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Register a new userImaging dynamics beneath turbid media via parallelized single-photon detection
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Noninvasive optical imaging through dynamic scattering media has numerous important biomedical applications but still remains a challenging task. While standard methods aim to form images based upon optical absorption or fluorescent emission, it is also well-established that the temporal correlation of scattered coherent light diffuses through tissue much like optical intensity. Few works to date, however, have aimed to experimentally measure and process such data to demonstrate deep-tissue imaging of decorrelation dynamics. In this work, we take advantage of a single-photon avalanche diode (SPAD) array camera, with over one thousand detectors, to simultaneously detect speckle fluctuations at the single-photon level from 12 different phantom tissue surface locations delivered via a customized fiber bundle array. We then apply a deep neural network to convert the acquired single-photon measurements into video of scattering dynamics beneath rapidly decorrelating liquid tissue phantoms. We demonstrate the ability to record video of dynamic events occurring 5-8 mm beneath a decorrelating tissue phantom with mm-scale resolution and at a 2.5-10 Hz frame rate.
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Optical coherence tomography (OCT) is a powerful biomedical imaging technology that relies on the coherent detection of backscattered light to image tissue morphology in vivo. As a consequence, OCT is susceptible to coherent noise (speckle noise), which imposes significant limitations on its diagnostic capabilities. Here we show a method based purely on light manipulation that is able to entirely remove the speckle noise originating from turbid samples without any compromise in resolution. We refer to this method as Speckle-Free OCT (SFOCT). Using SFOCT, we succeeded in revealing small structures that are otherwise hidden by speckle noise when using conventional OCT, including the inner stromal structure of a live mouse cornea, the fine structures inside the mouse pinna, sweat ducts, and Meissners corpuscle in the human fingertip skin. SFOCT has the potential to markedly increase OCTs diagnostic capabilities of various human diseases by revealing minute features that correlate with early pathology.
In Polarization Discrimination Imaging, the amplitude of a sinusoid from a rotating analyzer, representing residual polarized light and carrying information on the object, is detected with the help of a lock-in amplifier. When turbidity increases beyond a level, the lock-in amplifier fails to detect the weak sinusoidal component in the transmitted light. In this work we have employed the principle of Stochastic Resonance and used a 3-level quantizer to detect the amplitude of the sinusoids, which was not detectable with a lock-in amplifier. In using the three level quantizer we have employed three different approaches to extract the amplitude of the weak sinusoids: (a) using the probability of the quantized output to crossover a certain threshold in the quantizer (b) maximizing the likelihood function for the quantized detected intensity data and (c) arriving at an expression for the expected power in the detected output and comparing it with the experimentally measured power. We have proven these non-linear estimation methods by detecting the hidden object from experimental data from a polarization discrimination imaging system. When the turbidity increased to L/l = 5.05 (l is the transport mean-free-path and L is the thickness of the turbid medium) the data through analysis by the proposed methods revealed the presence of the object from the estimated amplitudes. This was not possible by using only the lock-in amplifier system.
For active optical imaging, the use of single-photon detectors can greatly improve the detection sensitivity of the system. However, the traditional maximum-likelihood based imaging method needs a long acquisition time to capture clear three-dimensional (3D) image in low light-level. To tackle this problem, we present a novel imaging method for depth estimate, which can obtain the accurate 3D image in a short acquisition time. Our method combines the photon-count statistics with the temporal correlations of the reflected signal. According to the characteristics of the target surface, including the surface reflectivity, our method is capable of adaptively changing the dwell time in each pixel. The experimental results demonstrate that the proposed method can fast obtain the accurate depth image despite the existence of strong background noise.
We propose and experimentally demonstrate a high-efficiency single-pixel imaging (SPI) scheme by integrating time-correlated single-photon counting (TCSPC) with time-division multiplexing to acquire full-color images at extremely low light level. This SPI scheme uses a digital micromirror device to modulate a sequence of laser pulses with preset delays to achieve three-color structured illumination, then employs a photomultiplier tube into the TCSPC module to achieve photon-counting detection. By exploiting the time-resolved capabilities of TCSPC, we demodulate the spectrum-image-encoded signals, and then reconstruct high-quality full-color images in a single-round of measurement. Based on this scheme, the strategies such as single-step measurement, high-speed projection, and undersampling can further improve the imaging efficiency.
For conventional imaging, the imaging resolution limit is given by the Rayleigh criterion. Exploiting the prior knowledge of imaging objects sparsity and fixed optical system, imaging beyond the conventional Rayleigh limit, which is backed up by numerical simulation and experiments, is achieved by illuminating the object with single-shot thermal light and detecting the objects information at the imaging plane with some sparse-array single-pixel detectors. The quality of sub-Rayleigh imaging with sparse detection is also shown to be related to the effective number of single-pixel detectors and the detection signal-to-noise ratio at the imaging plane.
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