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This paper presents the method for the creation of an expert knowledge base of a military object, for example a radar system. Such a knowldge base can be widely used to support the process of the maintenance of a complex technical object. The first step is a maintenance evaluation of the object. During this kind of analysis, it is necessary to perform the grouping and classification of the functional elements of the object. It is realised using the functional scheme of the object presented. Further, diagnostic information is combined with specialised experts. knowledge and transformed it into a set of servicing information. The participation of experts in the process of expert knowledge base preparation is significant. The purpose is to capture information that will be a fundamental for the design of a maintenance system dedicated to the particular technical object. The methods proposed were verified with appropriate examples, in which the set of specialised diagnostic information of the object was determined. [ABSTRACT FROM AUTHOR]

[Display omitted] In compressed sensing (CS)-based magnetic resonance imaging (MRI), it is very challenging to maintain the diagnostic quality due to limited measurements. Diagnostically critical information, like fine anatomical details, edges, and boundaries are distorted due to the leakage of energy and artifacts during CS-reconstruction. In this paper, we have proposed a deep learning architecture to reconstruct high-resolution diagnostically enhanced MR images from comprehensively sensed k-space data with reduced scan time. Proposed network is based on deep back-projection and generative adversarial network (GAN) architectures. For the performance evaluation, we have considered a pathological dataset having 3064 MR images with three types of brain tumors. The performance of the proposed technique is also compared with some of the well-known image super-resolution techniques. It has been observed that the proposed technique outperforms some of the recent image super-resolution techniques, both quantitatively and qualitatively. [ABSTRACT FROM AUTHOR]

As part of day-to-day practice, the most frequent role of the radiologist is to provide diagnostic information derived from imaging that will help the clinician better manage patient care. In essence, radiologists are reimbursed for the information we provide. The value of diagnostic information, however, extends beyond strict clinical and monetary value.The objectives of this paper are to provide the framework for understanding the “value” of diagnostic imaging. The article focuses on the application of these concepts using examples from the literature with particular emphasis on screening and suggests using a screening test experience as a means of improving health behavior. [Copyright &y& Elsevier]