Abstract
Nowadays it is hard to think of any applications in modern society in which electronic systems do not play a significant role. In aerospace and aviation, defence, telecommunication and healthcare, to name a few, computers have already assumed the most life-critical tasks. Unlike most human beings, computers seem to do their job pretty well, at most times and under all environmental conditions. Sometimes, however, for some reason, the fresh water supply in a city stops, the mainframe computer of an international security exchange centre malfunctions, or the satellite television goes out abruptly. Possible sources for such dissatisfactory performances are physical deterioration or design faults in hardware components. Fortunately in the 1950s and 1960s quite a number of reliability models were developed for hardware. Another major source for malfunctioning of computer systems is the presence of bugs in the software that controls the system. The modelling of software reliability was only begun in the early 1970s. This chapter presents a comprehensive approach to the development of a reliable PACS, which are capable of meeting the high-quality level required of mission-critical medical devices. To develop a preliminary design, the PACS team would begin with a system description and reliability evaluation of a baseline system, includ ing implementation of hardware redundancy, software provisions, and acceptance test. Through detailed system analyses and electrical, electronic and mechanical reliability studies, a final preliminary design can be derived. In this chapter the essential mathematical and statistical aspects of hardware and software reliability predictions are first presented, followed by a spreadsheet-based approach to model hardware and software reliabilities. A method of designing higher system reliability through parallel and cross-linked configurations is then given. Finally a brief case on the acceptance test of a PACS software is illustrated.
TopBasic Concepts
Since the use of PACS raises both medical and social concerns, it cannot be instituted without addressing concerns related to the reliability and security issues. Reliability encompassed hardware and software reliability, including the retrieval of the previously obtained images. Reliability may be defined as the ability of a system to operate correctly according to its specification for a given time interval (Musa et al., 1987), under the precondition that the system operates correctly at the beginning of this time interval. Traditionally, system reliability is measured in terms of mean time between failures or the failure probability of a system function. To predict the reliability of component-based systems many methods have been proposed (Goseva-Popstojanova and Tivedi, 2001). The basic concept of these methods is to identify failure probabilities for each service of a component and determine the reliability of system functions based on the call sequences and dependencies between the low-level services and the specific system function.
PACS technologists are in many ways like soothsayers - they are expected to predict many things for the PACS: how many failures from this and that lot will occur within x number of years, how much of this and that lot will survive after x number of years, what will happen if a device is operated under these conditions, etc. Fortunately, they do not need any paranormal abilities to give intelligent responses to questions involving failures that have not yet happened. All they need is a good understanding of statistics and reliability mathematics to be up to the task.
In the present context, reliability is defined as the probability that software, component, device, or the entire PACS will perform its prescribed duty without failure for a given time when operated correctly in a specified environment. Reliability assessment, or the process of determining to a certain degree of confidence the reliability of a PACS, applies various statistical analysis techniques to analyze reliability data. If properly done, a reliability prediction using such techniques will match the survival behavior of the system, many years after the prediction was made.