Neural Network in the Analysis of the MR Signal as an Image Segmentation Tool for the Determination of T and T Relaxation Times with Application to Cancer Cell Culture.

Journal: International journal of molecular sciences
Published Date: Jan 13, 2023

Abstract

Artificial intelligence has been entering medical research. Today, manufacturers of diagnostic instruments are including algorithms based on neural networks. Neural networks are quickly entering all branches of medical research and beyond. Analyzing the PubMed database from the last 5 years (2017 to 2021), we see that the number of responses to the query "neural network in medicine" exceeds 10,500 papers. Deep learning algorithms are of particular importance in oncology. This paper presents the use of neural networks to analyze the magnetic resonance imaging (MRI) images used to determine MRI relaxometry of the samples. Relaxometry is becoming an increasingly common tool in diagnostics. The aim of this work was to optimize the processing time of DICOM images by using a neural network implemented in the MATLAB package by The MathWorks with the patternnet function. The application of a neural network helps to eliminate spaces in which there are no objects with characteristics matching the phenomenon of longitudinal or transverse MRI relaxation. The result of this work is the elimination of aerated spaces in MRI images. The whole algorithm was implemented as an application in the MATLAB package.

Authors

  • Adrian Truszkiewicz
    Department of Photomedicine and Physical Chemistry, Medical College of University of Rzeszów, University of Rzeszów, Warzywna 1A Street, 35-310 Rzeszów, Poland.
  • Dorota Bartusik-Aebisher
    Department of Biochemistry and General Chemistry, Medical College of University of Rzeszów, University of Rzeszów, Kopisto 2a Street, 35-959 Rzeszów, Poland.
  • Lukasz Wojtas
    Department of Chemistry, University of South Florida, 4202 East Fowler Avenue, Tampa, Florida 33620, United States.
  • Grzegorz Cieślar
    Department of Internal Medicine, Angiology and Physical Medicine, Center for Laser Diagnostics and Therapy, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 40-055 Katowice, Poland.
  • Aleksandra Kawczyk-Krupka
    Department of Internal Medicine, Angiology and Physical Medicine, Center for Laser Diagnostics and Therapy, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 40-055 Katowice, Poland.
  • David Aebisher
    Department of Photomedicine and Physical Chemistry, Medical College of University of Rzeszów, University of Rzeszów, Warzywna 1A Street, 35-310 Rzeszów, Poland.

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