M.V. Polyakov1, A.V. Khoperskov2, A.G. Gudkov3, S.V. Chizhikov4

1,2 Volgograd State University (Volgograd, Russia)

3,4 BMSTU (Moscow, Russia)

The combined thermometry method is based on simultaneous measurement of the infrared radiation of the tissue surface and the intrinsic microwave radiation of the internal regions, which makes it possible to determine the brightness temperature. The latter is determined by the internal distribution of the thermodynamic temperature. Thus, the problem lies in the construction of mathematical models that should describe the electromagnetic and thermal fields inside the biological tissue, taking into account the realistic internal multicomponent structure of the biological tissue or organ. The main goal of the work is to develop a mathematical model for determining the brightness temperature in biological tissues in order to use it in medical tasks, such as the diagnosis and treatment of diseases associated with changes in tissue temperature. The paper provides an overview of the application of mathematical modeling of physical processes that determine the method of microwave radiothermometry (RTM) for measuring internal temperature in biological tissues. The results of computational experiments are discussed in detail in the appendix to the medical diagnosis of breast diseases. The developed mathematical model makes it possible to determine the brightness temperature in biological tissues with high accuracy and reliability. This opens up new possibilities for using this model in medical tasks, such as the diagnosis and treatment of diseases associated with changes in tissue temperature. The model can also be useful for monitoring the effectiveness of treatment, especially in cases where temperature change is a key factor in treatment. In general, the developed model has great potential for use in medical practice and can significantly improve the diagnosis and treatment of various diseases.

Polyakov M.V., Khoperskov A.V., Gudkov A.G., Chizhikov S.V. Mathematical modeling of brightness temperature in biological tissues for medical problems. Nanotechnology: development and applications – XXI century. 2023. V. 15. № 2. P. 5−21. DOI: https://doi.org/10.18127/j22250980-202302-01 (in Russian)

