Abstract
Laser photocoagulation is one of the most frequently used treatment approaches in ophthalmology for a variety of retinal diseases. Depending on indication, treatment intensity varies from application of specific micro injuries down to gentle temperature increases without inducing cell damage. Especially for the latter, proper energy dosing is still a challenging issue, which mostly relies on the physician’s experience. Pulsed laser photoacoustic temperature measurement has already proven its ability for automated irradiation control during laser treatment but suffers from a comparatively high instrumental effort due to combination with a conventional continuous wave treatment laser. In this paper, a simplified setup with a single pulsed laser at 10 kHz repetition rate is presented. The setup combines the instrumentation for treatment as well as temperature measurement and control in a single device. In order to compare the solely pulsed heating with continuous wave (cw) tissue heating, pulse energies of 4 µJ were applied with a repetition rate of 1 kHz to probe the temperature rise, respectively. With the same average laser power of 60 mW an almost identical temporal temperature course was retrieved in both irradiation modes as expected. The ability to reach and maintain a chosen aim temperature of 41 °C is demonstrated by means of model predictive control (MPC) and extended Kalman filtering at a the measurement rate of 250 Hz with an accuracy of less than ±0.1 °C. A major advantage of optimization-based control techniques like MPC is their capability of rigorously ensuring constraints, e.g., temperature limits, and thus, realizing a more reliable and secure temperature control during retinal laser irradiation.
Funding source: German Research Foundation (DFG)
Award Identifier / Grant number: MU 3929/3-1, WO 2056/7-1, BR 1349/6-1
Acknowledgments
We also gratefully acknowledge C. Kren and V. Danicke from the Medical laser Center Lübeck for supporting this work.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: This work is funded by the German Research Foundation (DFG) under the project number 430154635 (MU 3929/3-1, WO 2056/7-1, BR 1349/6-1).
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
[1] Early Treatment Diabetic Retinopathy Study Research Group, “Early photocoagulation for diabetic retinopathy,” Ophthalmology, vol. 98, no. 5, pp. 766–785, 1991.10.1016/S0161-6420(13)38011-7Search in Google Scholar
[2] Early Treatment Diabetic Retinopathy Study Research Group, “Photocoagulation for diabetic macular edema,” Arch. Ophthalmol., vol. 103, pp. 1796–1806, 1985.10.1001/archopht.1985.01050120030015Search in Google Scholar
[3] A. M. Shah, N. M. Bressler, and L. M. Jampol, “Does laser still have a role in the management of retinal vascular and neovascular diseases?” Am. J. Ophthalmol., vol. 152, no. 3, pp. 332–339.e1, 2011, https://doi.org/10.1016/j.ajo.2011.04.015.Search in Google Scholar PubMed
[4] The Central Vein Occlusion Study Group, “Natural history and clinical management of central retinal vein occlusion,” Arch. Ophthalmol., vol. 115, pp. 486–491, 1997, https://doi.org/10.1001/archopht.1997.01100150488006.Search in Google Scholar PubMed
[5] Branch Vein Occlusion Study Group, “Argon laser scatter photocoagulation for prevention of neovascularization and vitreous hemorrhage in branch vein occlusion. A randomized clinical trial. Branch Vein Occlusion Study Group,” Arch. Ophthalmol., vol. 104, pp. 34–41, 1986, https://doi.org/10.1001/archopht.1986.01050130044017.Search in Google Scholar PubMed
[6] G. Meyer-Schwickerath, “Licht Koagulation, eine Methode zur Behandlung und Verhuetung der Netzhautabloesung,” Albrecht Von Graefes Arch. Ophthalmol., vol. 156, no. 1, pp. 2–34, 1954, https://doi.org/10.1007/bf00703328.Search in Google Scholar
[7] S. Arrhenius, “Über die Reaktionsgeschwindigkeit bei der Inversion von Rohrzucker durch Säuren,” Z. Phys. Chem., vol. 4U, no. 1, pp. 226–248, 1889, https://doi.org/10.1515/zpch-1889-0416.Search in Google Scholar
[8] D. Lavinsky and D. Palanker, “Nondamaging photothermal therapy for the retina: initial clinical experience with chronic central serous retinopathy,” Retina, vol. 35, no. 2, pp. 213–222, 2015, https://doi.org/10.1097/iae.0000000000000340.Search in Google Scholar
