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Optimization-based adaptive optics for optical coherence tomography

2017· article· en· W2606418889 sur OpenAlexfundno aff
Hans R. G. W. Verstraete

Notice bibliographique

RevueResearch Repository (Delft University of Technology) · 2017
Typearticle
Langueen
DomaineEngineering
ThématiqueOptical Coherence Tomography Applications
Établissements canadiensnon disponible
Organismes subventionnairesSimon Fraser University
Mots-clésWavefrontAdaptive opticsWavefront sensorDeformable mirrorOptical coherence tomographyOpticsInterferometryOptical pathPhysicsLens (geology)Computer science

Résumé

récupéré en direct d'OpenAlex

Optical coherence tomography (OCT) is a technique for non-invasive imaging based on low coherence interferometry. Its main application is found in ophthalmology, where it is used for 3D in vivo imaging of the cornea and the retina. OCT has evolved over the past decade as one of the most important ancillary tests in ophthalmic practice, providing great diagnostic value for disease screening and monitoring. In retinal OCT imaging, the lateral resolution is not determined by the pupil size, but instead it is limited by optical wavefront aberrations of the cornea and lens. These aberrations reduce the OCT image resolution and lower the signal to noise ratio. To obtain high quality OCT images the optical aberrations can be removed using adaptive optics (AO).<br/><br/>In general, AO consists of an adaptive optical element and a wavefront sensor. The adaptive element, such as a deformable mirror, is used to reshape the wavefront and remove the undesired aberrations. The wavefront sensor measures the aberrations by reconstructing the phase of the wavefront, which is used to determine the correction on the wavefront applied by the deformable mirror. However, the use of a wavefront sensor has some disadvantages. It requires light being directed out of the imaging path onto the wavefront sensor. This leads to a loss of signal in the imaging path and can result in non-common optical path errors in the aberrations estimation procedure. Additionally, the use of a deformable mirror and a wavefront sensor leads to a bulky and expensive OCT setup.<br/><br/>The work presented in this thesis has the goal of reducing the cost and bulkiness of an AO-OCT system. First, we investigate the influence of optical wavefront aberrations to the OCT signal strength. The establishment of the relation between aberrations and the OCT signal strength is key to estimating and correcting the aberrations based on single OCT scans. By using Fresnel optical wave propagation and determining the fiber coupling efficiency, we find that the OCT transfer function, i.e. the function that expresses the relation between the aberrations and OCT signal strength, is quasi-convex. We determine both analytically and experimentally the transfer function for both reflective and scattering media, such as a mirror and Scotch tape sample. Additionally, if the OCT system and its optical properties are well-known we demonstrate a method to correct a defocus aberration in one step. <br/><br/>Second, we use the OCT transfer function to develop and determine an efficient wavefront sensorless (WFSL) AO optimization procedure. WFSL-AO methods aim to correct the aberrations without using a wavefront sensor, but instead base the determination of the wavefront on the imaging signal itself. This eliminates the use of the wavefront sensor, its extra cost and its disadvantages from an AO-OCT setup. To keep up with the OCT imaging rate, which is of the order of several tens of kHz, the algorithm has to be computationally efficient. Furthermore, there are no analytic derivatives available for the optimization and the OCT signal is very noisy. Finally, the derivative-free optimization algorithm also has to be able to determine the aberrations accurately when dealing with a minimum number of noisy measurements. We developed the Data-based Online Nonlinear extremum-seeker (DONE) algorithm. Every iteration, the DONE algorithm updates a surrogate function, which is based on random Fourier expansions (RFE) of the OCT transfer function, with a new OCT signal measurement. The optimum of the RFE surrogate function is then found with a well-known (quasi-Newton) optimization method. We demonstrate the effectiveness of the DONE algorithm compared to other optimization algorithms for WFSL-AO on biological and non-biological samples. We conclude that DONE has a smaller convergence error, while maintaining similar or faster convergence speeds compared to the other algorithms. <br/><br/>Third, we demonstrate a fully functional WFSL-AO OCT setup for retinal imaging. We use a state-of-the-art deformable lens with 18 actuators, rather than a deformable mirror, which leads to a smaller and more integrated WFSL-AO setup. The WFSL-AO OCT setup is successfully used for in vivo retinal OCT imaging and demonstrates that the DONE algorithm can remove the ocular wavefront aberrations with the deformable lens during in vivo OCT imaging. By developing a new algorithm and exploring the options for adaptive components, we have succeeded in retinal WFSL-AO OCT.<br/><br/>In a broader perspective, we show that the DONE algorithm is suitable for other applications than WFSL-AO OCT. We demonstrate that the DONE derivative-free optimization algorithm is robust towards noisy measurements for applications in robotics, microscopy and optical beam forming networks.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,604
Score d'incertitude au seuil0,782

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,002
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,033
Tête enseignante GPT0,276
Écart entre enseignants0,243 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

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