(45-3) 15 * << * >> * Russian * English * Content * All Issues
Development of vector algorithm using CUDA technology for three-dimensional retinal laser coagulation process modeling
A.S. Shirokanev 1,2, N.A. Andriyanov 3, N.Y. Ilyasova 1,2
1 Samara National Research University, 443086, Samara, Russia, Moskovskoye Shosse 34,
2 IPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS,
443001, Samara, Russia, Molodogvardeyskaya 151,
3 Financial University under the Government of the Russian Federation,
125167, Moscow, Russia, Leningradskii prospekt 49
PDF, 2414 kB
Full text of article: Russian language.
For diabetic retinopathy treatment, laser coagulation is used in modern practice. During the laser surgery process, the parameters of laser exposure are selected manually by a doctor, which requires the doctor to have sufficient experience and knowledge to achieve a therapeutic effect. On the basis of mathematical modeling of the laser coagulation process, it is possible to estimate the crucial parameters without performing an operation. However, the retina has a rather complex structure, and when even low-cost numerical methods are used for modeling, it takes a long time to obtain a result. In this regard, the development of time-efficient algorithms for three-dimensional modeling is an urgent task, since the use of such algorithms will provide a compre-hensive study within a limited time.
In this paper, we study the execution time of algorithms that implement various variations in the application of the splitting method and the finite difference method, adapted to the set problem of heat conduction. The study reveals the most efficient algorithm, which is then vectorized and implemented using the CUDA technology. The study was carried out using Intel Core i7-10875H and Nvidia RTX 2080 MAX Q and showed that an analog of the vector algorithm, focused on solving a multidimensional heat conduction problem, provides an acceleration of no more than 1.5 times compared to the sequential version. The developed vector-based algorithm, focused on the application of the sweep method in all directions of the three-dimensional problem, significantly reduces the time spent on copying into the memory of the video card and provides a 40-fold acceleration in comparison with the sequential three-dimensional modeling algorithm. On the basis of the same approach, a parallel algorithm of mathematical modeling was developed, which provided a 20-fold acceleration at full processor load.
diabetic retinopathy, laser coagulation, mathematical modeling, heat conduction equation, parallel algorithms, vector algorithms, CUDA.
Shirokanev AS, Andriyanov NA, Ilyasova NY. Development of vector algorithm using CUDA technology for three-dimensional retinal laser coagulation process modeling. Computer Optics 2021; 45(3): 427-437. DOI: 10.18287/2412-6179-CO-828.
The reported study was funded by RFBR, project numbers 19-31-90160, 19-29-01135, and by the Ministry of Science and Higher Education of the Russian Federation within the State assignment to the Samara University and FSRC "Crystallography and Photonics" RAS.
- Gafurov SD, Katakhonov ShM, Holmonov MM. Features of the use of lasers in medicine [In Russian]. Eur Sci J 2019; 3(45): 92-95.
- Kotsur TV, Izmailov AS. Comparative estimation of laser coagulation efficiency in macular and microphotocoagulation of high density in diabetic maculopathy treatment [In Russian]. Ophthalmology Journal 2016; 9(4): 43-45. DOI: 10.17816/OV9443-45.
- Zamytsky EA Laser treatment of diabetic macular edema [In Russian]. Postgraduate Bulletin of the Volga Region 2015; 1-2: 74-80.
- Kozak I, Luttrull J. Modern retinal laser therapy. Saudi J Ophthalmol 2014; 29(2): 137-146.
- Doga AV, Kachalina GF, Pedanova EK, Buryakov DA Modern diagnostic and treatment aspects of diabetic macular edema. Ophthalmology, Diabetes 2014; 4: 51-59.
- Whiting DR, Guariguata L, Weil C, Shaw J. IDF diadetes atlas: global estimates of the prevalence of diabetes for 2011 and 2030. Diabetes Res. Clin. Pract 2011; 94(3): 311–321.
- Bratko GV, Chernykh VV, Sazonova OV, Kovaleva MV, Sidorova EG, Shishkо AP, Mirochnik Lyu. On the early diagnosis and frequency of occurrence of diabetic macular edema and group formation at risk of its development. Siberian Scientific Medical Journal 2015; 35(1): 33–36.
- Vorobieva IV, Merkushenkova DA. Diabetic retinopathy in type two diabetic patients: Epidemiology and modern view on the pathogenesis. Review [In Russian]. Ophthalmology in Russia 2012; 9(4): 18-21. DOI: 10.18008/1816-5095-2012-4-18-21.
- Amirov AN, Abdulaeva EA, Minkhuzina EL. diabetic macular edema. Epidemiology, pathogenesis, diagnosis, clinical features, treatment. Kazan Medical Journal 2015; 96(1): 70-74.
- Astakhov YuS, Shadrichev FE, Krasavina MI, Grigorieva NN. Modern approaches to diabetic macular edema treatment [In Russian]. Ophthalmology Journal 2009; 2(4): 59-69.
- Iskhakova AG. The results of the clinical economic analysis of the treatment of patients with diabetic retinopathy with macular edema. Postgraduate Bulletin of the Volga Region 2014; 1: 96 – 98.
- Umanets NN, Rozanova ZA, Alzein M. Intravitreal ranibizumab injection in the treatment of cystoid diabetic macular edema [In Russian]. Oftalmol Zh 2013; 2: 56-60.
- Cohen SM, Shen JH, Smiddy WE. Laser energy and dye fluorescence transmission through blood in vitro. Amer J Ophthalmol 1995; 119(4): 452-457.
