State and parameter estimation for retinal laser treatment

Kleyman, Viktoria; Schaller, Manuel; Wilson, Mitsuru; Brinkmann, Ralf; Worthmann, Karl; Müller, Matthias A.

doi:10.48550/arxiv.2203.12452

Cited by 2 publications

(2 citation statements)

References 30 publications

(62 reference statements)

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“…In the future, model predictive control algorithms, compared to the PID control used here, could further reduce inaccuracies due to the conversion function by real-time parameter estimation. 29 – 31 …”

Section: Discussionmentioning

confidence: 99%

Real-Time Temperature-Controlled Retinal Laser Irradiation in Rabbits

von der Burchard,

Kren,

Fleger

et al. 2024

Trans. Vis. Sci. Tech.

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Purpose Subdamaging thermal retinal laser therapy has the potential to induce regenerative stimuli in retinal diseases, but validated dosimetry is missing. Real-time optoacoustic temperature determination and control could close this gap. This study investigates a first in vivo application. Methods Two iterations of a control module that were optically coupled in between a continuous-wave commercial laser source and a commercial slit lamp were evaluated on chinchilla rabbits. The module allows extraction of the temperature rise in real time and can control the power of the therapy laser such that a predefined temperature rise at the retina is quickly achieved and held constant. Irradiations with aim temperatures from 45°C to 69°C were performed on a diameter of 200 µm and a heating time of 100 ms. Results We analyzed 424 temperature-guided irradiations in nine eyes of five rabbits. The mean difference between the measured and aim temperature was −0.04°C ± 0.98°C. The following ED 50 values for visibility thresholds could be determined: 58.6°C for funduscopic visibility, 57.7°C for fluorescein angiography, and 57.0°C for OCT. In all measurements, the correlation of tissue effect was higher to the temperature than to the average heating laser power used. Conclusions The system was able to reliably perform temperature-guided irradiations, which allowed for better tissue effect control than simple power control. This approach could enhance the accuracy, safety, and reproducibility of thermal stimulating laser therapy. Translational Relevance This study is a bridge between preclinical ex vivo experiments and a pilot clinical study.

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Section: Discussionmentioning

confidence: 99%

Real-Time Temperature-Controlled Retinal Laser Irradiation in Rabbits

von der Burchard,

Kren,

Fleger

et al. 2024

Trans. Vis. Sci. Tech.

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“…The first discussions and applications of the JEKF approach started in the 1960s for the estimation of linear systems (in which there is a bilinear relation between the states and parameters) [6][7][8][9][10]. However, the JEKF is still very popular, with several new applications in different areas [5,[11][12][13][14][15][16][17][18][19][20][21], and with unsolved problems [22,23]. Furthermore, the JEKF has been established as the least expensive nonlinear estimator for moderate-size systems in terms of computational cost because the practical implementation of adaptive controllers using microcontrollers (and/or minicomputers and/or microprocessors) requires numerically economical and robust algorithms, such as the JEKF [11,24].…”

Section: Introductionmentioning

confidence: 99%

How Not to Make the Joint Extended Kalman Filter Fail with Unstructured Mechanistic Models

Iglesias,

Bolic

2024

Sensors

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The unstructured mechanistic model (UMM) allows for modeling the macro-scale of a phenomenon without known mechanisms. This is extremely useful in biomanufacturing because using the UMM for the joint estimation of states and parameters with an extended Kalman filter (JEKF) can enable the real-time monitoring of bioprocesses with unknown mechanisms. However, the UMM commonly used in biomanufacturing contains ordinary differential equations (ODEs) with unshared parameters, weak variables, and weak terms. When such a UMM is coupled with an initial state error covariance matrix P(t=0) and a process error covariance matrix Q with uncorrelated elements, along with just one measured state variable, the joint extended Kalman filter (JEKF) fails to estimate the unshared parameters and state simultaneously. This is because the Kalman gain corresponding to the unshared parameter remains constant and equal to zero. In this work, we formally describe this failure case, present the proof of JEKF failure, and propose an approach called SANTO to side-step this failure case. The SANTO approach consists of adding a quantity to the state error covariance between the measured state variable and unshared parameter in the initial P(t = 0) of the matrix Ricatti differential equation to compute the predicted error covariance matrix of the state and prevent the Kalman gain from being zero. Our empirical evaluations using synthetic and real datasets reveal significant improvements: SANTO achieved a reduction in root-mean-square percentage error (RMSPE) of up to approximately 17% compared to the classical JEKF, indicating a substantial enhancement in estimation accuracy.

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State and parameter estimation for retinal laser treatment

Cited by 2 publications

References 30 publications

Real-Time Temperature-Controlled Retinal Laser Irradiation in Rabbits

Real-Time Temperature-Controlled Retinal Laser Irradiation in Rabbits

How Not to Make the Joint Extended Kalman Filter Fail with Unstructured Mechanistic Models

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