Adaptive Optics for Fluorescence Microscopy

Booth, Martin J.; Patton, Brian

doi:10.1016/b978-0-12-409513-7.00002-6

Cited by 9 publications

(6 citation statements)

References 38 publications

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“…Aberration is a term used to describe the deviation of light rays away from the ideal focus, leading to the inability of a microscope to form an exact representation of all the point sources of light within the imaged object (Fig. 3A,B) (Booth and Patton, 2014;Sanderson, 2019). The image of a pointlike object, as seen from a microscope, is described by the PSFa concentric geometrical 3D pattern of diffracted light that is projected through the lens (Fig.…”

Section: Challenges and Optimisation Of Etlsmentioning

confidence: 99%

Electrically tunable lenses – eliminating mechanical axial movements during high-speed 3D live imaging

Efstathiou

Draviam

2021

Journal of Cell Science

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The successful investigation of photosensitive and dynamic biological events, such as those in a proliferating tissue or a dividing cell, requires non-intervening high-speed imaging techniques. Electrically tunable lenses (ETLs) are liquid lenses possessing shape-changing capabilities that enable rapid axial shifts of the focal plane, in turn achieving acquisition speeds within the millisecond regime. These human-eye-inspired liquid lenses can enable fast focusing and have been applied in a variety of cell biology studies. Here, we review the history, opportunities and challenges underpinning the use of cost-effective high-speed ETLs. Although other, more expensive solutions for three-dimensional imaging in the millisecond regime are available, ETLs continue to be a powerful, yet inexpensive, contender for live-cell microscopy.

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Section: Challenges and Optimisation Of Etlsmentioning

confidence: 99%

Electrically tunable lenses – eliminating mechanical axial movements during high-speed 3D live imaging

Efstathiou

Draviam

2021

Journal of Cell Science

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“…These aberrations can arise from imperfections in the optical design of the microscope, but are most commonly due to inhomogeneous refractive index structures within the specimen. Adaptive optics (AO) has been built into many microscopes, restoring image quality through aberration correction by reconfigurable elements, such as deformable mirrors (DMs) or liquid crystal spatial light modulators (LC-SLMs) [1][2][3][4][5][6] . Applications of AO-enabled microscopes have ranged from deep tissue imaging in multiphoton microscopy through to the ultra-high resolution required for optical nanoscopy.…”

Section: Introductionmentioning

confidence: 99%

Universal adaptive optics for microscopy through embedded neural network control

Hu,

Hailstone,

Wang

et al. 2023

Light Sci Appl

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The resolution and contrast of microscope imaging is often affected by aberrations introduced by imperfect optical systems and inhomogeneous refractive structures in specimens. Adaptive optics (AO) compensates these aberrations and restores diffraction limited performance. A wide range of AO solutions have been introduced, often tailored to a specific microscope type or application. Until now, a universal AO solution – one that can be readily transferred between microscope modalities – has not been deployed. We propose versatile and fast aberration correction using a physics-based machine learning assisted wavefront-sensorless AO control (MLAO) method. Unlike previous ML methods, we used a specially constructed neural network (NN) architecture, designed using physical understanding of the general microscope image formation, that was embedded in the control loop of different microscope systems. The approach means that not only is the resulting NN orders of magnitude simpler than previous NN methods, but the concept is translatable across microscope modalities. We demonstrated the method on a two-photon, a three-photon and a widefield three-dimensional (3D) structured illumination microscope. Results showed that the method outperformed commonly-used modal-based sensorless AO methods. We also showed that our ML-based method was robust in a range of challenging imaging conditions, such as 3D sample structures, specimen motion, low signal to noise ratio and activity-induced fluorescence fluctuations. Moreover, as the bespoke architecture encapsulated physical understanding of the imaging process, the internal NN configuration was no-longer a “black box”, but provided physical insights on internal workings, which could influence future designs.

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“…Adaptive optics (AO) has been built into many microscopes, restoring image quality through aberration correction by reconfigurable elements, such as deformable mirrors (DMs) or liquid crystal spatial light modulators (LC-SLMs). [1][2][3][4][5][6] Applications of AO-enabled microscopes have ranged from deep tissue imaging in multiphoton microscopy through to the ultra-high resolution required for optical nanoscopy. This range of applications has led to a wide variety of AO solutions that have invariably been tailored to a specific microscope modality or application.…”

Section: Introductionmentioning

confidence: 99%

Universal adaptive optics for microscopy through embedded neural network control

Booth

Hailstone

et al. 2023

Preprint

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The resolution and contrast of microscope imaging is often affected by aberrations introduced by imperfect optical systems and inhomogeneous refractive structures in specimens. Adaptive optics (AO) compensates these aberrations and restores diffraction limited performance. A wide range of AO solutions have been introduced, often tailored to a specific microscope type or application. Until now, a universal AO solution – one that can be readily transferred between microscope modalities – has not been deployed. We propose versatile and fast aberration correction using a physics-based machine learning assisted wavefront-sensorless AO control (MLAO) method. Unlike previous ML methods, we used a bespoke neural network (NN) architecture, designed using physical understanding of image formation, that was embedded in the control loop of the microscope. The approach means that not only is the resulting NN orders of magnitude simpler than previous NN methods, but the concept is translatable across microscope modalities. We demonstrated the method on a two-photon, a three-photon and a widefield three-dimensional (3D) structured illumination microscope. Results showed that the method outperformed commonly-used model-based sensorless AO methods. We also showed that our ML-based method was robust in a range of challenging imaging conditions, such as extended 3D sample structures, specimen motion, low signal to noise ratio and activity-induced fluorescence fluctuations. Moreover, as the bespoke architecture encapsulated physical understanding of the imaging process, the internal NN configuration was no-longer a “black box”, but provided physical insights on internal workings, which could influence future designs.

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Adaptive Optics for Fluorescence Microscopy

Cited by 9 publications

References 38 publications

Electrically tunable lenses – eliminating mechanical axial movements during high-speed 3D live imaging

Electrically tunable lenses – eliminating mechanical axial movements during high-speed 3D live imaging

Universal adaptive optics for microscopy through embedded neural network control

Universal adaptive optics for microscopy through embedded neural network control

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