Peer-reviewed articles 17,970 +



Title: MEASURING AND EVALUATION OF THE ECHOGENICITY GRADE OF SUBSTANTIA NIGRA IN MRI SEQUENCES VS B-MODE ULTRASOUND IMAGING USING THE SAME ALGORITHM: PILOT COMPARISON STUDY

MEASURING AND EVALUATION OF THE ECHOGENICITY GRADE OF SUBSTANTIA NIGRA IN MRI SEQUENCES VS B-MODE ULTRASOUND IMAGING USING THE SAME ALGORITHM: PILOT COMPARISON STUDY
Jiri Blahuta; Tomas Soukup; Jan Lavrincik; Lukas Pavlik; Jiri Kozel
10.5593/sgem2022/2.1
1314-2704
English
22
2.1
•    Prof. DSc. Oleksandr Trofymchuk, UKRAINE 
•    Prof. Dr. hab. oec. Baiba Rivza, LATVIA
Diagnostic ultrasound (US) and magnetic resonance imaging (MRI) are important medical imaging methods in modern radiology. Our research is focused on imaging brain structures in neurology. In this paper we present differences of digital image analysis of the substantia nigra (SN) between US and MRI using the same algorithm. In the past, we developed an application for analyzing substantia nigra echogenicity in BMODE US images. Our developed application is based on a principle of binary thresholding in Region of Interest (ROI) to evaluate echogenicity grade. Increased echogenicity of SN is one of important markers for Parkinson’s Disease (PD) progress. The goal of this paper is to analyze if the same principle used for US B-MODE imaging is also applicable for different MR sequences to find out SN changes. From the achieved results detectable SN changes using MRI are possible at least as a complementary examination to US imaging. We need to prove if echogenicity index (called Echo-Index) is well reproducible value between two different MR sequences; SWI and T2-TSE; how to distinguish between pathological SN and normal anatomy. In the first pilot analysis, it seems that the principle of Echo-Index measurement could be a starting point to create a new large clinical study in this field. Totally 23 MR images from two different sequences (T1 and T2) were analyzed in this pilot study. However, it seems that Echo-Index cannot distinguish normal and diseased SN.
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[3] Cermak, P., Vecerek, M. A reproducible method to transcranial B-MODE ultrasound images analysis based on echogenicity evaluation in selectable ROI. (2014) International Journal of Biology and Biomedical Engineering, Vol. 8, pp. 98 – 106. ISSN: 19984510.
[4] Skoloudik, D., Jelinkova, M., Bartova, Langova, K., Herzig, R. Transcranial Sonography of the Substantia Nigra: Digital Image Analysis. American Journal of Neuroradiology, 2014, Vol. 35 Issue 9, pp. 2273 – 2278.
[5] Skoloudik, D., Krulova, P., Kisvetrova, H., Herzig, R. Transkranialni sonografie mediotemporalniho laloku u pacientu s Alzheimerovou demenci. Cesk Slov Neurol N 2020; 83(2): pp. 189 – 193.
[6] Maskova, J., Dusek, P. Digitized Image Analysis of Insula Echogenicity Detected by TCS-MR Fusion Imaging in Wilson's and Early-Onset Parkinson's Diseases. Ultrasound in Medicine & Biology,Volume 46, Issue 3, 2020, pp. 842 – 848. ISSN: 0301-5629, https://doi.org/10.1016/j.ultrasmedbio.2019.12.013.
[7] Walter, U., How to Measure Substantia Nigra Hyperechogenicity in Parkinson Disease. Journal of Ultrasound in Medicine, 2013, Vol. 32, pp. 1837 – 1843. https://doi.org/10.7863/ultra.32.10.1837.
[8] Zhou HY, Huang P, Sun Q, Du JJ, Cui SS, Tan YY, Hu YY, Zhan WW, Wang Y, Xiao Q, Liu J, Chen SD. Substantia Nigra Echogenicity Associated with Clinical Subtypes of Parkinson's Disease. J Parkinsons Dis. 2018;8(2):333-340. doi: 10.3233/JPD-171264. PMID: 29614699.
[9] Blahuta, J., Cermak, P. Pilot Design of a Rule-Based System and an Artificial Neural Network to Risk Evaluation of Atherosclerotic Plaques in Long-Range Clinical Research. ICANN 2018, Lecture Notes in Computer Science book series (LNCS, volume 11140), Springer, 2018, pp. 90 – 100. ISSN: 978-3-030-01420-9.
[10] Sosik, P., Soukup, T. Approach to Automatic Segmentation of Atherosclerotic Plaque in B-images Using Active Contour Algorithm Adapted by Convolutional Neural Network to Echogenicity Index Computation. ITAT2020 Conference, CEUR Workshop Proceedings, Vol. 2718, 2020, pp. 223 – 229. ISSN: 1613-0073.
This research is supported by the grant SGS11/LF/2022 and Institute of Informatics Silesian University in Opava, the data supported by the project ROKA CZ.02.2.69/0.0/0.0/18_054/0014592.
conference
Proceedings of 22nd International Multidisciplinary Scientific GeoConference SGEM 2022
22nd International Multidisciplinary Scientific GeoConference SGEM 2022, 04 - 10 July, 2022
Proceedings Paper
STEF92 Technology
International Multidisciplinary Scientific GeoConference SGEM
SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Serbian Acad Sci and Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts and Letters; Acad Fine Arts Zagreb Croatia; Croatian Acad Sci and Arts; Acad Sci Moldova; Montenegrin Acad Sci and Arts; Georgian Acad Sci; Acad Fine Arts and Design Bratislava; Turkish Acad Sci.
59-66
04 - 10 July, 2022
website
8474
substantia nigra ultrasound imaging, substantia nigra echogenicity, BMODE images, MRI substantia nigra, comparison B-MODE MRI substantia nigra

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