efficiency. Providing for varying levels of functionality/complexity is also useful, because of the variation in the user base.

This report describes several image archival problems facing the authors' department and the results of their attempt to define the requirements for an enterprise digital image archive. The problems identified include the costs of supporting multiple distinct archives, the increased complexity of supporting multiple archive interfaces, the differences in data handling policies and resulting variations in data integrity, and variability in support for nonimage data. The authors also describe the data collected including image volumes and trends and imaging device trends. Finally, the resulting specification for an enterprise digital image archive, including storage and retrieval performance and interface requirements are presented.

A clinical viewing system was integrated with the Mayo Clinic Scottsdale picture archiving and communication system (PACS) for providing images and the report as part of the electronic medical record (EMR). Key attributes of the viewer include a single user log-on, an integrated patient centric EMR image access for all ordered examinations, prefetching of the most recent prior examination of the same modality, and the ability to provide comparison of current and past exams at the same time on the display. Other functions included preset windows, measurement tools, and multiformat display. Images for the prior 12 months are stored on the clinical server and are viewable in less than a second. Images available on the desktop include all computed radiography (CR), chest, magnetic resonance images (MRI), computed tomography (CT), ultrasound (U/S), nuclear, angiographic, gastrointestinal (GI) digital spots, and portable C-arm digital spots. Ad hoc queries of examinations from PACS are possible for those patients whose image may not be on the clinical server, but whose images reside on the PACS archive (10TB). Clinician satisfaction was reported to be high, especially for those staff heavily dependent on timely access to images, as well as those having heavy film usage. The desktop viewer is used for resident access to images. It is also useful for teaching conferences with large-screen projection without film. We report on the measurements of functionality, reliability, and speed of image display with this application.

The purpose of the study was to evaluate the effects of lossy compression on grayscale ultrasound images to determine how much compression can be applied while still maintaining images that are acceptable for diagnostic purposes. The study considered how the acquisition technique (video frame-grabber versus directly acquired in digital form) influences how much compression can be applied. For directly acquired digital images, the study considered how text (that is burned into the image) affects the compressibility of the image. The lossy compression techniques that were considered include JPEG and a Wavelet algorithm using set partitioning in hierarchical trees (SPIHT).

Although it is intuitively valuable that more expedient delivery of radiographic images and reports to clinicians would improve patient care, it is important to document these outcomes to validate further advances in these areas. We evaluated the care of 215 patients seen at a walk-in clinic to determine what benefit digital imaging is to the patient. Cohorts consisted of all patients for whom specified radiology examinations were ordered during a 7-day period. The first cohort was recruited when analog films were used. The second cohort received examinations performed with computed radiography (CR) acquisition and computer display, which had been in use for 2 years. Patients were categorized as to the type of study they received, as well as whether a staff radiologist was immediately available to read the study. Clinical behavior was characterized by outcome measures of time to final diagnosis, time to final treatment, and need for follow-up. Our analysis demonstrated a reduction in time to final diagnosis that was better appreciated during the times when a staff radiologist was not immediately available. It also suggested that greater time reductions were seen for patients who received extremity examinations than those who received chest, sinus, or rib films. These data suggest that digital imaging is a useful tool to improve clinical outcome of patients seen in the acute care setting.

We prospectively compared image and report delivery times in our Urgent Care Center (UCC) during a film-based practice (1995) and after complete implementation of an electronic imaging practice in 1997. Before switching to a totally electronic and filmless practice, multiple time periods were consistently measured during a 1-week period in May 1995 and then again in a similar week in May 1997 after implementation of electronic imaging. All practice patterns were the same except for a film-based practice in 1995 versus a filmless practice in 1997. The following times were measured: (1) waiting room time, (2) technologist’s time of examination, (3) time to quality control, (4) radiology interpretation times, (5) radiology image and report delivery time, (6) total radiology turn-around time, (7) time to room the patient back in the UCC, and (8) time until the ordering physician views the film. Waiting room time was longer in 1997 (average time, 26∶47) versus 1995 (average time, 15∶54). The technologist’s examination completion time was approximately the same (1995 average time, 06∶12; 1997 average time, 05∶41). There was also a slight increase in the time of the technologist’s electronic verification or quality control in 1997 (average time, 7∶17) versus the film-based practice in 1995 (average time, 2∶35). However, radiology interpretation times dramatically improved (average time, 49∶38 in 1995 versus average time 13∶50 in 1997). There was also a decrease in image delivery times to the clinicians in 1997 (median, 53 minutes) versus the film based practice of 1995 (1 hour and 40 minutes). Reports were available with the images immediately upon completion by the radiologist in 1997, compared with a median time of 27 minutes in 1995. Importantly, patients were roomed back into the UCC examination rooms faster after the radiologic procedure in 1997 (average time, 13∶36) than they were in 1995 (29∶38). Finally, the ordering physicians viewed the diagnostic images and reports in dramatically less time in 1997 (median, 26 minutes) versus 1995 (median, 1 hour and 5 minutes). In conclusion, a filmless electronic imaging practice within our UCC greatly improved radiology image and report delivery times, as well as improved clinical efficiency.

An efficient environment for picture archiving and communications systems (PACS) in the radiology department and throughout a medical practice requires good hardware, good software, and integration of the information sources that exist in a radiology department and institution. This tutorial will describe some of the considerations in evaluating a PACS workstation, with a view to the hardware requirements, user interface designs and integration with the information systems.

