Miu Tamamitsu

Design for sequentially timed all-optical mapping photography with optimum temporal performance

A recently developed ultrafast burst imaging method known as sequentially timed all-optical mapping photography (STAMP) [Nat. Photonics8, 695 (2014)10.1038/nphoton.2014.163] has been shown effective for studying a diverse range of complex ultrafast phenomena. Its all-optical image separation circumvents mechanical and electronic restrictions that traditional burst imaging methods have long struggled with, hence realizing ultrafast, continuous, burst-type image recording at a fame rate far beyond what is achievable with conventional methods. In this Letter, considering various design parameters and limiting factors, we present an optimum design for STAMP in terms of temporal properties including exposure time and frame rate. Specifically, we first derive master equations that can be used to predict the temporal performance of a STAMP system and then analyze them to realize optimum conditions. This Letter serves as a general guideline for the camera parameters of a STAMP system with optimum temporal performance that is expected to be of use for tackling problems in science that are previously unsolvable with conventional imagers.

Spectrum slicer for snapshot spectral imaging

Abstract. We propose and demonstrate an optical component that overcomes critical limitations in our previously demonstrated high-speed multispectral videography—a method in which an array of periscopes placed in a prism-based spectral shaper is used to achieve snapshot multispectral imaging with the frame rate only limited by that of an image-recording sensor. The demonstrated optical component consists of a slicing mirror incorporated into a 4f-relaying lens system that we refer to as a spectrum slicer (SS). With its simple design, we can easily increase the number of spectral channels without adding fabrication complexity while preserving the capability of high-speed multispectral videography. We present a theoretical framework for the SS and its experimental utility to spectral imaging by showing real-time monitoring of a dynamic colorful event through five different visible windows.

Spatial mapping and analysis of aerosols during a forest fire using computational mobile microscopy

Forest fires are a major source of particulate matter (PM) air pollution on a global scale. The composition and impact of PM are typically studied using only laboratory instruments and extrapolated to real fire events owing to a lack of analytical techniques suitable for field-settings. To address this and similar field test challenges, we developed a mobilemicroscopy- and machine-learning-based air quality monitoring platform called c-Air, which can perform air sampling and microscopic analysis of aerosols in an integrated portable device. We tested its performance for PM sizing and morphological analysis during a recent forest fire event in La Tuna Canyon Park by spatially mapping the PM. The result shows that with decreasing distance to the fire site, the PM concentration increases dramatically, especially for particles smaller than 2 µm. Image analysis from the c-Air portable device also shows that the increased PM is comparatively strongly absorbing and asymmetric, with an aspect ratio of 0.5–0.7. These PM features indicate that a major portion of the PM may be open-flame-combustion-generated element carbon soot-type particles. This initial small-scale experiment shows that c-Air has some potential for forest fire monitoring.

A robust holographic autofocusing criterion based on edge sparsity: Comparison of Gini index and Tamura coefficient for holographic autofocusing based on the edge sparsity of the complex optical wavefront

The Sparsity of the Gradient (SoG) is a robust autofocusing criterion for holography, where the gradient modulus of the complex refocused hologram is calculated, on which a sparsity metric is applied. Here, we compare two different choices of sparsity metrics used in SoG, specifically, the Gini index (GI) and the Tamura coefficient (TC), for holographic autofocusing on dense/connected or sparse samples. We provide a theoretical analysis predicting that for uniformly distributed image data, TC and GI exhibit similar behavior, while for naturally sparse images containing few high-valued signal entries and many low-valued noisy background pixels, TC is more sensitive to distribution changes in the signal and more resistive to background noise. These predictions are also confirmed by experimental results using SoG-based holographic autofocusing on dense and connected samples (such as stained breast tissue sections) as well as highly sparse samples (such as isolated Giardia lamblia cysts). Through these experiments, we found that ToG and GoG offer almost identical autofocusing performance on dense and connected samples, whereas for naturally sparse samples, GoG should be calculated on a relatively small region of interest (ROI) closely surrounding the object, while ToG offers more flexibility in choosing a larger ROI containing more background pixels.

A deep learning-enabled portable imaging flow cytometer for cost-effective, high-throughput, and label-free analysis of natural water samples

We report a deep learning-enabled field-portable and cost-effective imaging flow cytometer that automatically captures phase-contrast color images of the contents of a continuously flowing water sample at a throughput of 100 mL/h. The device is based on partially coherent lens-free holographic microscopy and acquires the diffraction patterns of flowing micro-objects inside a microfluidic channel. These holographic diffraction patterns are reconstructed in real time using a deep learning-based phase-recovery and image-reconstruction method to produce a color image of each micro-object without the use of external labeling. Motion blur is eliminated by simultaneously illuminating the sample with red, green, and blue light-emitting diodes that are pulsed. Operated by a laptop computer, this portable device measures 15.5 cm × 15 cm × 12.5 cm, weighs 1 kg, and compared to standard imaging flow cytometers, it provides extreme reductions of cost, size and weight while also providing a high volumetric throughput over a large object size range. We demonstrated the capabilities of this device by measuring ocean samples at the Los Angeles coastline and obtaining images of its micro- and nanoplankton composition. Furthermore, we measured the concentration of a potentially toxic alga (Pseudo-nitzschia) in six public beaches in Los Angeles and achieved good agreement with measurements conducted by the California Department of Public Health. The cost-effectiveness, compactness, and simplicity of this computational platform might lead to the creation of a network of imaging flow cytometers for large-scale and continuous monitoring of the ocean microbiome, including its plankton composition.Bio-analysis: Rapidly spotting toxicity in a drop of the oceanA portable device that combines holographic imaging with artificial intelligence can rapidly detect potentially harmful algae in ocean water. Aydogan Ozcan, Zoltan Gorocs and colleagues from the University of California Los Angeles in the United States developed an inexpensive flow cytometer that pumps water samples containing tiny marine organisms, past an LED chip pulsing red, blue, and green light simultaneously. Deep learning algorithms trained to recognize background signals automatically analyze the holographic interference patterns created by the marine organisms and rapidly generate color images with microscale resolution. Sample throughput is boosted 10-fold over conventional imaging flow cytometry by avoiding the use of lenses. Using a lightweight and inexpensive prototype, the team monitored plankton levels at six public beaches and detected a likely toxic organism, the algae Pseudo-nitzschia, at levels matching those from public health laboratories.

Ultrafast broadband Fourier-transform CARS spectroscopy operating at 50,000 spectra/second

We present a coherent Raman scattering (CRS) spectroscopy technique achieving a CRS spectral acquisition rate of 50,000 spectra/second over a Raman spectral region of 200 - 1430 cm-1 with a resolution of 4.2 cm-1. This ultrafast, broadband and high-resolution CRS spectroscopic performance is realized by a polygonal Fourier-domain delay line serving as an ultra-rapid optical-path-length scanner in a broadband Fourier-transform coherent anti-Stokes Raman scattering (CARS) spectroscopy platform. We present a theoretical description of the technique and demonstrate continuous, ultrafast, broadband, and high-resolution CARS spectroscopy on a liquid toluene sample using our proof-of-concept setup.