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

Overview

Journal Int J Mol Sci

Publisher MDPI

Specialties Biochemistry
Chemistry
Molecular Biology

Date 2023 Jan 21

PMID 36675075

Authors

Adrian Truszkiewicz

Dorota Bartusik-Aebisher

Lukasz Wojtas

Grzegorz Cieslar

Aleksandra Kawczyk-Krupka

David Aebisher

Affiliations

Soon will be listed here.

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.

Citing Articles

Utility of 1.5 Tesla MRI Scanner in the Management of Small Sample Sizes Driven from 3D Breast Cell Culture.

Guz W, Podgorski R, Aebisher D, Truszkiewicz A, Machorowska-Pieniazek A, Cieslar G Int J Mol Sci. 2024; 25(5).

PMID: 38474256 PMC: 10932374. DOI: 10.3390/ijms25053009.

References

Lustig M, Donoho D, Pauly J . Sparse MRI: The application of compressed sensing for rapid MR imaging. Magn Reson Med. 2007; 58(6):1182-95. DOI: 10.1002/mrm.21391. View

Knight M, McCann B, Kauppinen R, Coulthard E . Magnetic Resonance Imaging to Detect Early Molecular and Cellular Changes in Alzheimer's Disease. Front Aging Neurosci. 2016; 8:139. PMC: 4909770. DOI: 10.3389/fnagi.2016.00139. View

Boumaraf S, Liu X, Ferkous C, Ma X . A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms. Biomed Res Int. 2020; 2020:7695207. PMC: 7238352. DOI: 10.1155/2020/7695207. View

Hosny A, Parmar C, Quackenbush J, Schwartz L, Aerts H . Artificial intelligence in radiology. Nat Rev Cancer. 2018; 18(8):500-510. PMC: 6268174. DOI: 10.1038/s41568-018-0016-5. View

Zhou L, Wang J, Yu S, Wu G, Wei Q, Deng Y . Artificial intelligence in medical imaging of the liver. World J Gastroenterol. 2019; 25(6):672-682. PMC: 6378542. DOI: 10.3748/wjg.v25.i6.672. View

Wang W, Jiang R, Cui N, Li Q, Yuan F, Xiao Z . Semi-supervised vision transformer with adaptive token sampling for breast cancer classification. Front Pharmacol. 2022; 13:929755. PMC: 9353650. DOI: 10.3389/fphar.2022.929755. View

Mohamed A, Berg W, Peng H, Luo Y, Jankowitz R, Wu S . A deep learning method for classifying mammographic breast density categories. Med Phys. 2017; 45(1):314-321. PMC: 5774233. DOI: 10.1002/mp.12683. View

Alsaaidah B, Al-Hadidi M, Al-Nsour H, Masadeh R, AlZubi N . Comprehensive Survey of Machine Learning Systems for COVID-19 Detection. J Imaging. 2022; 8(10). PMC: 9604704. DOI: 10.3390/jimaging8100267. View

Maziero D, Straza M, Ford J, Bovi J, Diwanji T, Stoyanova R . MR-Guided Radiotherapy for Brain and Spine Tumors. Front Oncol. 2021; 11:626100. PMC: 7982530. DOI: 10.3389/fonc.2021.626100. View

10.

Guo L, Wang D, Qian Y, Zheng X, Zhao C, Li X . A two-stage multi-view learning framework based computer-aided diagnosis of liver tumors with contrast enhanced ultrasound images. Clin Hemorheol Microcirc. 2018; 69(3):343-354. DOI: 10.3233/CH-170275. View

11.

Whiting P, Gupta R, Burch J, Mota R, Wright K, Marson A . A systematic review of the effectiveness and cost-effectiveness of neuroimaging assessments used to visualise the seizure focus in people with refractory epilepsy being considered for surgery. Health Technol Assess. 2006; 10(4):1-250, iii-iv. DOI: 10.3310/hta10040. View

12.

Lundervold A, Lundervold A . An overview of deep learning in medical imaging focusing on MRI. Z Med Phys. 2018; 29(2):102-127. DOI: 10.1016/j.zemedi.2018.11.002. View

13.

Ruck L, Mennecke A, Wilferth T, Lachner S, Muller M, Egger N . Influence of image contrasts and reconstruction methods on the classification of multiple sclerosis-like lesions in simulated sodium magnetic resonance imaging. Magn Reson Med. 2022; 89(3):1102-1116. DOI: 10.1002/mrm.29476. View

14.

Bratt A, Kim J, Pollie M, Beecy A, Tehrani N, Codella N . Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification. J Cardiovasc Magn Reson. 2019; 21(1):1. PMC: 6322266. DOI: 10.1186/s12968-018-0509-0. View

15.

Sander J, de Vos B, Isgum I . Automatic segmentation with detection of local segmentation failures in cardiac MRI. Sci Rep. 2020; 10(1):21769. PMC: 7729401. DOI: 10.1038/s41598-020-77733-4. View

16.

Saeedinia S, Jahed-Motlagh M, Tafakhori A, Kasabov N . Design of MRI structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of EEG signals. Sci Rep. 2021; 11(1):12064. PMC: 8187669. DOI: 10.1038/s41598-021-90029-5. View

17.

Abdelhafiz D, Yang C, Ammar R, Nabavi S . Deep convolutional neural networks for mammography: advances, challenges and applications. BMC Bioinformatics. 2019; 20(Suppl 11):281. PMC: 6551243. DOI: 10.1186/s12859-019-2823-4. View

18.

Liu H, Chen Y, Zhang Y, Wang L, Luo R, Wu H . A deep learning model integrating mammography and clinical factors facilitates the malignancy prediction of BI-RADS 4 microcalcifications in breast cancer screening. Eur Radiol. 2021; 31(8):5902-5912. DOI: 10.1007/s00330-020-07659-y. View

19.

Tsai K, Chou M, Li H, Liu S, Hsu J, Yeh W . A High-Performance Deep Neural Network Model for BI-RADS Classification of Screening Mammography. Sensors (Basel). 2022; 22(3). PMC: 8838754. DOI: 10.3390/s22031160. View

20.

Johnson P, Recht M, Knoll F . Improving the Speed of MRI with Artificial Intelligence. Semin Musculoskelet Radiol. 2020; 24(1):12-20. PMC: 7416509. DOI: 10.1055/s-0039-3400265. View