[9] A. Baade, C. von der Burchard, M. Lawin, et al.., “Power-controlled temperature guided retinal laser therapy,” J. Biomed. Opt., vol. 22, no. 11, pp. 1–11, 2017, https://doi.org/10.1117/1.jbo.22.11.118001.Search in Google Scholar PubMed
[10] M. L. Denton, G. D. Noojin, M. S. Foltz, et al.., “Spatially correlated microthermography maps threshold temperature in laser-induced damage,” J. Biomed. Opt., vol. 16, no. 3, p. 36003, 2011, https://doi.org/10.1117/1.3548881.Search in Google Scholar PubMed
[11] S. Y. Schmidt and R. D. Peisch, “Melanin concentration in normal human retinal pigment epithelium. Regional variation and age-related reduction,” Investig. Ophthalmol. Vis. Sci., vol. 27, no. 7, pp. 1063–1067, 1986.Search in Google Scholar
[12] W. J. Geeraets, R. C. Williams, G. U. Chan, W. T. HamJr, D. GuerryIII, and F. H. Schmidt, “The relative absorption of thermal energy in retina and choroid,” Investig. Ophthalmol. Vis. Sci., vol. 1, no. 3, pp. 340–347, 1962.Search in Google Scholar
[13] R. Brinkmann, S. Koinzer, K. Schlott, et al.., “Real-time temperature determination during retinal photocoagulation on patients,” J. Biomed. Opt., vol. 17, no. 6, p. 61219, 2012, https://doi.org/10.1117/1.JBO.17.6.061219.Search in Google Scholar PubMed
[14] K. Schlott, S. Koinzer, A. Baade, R. Birngruber, J. Roider, and R. Brinkmann, “Lesion strength control by automatic temperature guided retinal photocoagulation,” J. Biomed. Opt., vol. 21, no. 9, p. 98001, 2016, https://doi.org/10.1117/1.JBO.21.9.098001.Search in Google Scholar PubMed
[15] K. Schlott, S. Koinzer, L. Ptaszynski, et al.., “Automatic temperature controlled retinal photocoagulation,” J. Biomed. Opt., vol. 17, no. 6, p. 61223, 2012, https://doi.org/10.1117/1.JBO.17.6.061223.Search in Google Scholar PubMed
[16] C. Herzog, O. Thomsen, B. Schmarbeck, M. Siebert, and R. Brinkmann, “Temperature-controlled laser therapy of the retina via robust adaptive H∞-control,” Automatisierungstechnik, vol. 66, no. 12, pp. 1051–1063, 2018, https://doi.org/10.1515/auto-2018-0066.Search in Google Scholar
[17] H. S. Abbas, C. Kren, V. Danicke, C. Herzog, and R. Brinkmann, “Toward feedback temperature control for retinal laser treatment,” in Medical Laser Applications and Laser-Tissue Interactions IX, Translation of Lasers and Biophotonics Technologies and Procedures: Toward the Clinic, L. D. Lilge and C. M. Philipp, Eds., Munich, Germany, SPIE, 2019, p. 10.10.1117/12.2527169Search in Google Scholar
[18] V. Kleyman, M. Schaller, M. Wilson, et al.., “State and parameter estimation for model-based retinal laser treatment,” in 7th IFAC Conference on Nonlinear Model Predictive Control 2021, 2021. 10.1016/j.ifacol.2021.08.552Search in Google Scholar
[19] J. Kandulla, H. Elsner, R. Birngruber, and R. Brinkmann, “Noninvasive optoacoustic online retinal temperature determination during continuous-wave laser irradiation,” J. Biomed. Opt., vol. 11, no. 4, p. 41111, 2006, https://doi.org/10.1117/1.2236301.Search in Google Scholar PubMed
[20] R. Birngruber, F. Hillenkamp, and V. P. Gabel, “Theoretical investigations of laser thermal retinal injury,” Health Phys., vol. 48, pp. 781–796, 1985, https://doi.org/10.1097/00004032-198506000-00006.Search in Google Scholar PubMed
[21] V. Kleyman, H. Gernandt, K. Worthmann, H. S. Abbas, R. Brinkmann, and M. A. Müller, “Modeling and parameter identification for real-time temperature controlled retinal laser therapies,” Automatisierungstechnik, vol. 68, no. 11, pp. 953–966, 2020, https://doi.org/10.1515/auto-2020-0074.Search in Google Scholar
[22] C. K. Chui and G. Chen, Kalman Filtering, Cham, Springer International Publishing, 2017.10.1007/978-3-319-47612-4Search in Google Scholar
[23] U. Baur, C. Beattie, P. Benner, and S. Gugercin, “Interpolatory projection methods for parameterized model reduction,” SIAM J. Sci. Comput., vol. 33, no. 5, pp. 2489–2518, 2011, https://doi.org/10.1137/090776925.Search in Google Scholar
[24] J. B. Rawlings, D. Q. Mayne, and M. M. Diehl, Model Predictive Control: Theory, Computation, and Design, 2nd ed. Santa Barbara, CA, USA, Nob Hill Publishing, LLC, 2019.Search in Google Scholar
[25] B. Stellato, G. Banjac, P. Goulart, A. Bemporad, and S. Boyd, “OSQP: an operator splitting solver for quadratic programs,” Math. Prog. Comp., vol. 12, no. 4, pp. 637–672, 2020, https://doi.org/10.1007/s12532-020-00179-2.Search in Google Scholar
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