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Over the last decade, a picture archiving and communications system (PACS) has been accepted by an increasing number of clinical organizations. Today, PACS is considered as an essential image management and productivity enhancement tool. Image compression could further increase the attractiveness of PACS by reducing the time and cost in image transmission and storage as long as 1) image quality is not degraded and 2) compression and decompression can be done fast and inexpensively. Compared to JPEG, JPEG 2000 is a new image compression standard that has been designed to provide improved image quality at the expense of increased computation. Typically, the decompression time has a direct impact on the overall response time taken to display images after they are requested by the radiologist or referring clinician. In this paper, we present a fast JPEG 2000 decoder running on a low-cost programmable processor. It can decode a losslessly compressed 2048 x 2048 CR image in 1.51 s. Using this kind of a decoder, performing JPEG 2000 decompression at the PACS display workstation right before images are displayed becomes viable. A response time of 2 s can be met with an effective transmission throughput between the central short-term archive and the workstation of 4.48 Mb/s in case of CT studies and 20.2 Mb/s for CR studies. We have found that JPEG 2000 decompression at the workstation is advantageous in that the desired response time can be obtained with slower communication channels compared to transmission of uncompressed images.

The increasing mobility of the population and frequent changes in healthcare coverage, in both the government and private sectors, require integration of medical records not only longitudinally, but also across a variety of healthcare providers. Early in 1998, the federal government decided to solve this problem by constructing a framework for access to medical records by all of the government's health care facilities, called the Government Computer-Based Patient Record (GCPR). The government consortium chose a proposal by Litton PRC, a partnership of 11 companies with complementary areas of expertise. The framework is based on open systems, which use publicly available standards, and includes a Master Patient Information Locator that allows access to medical information from remote facilities, based on creating a unique identifier for each and every individual patient. PRC will use the Digital Imaging and Communications in Medicine (DICOM) imaging standard for radiology, supplemented by Health Level Seven (HL7).

In recent years there have been many significant developments in the ability to process radiographic pictures and transfer the images from one location to another, and we can now examine the application of computers in the radiographic evaluation of rheumatoid arthritis. Image management and communication systems can assist in providing better health care while lowering costs. Although this work is still in its infancy, the rapid growth in both hardware capability and software techniques will soon make these techniques available to physicians everywhere. The expanding capability in the commercial world is being embraced by the radiology department, and will soon allow rheumatologists to access images at greater distances, and do more with the images which become available.

Computers have greatly facilitated the processing and storage of radiologic information. Manufacturers of radiology information systems (RISs) are gradually incorporating options for interfacing their products with other computers (e.g., hospital information systems). However, a growing need exists to also interface RISs with digital radiologic equipment so that images (e.g., computed radiographs of the chest and skeleton) are automatically labeled with identification data. Such connectivity would eliminate redundant work by technologists, decrease errors in the labeling of images, and increase the consistency of patients' data within a radiology department. Unfortunately, the rapid advances in digital technology, combined with the lack of a well-defined standard for the transfer of demographic information between dissimilar systems, have delayed development of these interfaces. We have developed a widely applicable method to automatically transfer patients' demographic data from an RIS to a commercially available computed radiography system. A personal computer is configured with inexpensive programmable telecommunications software to create an interactive gateway, which eliminates the need for redundant data entry (compared with entering data once on the RIS and again on the stand-alone computed radiography system), and thus also decreases errors in the labeling of images.

Rationale and Objectives: The compression of cranial computed tomography scans was improved by using independent intra- and interframe compression techniques., Methods: For intraframe compression, an image was decomposed into four subimages, one subimage was chosen as a reference subimage, and three of the subimages were predicted from the reference subimage. The prediction error was encoded with a classified vector quantizer (CVQ) based on human visual perception characteristics. Interframe redundancy is exploited by a displacement estimated interslice (DEI) algorithm that encodes the differences between reference subimages from adjacent slices. This combined DEI/CVQ method was subjectively evaluated by 13 radiologists under a blinded protocol, and was compared to the CVQ method alone, the DEI method alone, the original images, and to a standard intraframe discrete cosine transform (DCT) compression method., Results: Only the combined DEI/CVQ method at 10:1 compression was not scored significantly different from the original images. At 15:1 compression, the DEI/CVQ method was scored significantly better than the 10:1 DCT and any other 15:1 compression methods., Conclusions: Compressed image quality is enhanced by exploiting inter- and intraframe redundancy, and by modeling some characteristics of human visual perception. The DEI/CVQ method is well-suited for progressive transmission, and thus, holds potential in teleradiology as well as picture archiving and communications systems.