We developed a system for delivering radiologic images and reports to desktop computers used for the electronic medical record (EMR). This system was used by both primary care physicians and specialists primarily in the out-patient setting. The system records all physician interactions with the application to a database. This usage information was then studied in order to understand the value and requirements of an application that could display radiology information (reports and images) on EMR workstations. In this report we describe some of the differences and similarities in usage patterns for the two physician groups. A very high percentage of physicians indicated that having image display capabilities on the workstations was very valuable.

In this article, we describe the development and validation of an automatic algorithm to segment brain from extracranial tissues, and to classify intracranial tissues as cerebrospinal fluid (CSF), gray matter (GM), white matter (WM) or pathology. T1 weighted spin echo, dual echo fast spin echo (T2 weighted and proton density (PD) weighted images) and fast Fluid Attenuated Inversion Recovery (FLAIR) magentic resonance (MR) images were acquired ino 100 normal patients and 9 multiple sclerosis (MS) patients. One of the normal studies had synthesized MS-like lesions superimposed. This allowed precise measurement of the accuracy of the classification. The 9 MS patients were imaged twice in one week. The algorithm was applied to these data sets to measure reproducibility. The accuracy was measured based on the synthetic lesion images, where the true voxel class was known. Ninety-six percent of normal intradural tissue voxels (GM, WM, and CSF) were labeled correctly, and 94% of pathological tissues were labeled correctly. A low coefficient of variation (COV) was found (mean, 4.1%) for measurement of brain tissues and pathology when comparing MRI scans on the 9 patients. A totally automatic segmentation algorithm has been described which accurately and reproducibly segments and classifies intradural tissues based on both synthetic and actual images.

Conclusion

A successful PACS implementation requires well-designed hardware, a thoughtful software implementation, and a high degree of integration of PACS and RIS information. Although it may be difficult to avoid some of the technical jargon, perhaps the most important evaluation step is to sit at a workstation and see if the image display metaphor is one that can conform to your image interpretation style. Finally, integration of RIS and PACS is crucial.

We describe a method for rapid calculation of maximum intensity projection (MIP) images. The method is applied to 10 example cases with more extensive testing in 1 case. Measurements of calculation time for all 10 cases show good performance (less than 1.2 seconds) for calculating a MIP image that is a very faithful representation of the original (less than 2% of pixels differ from original image). We also demonstrate that the computation time is related linearly to the number of pixels used, allowing arbitrarily rapid computation rates. This technique may be helpful as a navigation aid in evaluation magnetic resonance (MR) angiography data.

Image display on electronic medical record (EMR) workstations is an important step in widespread implementation of picture archiving and communications systems (PACS). We describe a pilot project for implementing image display capability that is integrated with the EMR software, and will allow display of images on the physician’s workstation. We believe this pilot will provide valuable information about usage patterns in image display needs, which will be valuable in planning further expansion of PACS in our institution.

This article will take an analytical look at how lossy Joint Photographic Experts Group (JPEG) and wavelet image compression techniques affect medical image content. It begins with a brief explanation of how the JPEG and wavelet algorithms work, and describes in general terms what effect they can have on image quality (removal of noise, blurring, and artifacts). It then focuses more specifically on medical image diagnostic content and explains why subtle pathologies, that may be difficult for the human eye to discern because of low contrast, are generally very well preserved by these compression algorithms. By applying a wavelet decomposition to the whole image and to specific regions of interest (ROI), and by understanding how the lossy quantization step attenuates signals in those decomposition energy subbands, much can be learned about how tolerant various anatomical structures are to compression. High-frequency anatomical structures that have their energy represented by a few large coefficients (in the wavelet domain) will be well preserved, while, those structures with high frequency energy distributed over numerous smaller coefficients are the most vulnerable to compression. Digitized films showing subtle chest nodules, a subtle stress fracture, and CT and MR images are used to show these results.

READS is a picture archiving and communications system (PACS) display program that is tailored to allow a radiologist to efficiently perform image review tasks. In this study, the image review process was observed and functional patterns were identified. These were used to define a design that was considered to represent the optimal balance of compromises for a low-cost review station that also allowed easy addition of new functionality. As a result, a program was designed and implemented that has been found to be acceptable for image review and for special image processing function development.

The purpose of this article is to assess lossy image compression of digitized chest radiographs using radiologist assessment of anatomic structures and numerical measurements of image accuracy. Forty posterior-anterior (PA) chest radiographs were digitized and compressed using an irreversible wavelet technique at 10, 20, 40, and 80∶1. These were presented in a blinded fashion with an uncompressed image for A-B comparison of 11 anatomic structures as well as overall quality assessments. Mean error, root-mean square (RMS) error, maximum pixel error, and number of pixels within 1% of original value were also computed for compression ratios from 5∶1 to 80∶1. We found that at low compression (10∶1) there was a slight preference for compressed images. There was no significant difference at 20∶1 and 40∶1. There was a slight preference on some structures for the original compared with 80∶1 compressed images. Numerical measures showed high image faithfulness, both in terms of number of pixels that were within 1% of their original value, and by the average error for all pixels. Our findings suggest that lossy compression at 40∶1 or more can be used without perceptible loss in the representation of anatomic structures. On this finding, we will do a receiver-operator characteristic (ROC) analysis of nodule detection in lossy compressed images using 40∶1 compression.