1. Polyakov M. Computational modelling to determine the physical characteristics of biological tissues for medical diagnosis. International Journal of Engineering Systems Modelling and Simulations. 2020. 11(4). P. 214−221.
2. Khoperskov A.V., Polyakov M.V. Improving the Efficiency of Oncological Diagnosis of the Breast Based on the Combined Use of Simulation Modeling and Artificial Intelligence Algorithms. Algorithms. 2022. V. 15(8). id.292. 29 p.
3. Polyakov M., Levshinskii V., Khoperskov A. Modeling of brightness temperature in biological tissue. Journal of Physics: Conference Series. 2019. V. 1368. id.042057. 8 p.
4. Zamechnik T.V., Losev A.G., Petrenko A.Yu. Upravlyaemyi klassifikator v diagnostike raka molochnoi zhelezy po dannym mikrovolnovoi radiotermometrii. Matematicheskaya fizika i kompyuternoe modelirovanie. 2019. T. 22. № 3. S. 52−66. (in Russian)
5. Pennes H.H. Analysis of Tissue and Arterial Blood Temperatures in the Resting Human Forearm. Journal of Applied Physiology. 1948. V. 1. P. 93−122.
6. Robert J., Edrich J., Thouvenot P., Gautherie M., Escanye J.M. Millimeter-wave thermography: Preliminary clinical findings in head and neck diseases. Journal of Microwave Power. 1979. V. 14(2). P. 131−134.
7. Barrett A.H., Myers P.C., Sadowsky N.L. Detection of breast cancer by microwave radiometry. Radio Science. 1977. V. 12(6). P. 167−171.
8. Losev A.G., Svetlov A.V. Artificial Intelligence Algorithms in Diagnosis of Breast Cancer. Smart Innovation. Systems and Technologies. 2022. V. 287. P. 175−182.
9. Tarakanov A.V., Tarakanov A.A., Vesnin S., Efremov V.V., Goryanin I., Roberts N. Microwave Radiometry (MWR) temperature measurement is related to symptom severity in patients with Low Back Pain (LBP). Journal of Bodywork and Movement Therapies. 2021. V. 26. P. 548−552.
10. Chupina D.N., Sedankin M.K., Vesnin S.G. Application of modern technologies of mathematical simulation for the development of medical equipment. IEEE 11th International Conference on Application of Information and Communication Technologies (AICT). 2017. P. 1−5.
11. Sedankin M.K., Vesnin S.G., Leushin V.Yu., Dudkin D.I., Myshletsov I.I., Nazarov V.G., Agasieva S.V. Vnutripolostnaya antenna dlya mnogokanalnogo radiotermografa. Nanotekhnologii: razrabotka, primenenie – XXI vek. 2021. T. 13. № 2. S. 54−62. (in Russian)
12. Polyakov M.V., Khoperskov A.V. Create Combined Thermometric Datasets for Machine Learning in Medicine. IEEE Xplore: 4rd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA). 2022. P. 01−06.
13. Levshinskii V., Polyakov M., Losev A., Khoperskov A. Verification and Validation of Computer Models for Diagnosing Breast Cancer Based on Machine Learning for Medical Data Analysis. Communications in Computer and Information Science. 2019. V. 1084. P. 447−460.
14. Polyakov M.V., Popov I.E., Losev A.G., Khoperskov A.V. Application of computer simulation results and machine learning in analysis of microwave radiothermometry data. Mathematical Physics and Computer Simulation. 2021. V. 24. № 2. 27−37.
15. Fisher L., Fisher O., Chebanov D., Vesnin S., Goltsov A., Turnbull A., Dixon M., Kudaibergenova I., Osmonov B., Karbainov S., Popov L., Losev A., Goryanin I. Passive Microwave Radiometry and microRNA Detection for Breast Cancer Diagnostics. Diagnostics. 2023. V. 13(1). 118.
16. Vesnin S.G., Agasieva S.V., Sedankin M.K., Leushin V.Yu., Sidorov I.A., Porokhov I.O., Gudkov A.G. Postroenie gibkikh konformnykh antenn dlya izmereniya sobstvennogo izlucheniya golovnogo mozga. Nanotekhnologii: razrabotka, primenenie – XXI vek. 2022. T. 14. № 4. S. 5−18. (in Russian)
17. Agasieva S.V., Sedankin M.K., Leushin V.Y., Gudkov A.G., Zhuravleva K.V., Porokhov I.O., Gudkov G.A., Vesnin S.G. A Conformal Medical Antenna Based on a Flexible Substrate. Biomedical Engineeringthis link is disabled. 2023. V. 56(6). P. 373−377.
18. Leushin V.Y., Gudkov A.G., Sidorov I.A., Korolev A.V., Rykov S.G., Chizhikov S.V., Agasieva S.V., Porokhov I.O. Principles of Construction and Approaches to Further Improvements in Multichannel Multifrequency Radiothermographs. Biomedical Engineeringthis. 2023. V. 56(6). P. 449−452.
19. Sedankin M.K., Leushin V.Y., Agasieva S.V. et al. Medical Antennas for Microwave Radiothermometry of Biological Objects. Biomedical Engineeringthis. 2023. V. 56(6). P. 419−423.
