SignificanceTissues’ biomechanical properties, such as elasticity, are related to tissue health. Optical coherence elastography produces images of tissues based on their elasticity, but its performance is constrained by the laser power used, working distance, and excitation methods.AimWe develop a new method to reconstruct the elasticity contrast image over a long working distance, with only low-intensity illumination, and by non-contact acoustic wave excitation.ApproachWe combine single-photon vibrometry and quantum parametric mode sorting (QPMS) to measure the oscillating backscattered signals at a single-photon level and derive the phantoms’ relative elasticity.ResultsWe test our system on tissue-mimicking phantoms consisting of contrast sections with different concentrations and thus stiffness. Our results show that as the driving acoustic frequency is swept, the phantoms’ vibrational responses are mapped onto the photon-counting histograms from which their mechanical properties—including elasticity—can be derived. Through lateral and longitudinal laser scanning at a fixed frequency, a contrast image based on samples’ elasticity can be reliably reconstructed upon photon level signals.ConclusionsWe demonstrated the reliability of QPMS-based elasticity contrast imaging of agar phantoms in a long working distance, low-intensity environment. This technique has the potential for in-depth images of real biological tissue and provides a new approach to elastography research and applications.
KEYWORDS: Machine learning, Artificial intelligence, Face image reconstruction, LIDAR, 3D mask effects, 3D modeling, Transformers, 3D image reconstruction, Image restoration, 3D acquisition, Raster graphics, Microelectromechanical systems, Data modeling
We increase single-photon LiDAR capabilities via a hardware-accelerating inpainting transformer model. This model reconstructs all non-observed information within the image plane as it communicates with the beam steering hardware. We apply this to 3D time-of-flight (ToF) reconstruction, where objects obstruct each other’s line of sight. We use ToF histograms to distinguish objects within either the foreground and background, and their overlap will be treated as the dynamic mask for the model to reconstruct. We also employ this to unorthodox scanning patterns such as Lissajous and spiral, which are riddled with sparsity. Lastly, we are developing an AI MEMs system, which intelligently downsamples the image plane based off foreground masks, combating sampling redundancy. We believe that our approach will be useful in applications for imaging and sensing dynamic targets with sparse single-photon data across all domains.
We are investigating a method for identifying materials from a distance, even when they are obscured, using a technique called Quantum Parametric Mode Sorting and single photons detection. By scanning a segment of the material, we are able to capture data on the relationships between the peak count of photons reflected at each position and the location of that reflection. This information allows us to measure the relative reflectance of the material and the texture of its surface, which enables us to achieve a material recognition accuracy of 99%, even maintaining 89.17% when materials are obscured by a lossy and multi-scattering obscurant that causes up to 15.2 round-trip optical depth.
Detection signal to noise ratio lies at the heart of any remote sensing task. Matched filters have long been deployed as the optimal devices for radio and microwave receiving. However, their implements in the optics domain are challenging due to a much higher bandwidth and the intensity-only measurement by incoherent detectors. For applications involving high noise and low signal, their utilities are overly restricted by a fundamental trade-off between the signal detection efficiency and noise rejection. In this talk, I will describe a nascent approach, quantum parametric mode sorting, to addressing these practical and fundamental challenges. It is realized via quantum frequency conversion at the phase matching edge, where the signal distillation, picosecond timing tagging, and wavelength transduction are accomplished during a single pass through a nonlinear waveguide or crystal. I will report our experimental progress on using it a variety of single photon sensing and imaging tasks.
KEYWORDS: Photons, Signal to noise ratio, Signal detection, Imaging systems, Scattering, Stereoscopy, Image resolution, 3D image processing, Absorption, Signal attenuation
High resolution three dimensional (3D) optical imaging in the turbid underwater scenarios over extended length remains an outstanding challenge, primarily impeded by the absorption and scattering in turbid water, which result in substantial signal attenuation over short propagation distances. Overcoming water absorption by using optimum illumination wavelengths (480- 600 nm) of the visible spectrum, however, still requires one to address the strong scattering effects. To address the above challenge, we introduce a novel 3D imaging modality based on quantum parametric mode sorting (QPMS). It is a nascent quantum measurement technique that utilizes mode-selective quantum frequency conversion (QFC) in a χ2 nonlinear waveguide to up convert signal photons in a single spatiotemporal mode efficiently. Undesirable photons in other modes, even if they spectrally and temporally overlap with the signal, are converted with much less efficiency. This unique feature, combining with picosecond time gated detection as defined by the pump pulse width, can isolate signal photon backscattered by the target from multiscattered photons by the obstacle. It thus enables imaging through a strongly scattering medium, where the background photons are orders of magnitude stronger. With QPMS, we demonstrate 3D imaging of a target occluded by strongly scattering turbid media with optical depth < 9 (<18 round trip), while needing only 105 detected photons/pulse to achieve sub-millimeters resolution. This makes our single photon sensitive 3D imager suitable for imaging and remote sensing applications in photon-starved natural water environments where it's high sensitivity and excellent temporal resolution can be exploited to its full extent.
Conference Committee Involvement (4)
Quantum Computing, Communication, and Simulation V
25 January 2025 | San Francisco, California, United States
Quantum Computing, Communication, and Simulation IV
27 January 2024 | San Francisco, California, United States
Quantum Computing, Communication, and Simulation III
29 January 2023 | San Francisco, California, United States
Quantum Computing, Communication, and Simulation II
24 January 2022 | San Francisco, California, United States
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