Wavefront-sensorless adaptive optics with a laser-free spinning disk confocal microscope

Deflectometry based calibration of a deformable mirror for aberration correction and remote focusing in microscopy

Adaptive optics (AO) techniques enhance the capability of optical microscopy through precise control of wavefront modulations to compensate phase aberrations and improves image quality. However, the aberration correction is often limited due to the lack of dynamic range in existing calibration methods, such as interferometry or Shack-Hartmann (SH) wavefront sensors. Here, we use deflectometry (DF) as a calibration method for a deformable mirror (DM) to extend the available range of aberration correction. We characterised the dynamic range and accuracy of the DF-based calibration of DMs depending on the spatial frequency of the test pattern used in DF. We also demonstrated the capability of large magnitude phase control for remote-focusing over a range larger than was possible with SH sensing.

NeuWS: Neural wavefront shaping for guidestar-free imaging through static and dynamic scattering media

Diffraction-limited optical imaging through scattering media has the potential to transform many applications such as airborne and space-based imaging (through the atmosphere), bioimaging (through skin and human tissue), and fiber-based imaging (through fiber bundles). Existing wavefront shaping methods can image through scattering media and other obscurants by optically correcting wavefront aberrations using high-resolution spatial light modulators-but these methods generally require (i) guidestars, (ii) controlled illumination, (iii) point scanning, and/or (iv) statics scenes and aberrations. We propose neural wavefront shaping (NeuWS), a scanning-free wavefront shaping technique that integrates maximum likelihood estimation, measurement modulation, and neural signal representations to reconstruct diffraction-limited images through strong static and dynamic scattering media without guidestars, sparse targets, controlled illumination, nor specialized image sensors. We experimentally demonstrate guidestar-free, wide field-of-view, high-resolution, diffraction-limited imaging of extended, nonsparse, and static/dynamic scenes captured through static/dynamic aberrations.

Microscope-Cockpit: Python-based bespoke microscopy for bio-medical science

We have developed “Microscope-Cockpit” (Cockpit), a highly adaptable open source user-friendly Python-based Graphical User Interface (GUI) environment for precision control of both simple and elaborate bespoke microscope systems. The user environment allows next-generation near instantaneous navigation of the entire slide landscape for efficient selection of specimens of interest and automated acquisition without the use of eyepieces. Cockpit uses “Python-Microscope” (Microscope) for high-performance coordinated control of a wide range of hardware devices using open source software. Microscope also controls complex hardware devices such as deformable mirrors for aberration correction and spatial light modulators for structured illumination via abstracted device models. We demonstrate the advantages of the Cockpit platform using several bespoke microscopes, including a simple widefield system and a complex system with adaptive optics and structured illumination. A key strength of Cockpit is its use of Python, which means that any microscope built with Cockpit is ready for future customisation by simply adding new libraries, for example machine learning algorithms to enable automated microscopy decision making while imaging.

Python-Microscope - a new open-source Python library for the control of microscopes

Custom-built microscopes often require control of multiple hardware devices and precise hardware coordination. It is also desirable to have a solution that is scalable to complex systems and that is translatable between components from different manufacturers. Here we report Python-Microscope, a free and open-source Python library for high-performance control of arbitrarily complex and scalable custom microscope systems. Python-Microscope offers simple to use Python-based tools, abstracting differences between physical devices by providing a defined interface for different device types. Concrete implementations are provided for a range of specific hardware, and a framework exists for further expansion. Python-Microscope supports the distribution of devices over multiple computers while maintaining synchronisation via highly precise hardware triggers. We discuss the architectural features of Python-Microscope that overcome the performance problems often raised against Python and demonstrate the different use cases that drove its design: integration with user-facing projects, namely the Microscope-Cockpit project; control of complex microscopes at high speed while using the Python programming language; and use as a microscope simulation tool for software development.

Fluorescence Microscopy Light Source Review

Traditional arc-based light sources and Light Emitting Diodes (LEDs) are described, and the pros and cons of these sources with respect to fluorescence microscopy are discussed. For multi-color applications, arc-based light sources offer white light ranging from the ultraviolet (UV) to the infrared (IR), while LEDs come in a range of colors spanning the same wavelengths. The power of traditional arc-based sources is controlled with neutral density (ND) filters, reducing power across the entire range of wavelengths, while LED-based sources can be controlled directly by modulating current passing through the electronics. Similarly, arc-based sources use physical shutters to control sample exposure to light in a range of tens to hundreds of milliseconds (ms), while LEDs can be turned ON/OFF electronically in <1 ms. The complexity of comparing and measuring light power on the sample, due to normalization of available light source spectra and complex power measurements, is discussed. The superiority of LEDs for stability of light power output is covered. Direct coupling of light sources to the microscope is more cost effective and leads to higher available light power. Various options for setting up multi-color imaging, including high-speed imaging with multiple LEDs and a triple cube, are described. A brief introduction to lasers, with suggested further reading, is included in this article. Finally, the smaller environmental footprint of LEDs relative to arc-based light sources is highlighted. © 2021 Wiley Periodicals LLC.

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

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.

Microscope-Cockpit: Python-based bespoke microscopy for bio-medical science

We have developed “Microscope-Cockpit” (Cockpit), a highly adaptable open source user-friendly Python-based Graphical User Interface (GUI) environment for precision control of both simple and elaborate bespoke microscope systems. The user environment allows next-generation near instantaneous navigation of the entire slide landscape for efficient selection of specimens of interest and automated acquisition without the use of eyepieces. Cockpit uses “Python-Microscope” (Microscope) for high-performance coordinated control of a wide range of hardware devices using open source software. Microscope also controls complex hardware devices such as deformable mirrors for aberration correction and spatial light modulators for structured illumination via abstracted device models. We demonstrate the advantages of the Cockpit platform using several bespoke microscopes, including a simple widefield system and a complex system with adaptive optics and structured illumination. A key strength of Cockpit is its use of Python, which means that any microscope built with Cockpit is ready for future customisation by simply adding new libraries, for example machine learning algorithms to enable automated microscopy decision making while imaging.