20. Scapaticci R., Di Donato L., Catapano I., Crocco L. A feasibility study on microwave imaging for brain stroke monitoring. Progress In Electromagnetics Research B. 2012. V. 40. P. 305−324.
21. Stauffer P.R., Snow B.W., Rodrigues D.B., Salahi S., Oliveira T.R., Reudink D., Maccarini P.F. Non-invasive measurement of brain temperature with microwave radiometry: Demonstration in a head phantom and clinical case. Neuroradiology Journal. 2014. V. 27(1). P. 3−12.
22. Hand J.W., VanLeeuwen G.M.J., Mizushina S., Van deKamer J.B., Maruyama K., Sugiura T., Azzopardi D.V., Edwards A.D. Monitoring of deep brain temperature in infants using multi-frequency microwave radiometry and thermal modeling. Physics in Medicine and Biology. 2001. V. 46(7). P. 1885−1903.
23. Verma V., Lange F., Bainbridge A., Harvey-Jones K., Robertson N.J., Tachtsidis I., Mitra S. Brain temperature monitoring in newborn infants: Current methodologies and prospects. Frontiers in Pediatrics. 2022. V. 10. 1008539.
24. Shevelev O., Petrova M., Smolensky A., Osmonov B., Toimatov S., Kharybina T., Karbainov S., Ovchinnikov L., Vesnin S., Tarakanov A., Goryanin I. Using medical microwave radiometry for brain temperature measurements. Drug Discovery Today. 2022. V. 27(3). P. 881−889.
25. Leushin V.Y., Gudkov A.G., Porokhov I.O., Vesnin S.G., Sedankin M.K., Sidorov I.A., Solov'ev Y.V., Agasieva S.V., Chizhikov S.V. Possibilities of increasing the interference immunity of radiothermograph applicator antennas for brain diagnostics. Sensors and Actuators A: Physical. 2022. V. 337. 113439.
26. Polyakov M.V. Matematicheskoe modelirovanie dinamiki teplovykh protsessov v mnogokomponentnykh biologicheskikh tkanyakh: analiz prostranstvennykh raspredelenii termodinamicheskoi i yarkostnoi temperatur. Dis. … kand. tekhn. nauk. Volgograd. 2022. 178 s. (in Russian)
27. Pennes H.H. Analysis of Tissue and Arterial Blood Temperatures in the Resting Human Forearm. Journal of applied physiology. 1948. V. 1 (2). P. 93−122.
28. Gonzalez F.J. Thermal simulation of breast tumors. Revista Mexicana de Fisica. 2007. V. 53(4). P. 323−326.
29. Zuluaga-Gomez J., Zerhouni N., Al Masry Z., Devalland C., Varnier C. A survey of breast cancer screening techniques: thermography and electrical impedance tomography. Journal of Medical Engineering & Technology. 2019. V. 43(5). P. 305−322.
30. Vesnin S.G., Sedakin M.K. Development of antenna-applicator series for tissue temperature non-invasive measurement of a human. Eng. J. Sci. InnoV. 2012. V. 11. P. 1−18.
31. Kublanov V., Borisov V., Babich M. Simulation the distribution of thermodynamic temperatures and microwave radiation of the human head. Computer Methods and Programs in Biomedicine. 2020. V. 190. 105377.
32. Krenzer A., Makowski K., Hekalo A., Fitting D., Troya J., Zoller W.G., Hann A., Puppe F. Fast machine learning annotation in the medical domain: a semi-automated video annotation tool for gastroenterologists. BioMedical Engineering Online. 2022. V. 21. № 1. 33.
33. Levshinskii V., Galazis C., Losev A., Zamechnik T., Kharybina T., Vesnin S. Goryanin I. Using AI and passive medical radiometry for diagnostics (MWR) of venous diseases. Computer Methods and Programs in Biomedicine. 2022. V. 215. 106611.
34. Onemli E., Joof S., Aydinalp C., Pastacı Özsobacı N., Ateş Alkan F., Kepil N., Rekik I., Akduman I., Yilmaz T. Classification of rat mammary carcinoma with large scale in vivo microwave measurements. Scientific Reports. 2022. V. 12. № 1. 349.
35. Germashev I.V., Dubovskaya V.I. Primenenie modelei nechetkoi matematiki dlya resheniya zadach meditsinskoi diagnostiki. Matematicheskaya fizika i kompyuternoe modelirovanie. 2021. T. 24 № 4. S. 53−66. (in Russian)
36. Ferro A., Kotecha S., Fan K. Machine learning in point-of-care automated classification of oral potentially malignant and malignant disorders: a systematic review and meta-analysis. Scientific Reports. 2022. V. 12. № 1. 13797.
37. Polyakov M., Khoperskov A., Borisovskii E., Emelyanov E. The using of machine learning and neural networks in the processing of computer simulation results for medical diagnostics. CEUR Workshop Proceedings. 2020. V. 2667. P. 189−192.
38. Bardati F., Iudicello S. Modeling the Visibility of Breast Malignancy by a Microwave Radiometer. IEEE Transactions on Biomedical Engineering. 2008. № 55. P. 214−221.