Time-resolved spectral-domain optical coherence tomography with CMOS SPAD sensors

Abstract

We present a first spectral-domain optical coherence tomography (SD-OCT) system deploying a complementary metal-oxide-semiconductor (CMOS) single-photon avalanche diode (SPAD) based, time-resolved line sensor. The sensor with 1024 pixels achieves a sensitivity of 87 dB at an A-scan rate of 1 kHz using a supercontinuum laser source with a repetition rate of 20 MHz, 38 nm bandwidth, and 2 mW power at 850 nm centre wavelength. In the time-resolved mode of the sensor, the system combines low-coherence interferometry (LCI) and massively parallel time-resolved single-photon counting to control the detection of interference spectra on the single-photon level based on the time-of-arrival of photons. For proof of concept demonstration of the combined detection scheme we show the acquisition of time-resolved interference spectra and the reconstruction of OCT images from selected time bins. Then, we exemplify the temporal discrimination feature with 50 ps time resolution and 249 ps timing uncertainty by removing unwanted reflections from along the optical path at a 30 mm distance from the sample. The current limitations of the proposed technique in terms of sensor parameters are analysed and potential improvements are identified for advanced photonic applications.

Quantum optical tomography based on time-resolved and mode-selective single-photon detection by femtosecond up-conversion

“…Recently, the shot-noise limited OCT has been demonstrated and its sensitivity was ~ 105 dB because the probe light power is restricted to several milliwatts 34 , 35 , which indicates that the maximal penetration depth of OCT cannot be improved without increasing the probe light power. Even if the single-photon detector (or single-photon camera) is employed to efficiently detect the OCT signal with substantially low noise counts, the sensitivity is still limited by the shot noise 36 , 37 . However, the use of the single-photon detector allows us to strongly attenuate the reference light wave, which results in the suppression of its excess intensity noise that inhibits the shot-noise-limited detection of the OCT signal.…”

Multimodal Raman spectroscopy and optical coherence tomography for biomedical analysis

“…Several efforts have been made toward developing miniaturized RS-OCT systems that suit particular in vivo applications [69][70][71][72]. Recent advancements in system designs utilize optical fibers that serve as light-guides to decouple the primary imaging head from excitation sources, spectral detection components, and OCT reference arm.…”

“…On the contrary, FD‐OCT requires either a fast‐scanning laser (with a MHz sampling rate for SS‐OCT) or a high‐speed spectrometer (with a kHz line speed for SD‐OCT) to ensure fast imaging speeds. Another important characteristic for detecting the SD‐OCT spectrum is having a large full‐well capacity, as the smallest reflectivity from the sample that can be captured in an OCT scan (i.e., sensitivity) is dependent on the electron‐storing capacity of pixel wells [72]. So, the ideal shared detector for RS and SD‐OCT would have high sensitivity, low noise, a large full‐well capacity, and fast readout; four characteristics that are seldom found in a single optical detector.…”

Optical coherence tomography and convolutional neural networks can differentiate colorectal liver metastases from liver parenchyma ex vivo

“…This is a non-invasive imaging technology, based on low-coherence interferometry, which produces real-time, high-resolution cross-sectional images at a depth of 1–3 mm, depending on tissue type and wavelength (usually 800–1300 nm). Axial and lateral resolutions of 1–20 μm can be achieved, which are high enough to identify microscopic features such as lymphatic aggregates and blood vessels (Garcia-Allende et al 2011 ; Amygdalos 2014 ; Samel and Mashimo 2019 ; Zhu et al 2020 ; Kufcsak et al 2021 ). Combining the attractive features of OCT with an efficient and accurate quantitative analysis technique would result in a powerful diagnostic tool, especially when using advanced processing modalities, such as machine learning (ML) (Aggarwal et al 2021 , Saratxaga, Bote et al 2021).…”