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The authors have investigated the application of the NASA Advanced Communications Technology Satellite (ACTS) to teleradiology and telemedicine using the Jet Propulsion Laboratory (JPL)-developed ACTS Mobile Terminal (AMT) uplink. In this experiment, bidirectional 128, 256, and 384 kbps satellite links were established between the ACTS/AMT, the ACTS in geosynchronous orbit, and the downlink terrestrial terminal at JPL. A terrestrial Integrated Digital Services Network (ISDN) link was established from JPL to the University of Washington Department of Radiology to complete the bidirectional connection. Ultrasound video imagery was compressed in real-time using video codecs adhering to the International Telecommunication Union-Telecommunication Standardization Sector (ITU-T) Recommendation H.261. A 16 kbps in-band audio channel was used throughout. A five-point Likert scale was used to evaluate the quality of the compressed ultrasound imagery at the three transmission bandwidths (128, 256, and 384 kbps). The central question involved determination of the bandwidth requirements to provide sufficient spatial and contrast resolution for the remote visualization of fine- and low-contrast objects. The 384 kbps bandwidth resulted in only slight tiling artifact and fuzziness owing to the quantizer step size; however, these motion artifacts were rapidly resolved in time at this bandwidth. These experiments have demonstrated that real-time compressed ultrasound video imagery can be transmitted over multiple ISDN line bandwidth links with sufficient temporal, contrast, and spatial resolution for clinical diagnosis of multiple disease and pathology states to provide subspecialty consultation and educational at a distance. Copyright (c) 1999 by W.B. Saunders Company [ABSTRACT FROM AUTHOR]

There is continual pressure on the radiology department to increase its productivity. Two important links to productivity in the computed/digital radiography (CR/DR) workflow chain are the postprocessing step by technologists and the primary diagnosis step by radiologists, who may apply additional image enhancements to aid them in diagnosis. With the large matrix size of CR and DR images and the computational complexity of these algorithms, it has been challenging to provide interactive image enhancement, particularly on full-resolution images. We have used a new programmable processor as the main computing engine of enhancement algorithms for CR or DR images. We have mapped these algorithms to the processor, maximally utilizing its architecture. On a 12-bit 2688 x 2688 image, we have achieved the execution time of 465A ms for adaptive unsharp masking, window/level, image rotate, and lookup table operations using a single processor, which represents at least an order of magnitude improvement compared to the response time of current systems. This kind of performance facilitates rapid computation with preset parameter values and/or enables truly interactive QA processing on radiographs by technologists. The fast response time of these algorithms would be especially useful in a real-time radiology setting, where the radiologist's waiting time in performing image enhancements before making diagnosis can be greatly reduced. We believe that the use of these processors for fast CR/DR image computing coupled with the seamless flow of images and patient data will enable the radiology department to achieve higher productivity.

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).

Image storage, display, and distribution have been difficult problems in radiology for many years. As improvements in technology have changed the nature of the storage and display media, demand for image portability, faster image acquisition, and flexible image distribution is driving the development of responsive systems. Technology, such as the wavelet-based multiresolution seamless image database (MrSID) portable image format (PIF), is enabling image management solutions that address the shifting "point-of-care." The MrSID PIF employs seamless, multiresolution technology, which allows the viewer to determine the size of the image to be viewed, as well as the position of the viewing area within the image dataset. In addition the MrSID PIF allows control of the compression ratio of decompressed images. This capability offers the advantage of very rapid image recall from storage devices and portability for rapid transmission and distribution using the internet or wide-area networks. For example, in teleradiology, the radiologist or other physician desiring to view images at a remote location has full flexibility in being able to choose a quick display of an overview image, a complete display of a full diagnostic quality image, or both without compromising communication bandwidth. The MrSID algorithm will satisfy Joint Photographic Experts Group (JPEG) 2000 standards, thereby being compatible with future versions of the Digital Imaging and Communications in Medicine (DICOM) standard for image data compression.

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)

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)

This series of articles will explore the issue related to displaying, manipulating, and analyzing radiological images on personal computers (PC). This first article discusses the digital image data file, standard PC graphic file formats, and various methods for importing radiological images into the PC.