- Zamytsky EA, Zolotarev AV, Karlova EV, Zamytsky PA. Analysis of the coagulates intensity in laser treatment of diabetic macular edema in a Navilas robotic laser system [In Russian]. Saratov Journal of Medical Scientific Research 2017; 13(2): 375-378.
- Ilyasova NYu. Estimating the geometric features of a 3D vascular structure. Computer Optics 2014; 38(3): 529-538. DOI: 10.18287/0134-2452-2014-38-3-529-538.
- Khorin PA, Ilyasova NYu, Paringer RA. Informative feature selection based on the Zernike polynomial coefficients for various pathologies of the human eye cornea. Computer Optics 2018; 42(1): 159-166. DOI: 10.18287/2412-6179-2018-42-1-159-166.
- Shirokanev AS, Kirsh DV, Ilyasova NYu, Kupriyanov AV. Investigation of algorithms for coagulate arrangement in fundus images. J Computer Optics 2018; 42(4): 712-721. DOI: 10.18287 / 2412-6179-2018-42-4-712-721.
- Ilyasova NYu. Diagnostic complex for the analysis of fundus vessels [In Russian]. Biotechnosphere 2014; 3(33): 20-24.
- Ilyasova NYu. Methods for digital analysis of the human vascular system. Literature review. Computer Optics 2013; 37(4): 511-535. DOI: 10.18287/0134-2452-2013-37-4-511-535.
- Ilyasova NYu, Ustinov AV, Kupriyanov AV, Ananin MA, Gavrilova NA Measurement of biomechanical characteristics of vessels for early diagnosis of vascular pathology of the fundus [In Russian]. Computer Optics, 2005; 27: 165-169.
- Soifer VA, Ilyasova NA, Kupriyanov AV, Khramov AG, Ananyin MA. Methods for computer diagnostics using eye's fundus images [In Russian]. Technologies of Living Systems 2008; 5(5-6): 61-71.
- Simchera VM. Methods of multivariate analysis of statistical data [In Russian]. Moscow: "Financy i Statistika" Publisher; 2008. ISBN: 978-5-279-03184-9.
- Dementyiev VE, Andriyanov NA, Vasilyiev KK. Use of images augmentation and implementation of doubly stochastic models for improving accuracy of recognition algorithms based on convolutional neural networks. In Book: Proceedings of 2020 Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO) 2020: 1-4. DOI: 10.1109/SYNCHROINFO49631.2020.9166000.
- Vasiliev KK, Dementyiev VE, Andriyanov NA. Using probabilistic statistics to determine the parameters of doubly stochastic models based on autoregression with multiple roots. J Phys Conf Ser 2019; 1368: 032019. DOI: 10.1088/1742-6596/1368/3/032019.
- Jung JJ, Gallego-Pinazo R, Lleó-Pérez A, Huz JI, Barbazetto IA. NAVILAS Laser System Focal Laser Treatment for Diabetic Macular Edema - One Year Results of a Case Series. Open Ophthalmology Journal 2013; 7: 48-53.
- Kovenya VM. Splitting algorithms for solving multidimensional problems of aerohydrodynamics [In Russian]. Novosibirsk: Publishing house of the SB RAS; 2014.
- Chernov VM. Parallel machine arithmetic for recurrent number systems in non-quadratic fields. Computer Optics 2020; 44(2): 274-281. DOI: 10.18287/2412-6179-CO-666.
- Yakobovsky MV. Introduction to parallel methods for solving problems: Textbook [In Russian]. Moscow: Publishing house of Moscow University; 2012.
- Fadeev DA. High performance 2D simulations for the problem of optical breakdown. Computer Optics 2016; 40(5): 654-658. DOI: 10.18287/2412-6179-2016-40-5-654-658.
- Polyakov MV, Khoperskov AV. Mathematical modeling of the spatial distribution of the radiation field in biological tissue: determination of the brightness temperature for diagnostics [In Russian]. Bulletin of the Volgograd State University 2016; 36(5): 73-84.
- Polyakov MV. Numerical modeling of the dynamics of temperature distribution in biological tissue [In Russian]. In Book: Proceedings of the All-Russian School-Conference of Young Scientists 2015; 1: 971-978.
- Pushkareva AE. Methods of mathematical modeling in biotissue optics. Textbook [In Russian]. Saint-Petersburg: "SPbGU ITMO" Publisher; 2008.
- Kistenev Y, Buligin A, Sandykova E, Sim E, Vrazhnov D. Modeling of IR laser radiation propagation in bio-tissues. Proc SPIE 2019; 11208: 112081Q.
- Samarsky AA. Schemes of the increased order of accuracy for the multidimensional heat conduction equation. J Comput Math Math Phys 1963; 3(5): 1107-1146.
- Anufriev IE, Osipov PA. Mathematical methods for modeling physical processes. Finite Difference Method. With solutions of typical problems: a textbook [In Russian]. Saint-Peterburg: Saint-Petersburg State Polytechnic University Publisher; 2014.
- Fedorov AA, Bykov AN. Comparison of two methods for parallelizing a run on hybrid computers with graphics accelerators [In Russian]. Problems of Atomic Science and Technology. Series: Mathematical Modeling of Physical Processes 2016; 4: 40-50.
- Shirokanev AS, Kirsh DV, Kupriyanov AV. Development of a vector algorithm for parametric identification of three-dimensional crystal lattices based on estimating the distances between two-dimensional layers [In Russian]. Proc ITNT-2017 Conference 2017; 1: 1615-1619.
© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: firstname.lastname@example.org ; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20