This is the fifth and final article in our series for radiologists and imaging scientists on displaying, manipulating, and analyzing radiologic images on personal computers (PCs). There are many methods of transferring radiologic images into a PC, including transfer over a network, transfer from an imaging modality storage archive, using a frame grabber in the image display console, and digitizing a radiograph or 35-mm slide. Depending on the transfer method, the image file may be an extended gray-scale contrast, 16-bit raster file or an 8-bit PC graphics file. On the PC, the image can be viewed, analyzed, enhanced, and annotated. Some specific uses and applications include making 35-mm slides, printing images for publication, making posters and handouts, facsimile (fax) transmission to referring clinicians, converting radiologic images into medical illustrations, creating a digital teaching file, and using a network to disseminate teaching material. We are distributing a 16-bit image display and analysis program for Macintosh computers, Dr Razz, that illustrates many of the principles discussed in this review series. The program is available for no charge by anonymous file transfer protocol (ftp).

This is the fourth article of our series for radiologists and imaging scientists on displaying, manipulating, and analyzing radiologic images on personal computers. Classic image processing is divided into point, area, frame, and geometric processes. Point processes change image pixel values based on the value of the pixel of interest. Histogram equalization adjusts the pixel values in the image based on the distribution of pixel values. Area processes change the pixel of interest based on the values of the surrounding pixels, known as the neighborhood. Area processes using a convolution kernel are often used as image filters. Common convolution kernels include low-frequency, high-frequency, and edge-enhancement filters. Edge enhancement can be performed with convolution kernels such as shift and difference, gradient-directional and Laplacian filters, or with nonlinear methods such as Sobel's algorithm. Frame processes mathematically combine two or more images, often for noise reduction and background subtraction. Geometric processes alter the location of pixels within the image, but usually not the pixel values. Common radiologic applications of image processing include window width and window level adjustments (point process), adaptive histogram equalization (area process), unsharp masking (area process), computed radiography image processing (combined area and point processes), digital subtraction angiography (frame and geometric processes), region of interest analysis (area process), and image rotation (geometric process). As digital imaging becomes more widespread, radiologists need to understand the image processing that is fundamental to these modalities.

This is part 2 of our article on image storage and compression, the third article of our series for radiologists and imaging scientists on displaying, manipulating, and analyzing radiologic images on personal computers. Image compression is classified as lossless (nondestructive) or lossy (destructive). Common lossless compression algorithms include variable-length bit codes (Huffman codes and variants), dictionary-based compression (Lempel-Ziv variants), and arithmetic coding. Huffman codes and the Lempel-Ziv-Welch (LZW) algorithm are commonly used for image compression. All of these compression methods are enhanced if the image has been transformed into a differential image based on a differential pulse-code modulation (DPCM) algorithm. The LZW compression after the DPCM image transformation performed the best on our example images, and performed almost as well as the best of the three commercial compression programs tested. Lossy compression techniques are capable of much higher data compression, but reduced image quality and compression artifacts may be noticeable. Lossy compression is comprised of three steps: transformation, quantization, and coding. Two commonly used transformation methods are the discrete cosine transformation and discrete wavelet transformation. In both methods, most of the image information is contained in a relatively few of the transformation coefficients. The quantization step reduces many of the lower order coefficients to 0, which greatly improves the efficiency of the coding (compression) step. In fractal-based image compression, image patterns are stored as equations that can be reconstructed at different levels of resolution.

