Long working distance OCT with a compact 2f retinal scanning configuration for pediatric imaging.

Young and/or autistic children cannot be imaged with tabletop or handheld optical coherence tomography (OCT) because of their lack of attention and fear of large objects close to their face. We demonstrate a prototype retinal swept-source OCT system with a long working distance (from the last optical element to the subject's eye) to facilitate pediatric imaging. To reduce the number of optical elements and axial length compared to the traditional 4f telescope, we employ a compact 2f retinal scanning configuration and achieve a working distance of 350 mm with a 16° OCT field of view. We test our prototype system on pediatric and adult subjects.

Coherence revival multiplexed, buffered swept source optical coherence tomography: 400 kHz imaging with a 100 kHz source.

The effective speed of a swept source optical coherence tomography (SSOCT) imaging system was quadrupled using efficient sweep buffering along with coherence revival and spatial multiplexing. A polarizing beam splitter and fold mirror assembly were used to create a dual spot sample arm with a common objective designed for near-diffraction-limited retinal imaging. Using coherence revival, a variable optical delay line allowed for separate locations within a sample to be simultaneously imaged and frequency encoded by carefully controlling the optical path length of each sample path. This method can be used to efficiently quadruple the imaging speed of any SSOCT system employing a low duty-cycle laser that exhibits coherence revival. The system was used to image the retina of healthy human volunteers.

Real-time open-source FLIM analysis.

Fluorescence lifetime imaging microscopy (FLIM) provides valuable quantitative insights into fluorophores' chemical microenvironment. Due to long computation times and the lack of accessible, open-source real-time analysis toolkits, traditional analysis of FLIM data, particularly with the widely used time-correlated single-photon counting (TCSPC) approach, typically occurs after acquisition. As a result, uncertainties about the quality of FLIM data persist even after collection, frequently necessitating the extension of imaging sessions. Unfortunately, prolonged sessions not only risk missing important biological events but also cause photobleaching and photodamage. We present the first open-source program designed for real-time FLIM analysis during specimen scanning to address these challenges. Our approach combines acquisition with real-time computational and visualization capabilities, allowing us to assess FLIM data quality on the fly. Our open-source real-time FLIM viewer, integrated as a Napari plugin, displays phasor analysis and rapid lifetime determination (RLD) results computed from real-time data transmitted by acquisition software such as the open-source Micro-Manager-based OpenScan package. Our method facilitates early identification of FLIM signatures and data quality assessment by providing preliminary analysis during acquisition. This not only speeds up the imaging process, but it is especially useful when imaging sensitive live biological samples.

New Extensibility and Scripting Tools in the ImageJ Ecosystem.

ImageJ provides a framework for image processing across scientific domains while being fully open source. Over the years ImageJ has been substantially extended to support novel applications in scientific imaging as they emerge, particularly in the area of biological microscopy, with functionality made more accessible via the Fiji distribution of ImageJ. Within this software ecosystem, work has been done to extend the accessibility of ImageJ to utilize scripting, macros, and plugins in a variety of programming scenarios, e.g., from Groovy and Python and in Jupyter notebooks and cloud computing. We provide five protocols that demonstrate the extensibility of ImageJ for various workflows in image processing. We focus first on Fluorescence Lifetime Imaging Microscopy (FLIM) data, since this requires significant processing to provide quantitative insights into the microenvironments of cells. Second, we show how ImageJ can now be utilized for common image processing techniques, specifically image deconvolution and inversion, while highlighting the new, built-in features of ImageJ-particularly its capacity to run completely headless and the Ops matching feature that selects the optimal algorithm for a given function and data input, thereby enabling processing speedup. Collectively, these protocols can be used as a basis for automating biological image processing workflows. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Using PyImageJ for FLIM data processing Alternate Protocol: Groovy FLIMJ in Jupyter Notebooks Basic Protocol 2: Using ImageJ Ops for image deconvolution Support Protocol 1: Using ImageJ Ops matching feature for image inversion Support Protocol 2: Headless ImageJ deconvolution.