Informatica

In recent years, Magnetic Resonance Imaging (MRI) has emerged as a prevalent medical imaging technique, offering comprehensive anatomical and functional information. However, the MRI data acquisition process presents several challenges, including time-consuming procedures, prone motion artifacts, and hardware constraints. To address these limitations, this study proposes a novel method that leverages the power of generative adversarial networks (GANs) to generate multi-domain MRI images from a single input MRI image. Within this framework, two primary generator architectures, namely ResUnet and StarGANs generators, were incorporated. Furthermore, the networks were trained on multiple datasets, thereby augmenting the available data, and enabling the generation of images with diverse contrasts obtained from different datasets, given an input image from another dataset. Experimental evaluations conducted on the IXI and BraTS2020 datasets substantiate the efficacy of the proposed method compared to an existing method, as assessed through metrics such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR) and Normalized Mean Absolute Error (NMAE). The synthesized images resulting from this method hold substantial potential as invaluable resources for medical professionals engaged in research, education, and clinical applications. Future research gears towards expanding experiments to larger datasets and encompassing the proposed approach to 3D images, enhancing medical diagnostics within practical applications.

The existing decomposition technology of cooperative developments of multidiscipline technical complexes (MTC) don't provide global optimality die to the imposiolity of solving the problem of developing principles of local project solutions made by a dreat number of specialists of different branches of science. This problem is supposed to be solved by means of controlling of real-time of MTC space structural-parametric synthesis in terms of hierarchically organized variety of assumed scheme-structural and technological solutions. The basis algorithm: 1) realization method for a variety of possible structural organizations of a complex technical system in the form of a network analyzer; 2) method combining synthesis combinatorial operations and parametric operations in search for short routes of the developed network analyzer.

Identification problems of linear dynamic systems in the class of parametric mathematical models are considered. A method of calculating of guaranteed estimates of indefinite parameters is proposed. The method is based on the specific semiinfinite extremal problems solution.