Most digital radiologic images have an extended contrast range of 9 to 13 bits, and are stored in memory and disk as 16-bit integers. Consequently, it is difficult to view such images on computers with 8-bit red-green-blue (RGB) graphic systems. Two approaches have traditionally been used: (1) perform a one-time conversion of the 16-bit image data to 8-bit gray-scale data, and then adjust the brightness and contrast of the image by manipulating the color palette (palette animation); and (2) use a software lookup table to interactively convert the 16-bit image data to 8-bit gray-scale values with different window width and window level parameters. The first method can adjust image appearance in real time, but some image features may not be visible because of the lack of access to the full contrast range of the image and any region of interest measurements may be inaccurate. The second method allows "windowing" and "leveling" through the full contrast range of the image, but there is a delay after each adjustment that some users may find objectionable. We describe a method that combines palette animation and the software lookup table conversion method that optimizes the changes in image contrast and brightness on computers with standard 8-bit RGB graphic hardware--the dual lookup table algorithm. This algorithm links changes in the window/level control to changes in image contrast and brightness via palette animation.(ABSTRACT TRUNCATED AT 250 WORDS)

The emerging widespread adoption of the Digital Imaging Communications in Medicine (DICOM) standard will increase the demand for radiologic image transfer between radiologic image acquisition, archive, display and printing devices. Unfortunately, there are and will continue to be many devices that do not and will not support this standard, especially older radiologic equipment and devices from nonradiologic vendors. Determining the image file format characteristics of images from such equipment is often difficult, and done on an ad hoc basis. We have developed a software tool that assists users in determining the image file format parameters of unknown radiologic images.

An increasing number of radiology departments are using computers to facilitate management of radiologic information. Transcription, storage, and printing of radiologic reports are among the primary functions of a radiology information system. Consequently, manual signature of radiologic reports is being replaced by on-line electronic authentication. However, the utilities provided on most radiology information systems to review, edit, and otherwise manipulate radiologic reports are relatively crude compared with commercial word-processing and data-base management software available for personal computers. We have developed a personal computer software system that is integrated with our existing radiology information system; expedites the radiologist's task of reviewing, editing, and authenticating (i.e., signing) radiologic reports; provides a teaching file data base on the workstation into which radiologic reports and patients' demographics can be instantly transferred; and provides a similar data base to facilitate follow-up on those examinations deemed appropriate for quality-assurance procedures. These features improve the radiologist's efficiency and increase his or her willingness to more fully exploit the intended purposes of a radiology information system.

This is the third article of our series for radiologists and imaging scientists on displaying, manipulating, and analyzing radiologic images on personal computers. Part 1 of this article discusses image storage and reviews the basic concepts of information theory and image compression; part 2 will discuss specific methods of image compression. There are a wide variety of removable storage devices available to users who need to archive radiologic images on their personal computers. Tape drives have potentially very large storage capacity but slow performance. Removable SyQuest (SyQuest Technology, Femont, CA) and Bernoulli disks have near hard disk performance and can store from 100 to 150 Mbytes. Magneto-optical drives can store nearly 1 Gb on a 5.25" disk, with somewhat slower performance. Selecting the most appropriate storage solution requires a careful balance of the user's requirements, including performance, storage needs, cost and compatibility with other users. Despite the advances in low cost high capacity storage technology, image compression remains a crucial technology for modern diagnostic radiology because digital images require such large amounts of storage. Image compression is possible because radiologic images have relatively low entropy (high information content) compared with random noise. Image compression is classified as lossless (nondestructive) or lossy (destructive). Lossless image compression commonly achieve compression ratios of 1.5:1 to 3:1 (33% to 67%), whereas lossy compression can compresses images from 3:1 to 30:1 (67% to 97%).(ABSTRACT TRUNCATED AT 250 WORDS)

Radiology reports are likely to be more useful if they contain appropriate graphic material. Diagnostic conclusions and recommendations become more convincing and useful when the clinician personally can review the image on which these are based. Modern desk-top publishing techniques make it possible to incorporate radiographic images, appropriately selected and annotated, as part of the radiology report. It is believed that such illustrated reports would be preferred by referring physicians, notwithstanding a significant loss of image detail. A survey of these referring physicians was carried out to determine whether this hypothesis was correct.