Optimizing sensitivity and dynamic range of silicon photomultipliers for frequency-domain near infrared spectroscopy

Review of recent advances in frequency-domain near-infrared spectroscopy technologies [Invited]

Over the past several decades, near-infrared spectroscopy (NIRS) has become a popular research and clinical tool for non-invasively measuring the oxygenation of biological tissues, with particular emphasis on applications to the human brain. In most cases, NIRS studies are performed using continuous-wave NIRS (CW-NIRS), which can only provide information on relative changes in chromophore concentrations, such as oxygenated and deoxygenated hemoglobin, as well as estimates of tissue oxygen saturation. Another type of NIRS known as frequency-domain NIRS (FD-NIRS) has significant advantages: it can directly measure optical pathlength and thus quantify the scattering and absorption coefficients of sampled tissues and provide direct measurements of absolute chromophore concentrations. This review describes the current status of FD-NIRS technologies, their performance, their advantages, and their limitations as compared to other NIRS methods. Significant landmarks of technological progress include the development of both benchtop and portable/wearable FD-NIRS technologies, sensitive front-end photonic components, and high-frequency phase measurements. Clinical applications of FD-NIRS technologies are discussed to provide context on current applications and needed areas of improvement. The review concludes by providing a roadmap toward the next generation of fully wearable, low-cost FD-NIRS systems.

Design for a low-cost heterodyne frequency domain-diffuse optical spectroscopy system

A design for a low-cost, heterodyne, frequency domain-diffuse optical spectroscopy system is presented and validated. The system uses a single wavelength of 785 nm and a single detector to illustrate the capability, but is built in a modular fashion to make it easily expandable to additional wavelengths and detectors. The design incorporates methods to allow software-based control over the system operating frequency, laser diode output amplitude, and detector gain. Validation methods include characterization of electrical designs as well as determination of the system stability and accuracy using tissue-mimicking optical phantoms. The system requires only basic equipment for its construction and can be built for under $ 600 .

Lawrence K, Tachtsidis I, Tong Y, Torricelli A, Urner T, Wabnitz H, Wolf M, Wolf U, Xu S, Yang C, Yodh AG, Yücel MA, Zhou W. Optical imaging and spectroscopy for the study of the human brain: status report

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.

Tutorial on the Use of Deep Learning in Diffuse Optical Tomography

Diffuse optical tomography using deep learning is an emerging technology that has found impressive medical diagnostic applications. However, creating an optical imaging system that uses visible and near-infrared (NIR) light is not straightforward due to photon absorption and multi-scattering by tissues. The high distortion levels caused due to these effects make the image reconstruction incredibly challenging. To overcome these challenges, various techniques have been proposed in the past, with varying success. One of the most successful techniques is the application of deep learning algorithms in diffuse optical tomography. This article discusses the current state-of-the-art diffuse optical tomography systems and comprehensively reviews the deep learning algorithms used in image reconstruction. This article attempts to provide researchers with the necessary background and tools to implement deep learning methods to solve diffuse optical tomography.

Ultrahigh-speed point scanning two-photon microscopy using high dynamic range silicon photomultipliers

Conventional two-photon microscopes use photomultiplier tubes, which enable high sensitivity but can detect relatively few photons per second, forcing longer pixel integration times and limiting maximum imaging rates. We introduce novel detection electronics using silicon photomultipliers that greatly extend dynamic range, enabling more than an order of magnitude increased photon detection rate as compared to state-of-the-art photomultiplier tubes. We demonstrate that this capability can dramatically improve both imaging rates and signal-to-noise ratio (SNR) in two-photon microscopy using human surgical specimens. Finally, to enable wider use of more advanced detection technology, we have formed the OpenSiPM project, which aims to provide open source detector designs for high-speed two-photon and confocal microscopy.