Neural Network in the Analysis of the MR Signal as an Image Segmentation Tool for the Determination of T<sub>1</sub> and T<sub>2</sub> Relaxation Times with Application to Cancer Cell Culture
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...
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| Format: | Article |
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MDPI AG
2023-01-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/24/2/1554 |
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| author | Adrian Truszkiewicz Dorota Bartusik-Aebisher Łukasz Wojtas Grzegorz Cieślar Aleksandra Kawczyk-Krupka David Aebisher |
| author_facet | Adrian Truszkiewicz Dorota Bartusik-Aebisher Łukasz Wojtas Grzegorz Cieślar Aleksandra Kawczyk-Krupka David Aebisher |
| author_sort | Adrian Truszkiewicz |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T12:20:18Z |
| format | Article |
| id | doaj.art-e42a959a25fc4e02ae088fe8b26694a9 |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-09T12:20:18Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-e42a959a25fc4e02ae088fe8b26694a92023-11-30T22:42:12ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672023-01-01242155410.3390/ijms24021554Neural Network in the Analysis of the MR Signal as an Image Segmentation Tool for the Determination of T<sub>1</sub> and T<sub>2</sub> Relaxation Times with Application to Cancer Cell CultureAdrian Truszkiewicz0Dorota Bartusik-Aebisher1Łukasz Wojtas2Grzegorz Cieślar3Aleksandra Kawczyk-Krupka4David Aebisher5Department of Photomedicine and Physical Chemistry, Medical College of University of Rzeszów, University of Rzeszów, Warzywna 1A Street, 35-310 Rzeszów, PolandDepartment of Biochemistry and General Chemistry, Medical College of University of Rzeszów, University of Rzeszów, Kopisto 2a Street, 35-959 Rzeszów, PolandDepartment of Medical Equipment, Provincial Clinical Hospital No. 2, 35-959 Rzeszów, PolandDepartment 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, PolandDepartment 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, PolandDepartment of Photomedicine and Physical Chemistry, Medical College of University of Rzeszów, University of Rzeszów, Warzywna 1A Street, 35-310 Rzeszów, PolandArtificial 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.https://www.mdpi.com/1422-0067/24/2/1554MATLABT1T2relaxation timesMR |
| spellingShingle | Adrian Truszkiewicz Dorota Bartusik-Aebisher Łukasz Wojtas Grzegorz Cieślar Aleksandra Kawczyk-Krupka David Aebisher Neural Network in the Analysis of the MR Signal as an Image Segmentation Tool for the Determination of T<sub>1</sub> and T<sub>2</sub> Relaxation Times with Application to Cancer Cell Culture International Journal of Molecular Sciences MATLAB T1 T2 relaxation times MR |
| title | Neural Network in the Analysis of the MR Signal as an Image Segmentation Tool for the Determination of T<sub>1</sub> and T<sub>2</sub> Relaxation Times with Application to Cancer Cell Culture |
| title_full | Neural Network in the Analysis of the MR Signal as an Image Segmentation Tool for the Determination of T<sub>1</sub> and T<sub>2</sub> Relaxation Times with Application to Cancer Cell Culture |
| title_fullStr | Neural Network in the Analysis of the MR Signal as an Image Segmentation Tool for the Determination of T<sub>1</sub> and T<sub>2</sub> Relaxation Times with Application to Cancer Cell Culture |
| title_full_unstemmed | Neural Network in the Analysis of the MR Signal as an Image Segmentation Tool for the Determination of T<sub>1</sub> and T<sub>2</sub> Relaxation Times with Application to Cancer Cell Culture |
| title_short | Neural Network in the Analysis of the MR Signal as an Image Segmentation Tool for the Determination of T<sub>1</sub> and T<sub>2</sub> Relaxation Times with Application to Cancer Cell Culture |
| title_sort | neural network in the analysis of the mr signal as an image segmentation tool for the determination of t sub 1 sub and t sub 2 sub relaxation times with application to cancer cell culture |
| topic | MATLAB T1 T2 relaxation times MR |
| url | https://www.mdpi.com/1422-0067/24/2/1554 |
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