SUPER-RESOLUTION METHODS FOR IMPROVING THE DETAILS OF ULTRASOUND IMAGES

Abstract

The paper investigates the possibilities of using super-resolution methods to improve the spatial detail of ultrasound images in medical diagnostics. The relevance of the topic is due to the limitations of the hardware resolution of traditional ultrasound systems, which limits the accuracy of visualization of small anatomical structures and complicates the clinical diagnosis. The purpose of the article is to provide a comprehensive justification for the effectiveness and feasibility of introducing super-resolution methods into ultrasound diagnostics by systematically analyzing their technical characteristics, mechanisms for improving the quality of image reconstruction, and factors affecting the possibilities of clinical use. The research methodology is based on the generalization of modern approaches to the optimization of model architectures, preparation of input data, pre-training and clinical validation of results. Particular attention is paid to a comparative analysis of the advantages of transformable models, such as SwinIR, in the context of medical visualization. The paper characterizes the main factors of loss of spatial detail in ultrasound images, systematizes the classification of superresolution methods by technical parameters, analyzes organizational barriers to the implementation of algorithms in the clinical environment, and develops reasonable recommendations for the integration of such solutions into medical infrastructure. The study found that neural models of superresolution, in particular, architectures based on convolutional networks and transformers, have a high potential for improving the quality of ultrasound images without the need to upgrade hardware. The key problems associated with the lack of labeled ultrasound datasets, the difficulty of complying with DICOM and PACS standards, and the ethical aspects of medical liability are identified. The paper proves that successful implementation of superresolution methods requires adaptation of algorithms to the specifics of medical systems, gradual integration into existing software solutions, maintaining physician control, and ensuring compliance with regulatory requirements. The scientific novelty is a comprehensive analysis of ways to improve the efficiency of superresolution algorithms in ultrasound imaging and to determine the directions of their optimal use in medical practice.