arXiv subjects

Qifa Zhou

Publications and source records attributed to Qifa Zhou.

6 recordsLinked to original sources

arXiv2026

Label-free super-resolution color flow imaging using ultrasound phase microscopy

Ultrasound vascular imaging is limited by acoustic diffraction, restricting visualization of microvessels essential for understanding organ function and disease. Label-free super-resolution methods exploiting endogenous red blood cells have faced challenges in acquisition time and complexity. Here we introduce ultrasound phase microscopy (UPM), a label-free technique that achieves sub-wavelength resolution flow imaging by exploiting phase differences between consecutively beamformed frames with mismatched apodizations, without requiring localization or tracking. Validated in vivo across multiple species, organs, and ultrasound platforms, UPM attains spatial resolutions better than 5 um up to tenfold improvement over conventional color flow imaging while accelerating data acquisition by nearly two orders of magnitude compared to ultrasound localization microscopy. UPM enables rapid, high resolution vascular imaging and offers a practical approach for label-free super-resolution vascular imaging.

eess.SP↗

arXiv2025

Super-Resolution Posterior Ocular Microvascular Imaging Using 3-D Ultrasound Localization Microscopy With a 32X32 Matrix Array

The purpose of this study is to enable in-vivo three-dimensional (3-D) ultrasound localization microscopy (ULM) of posterior ocular microvasculature using a 256-channel system and a 1024-element matrix array, and to overcome limitations of restricted transmit angles, sound speed mismatch caused by the crystalline lens and surrounding tissues, and the low signal-to-noise ratio (SNR) of microbubble signals. To address phase distortions from the crystalline lens, which has a higher speed of sound (SOS) than surrounding tissues, a region-dependent SOS beamforming approach was implemented to improve microbubble resolution. A 4-D non-local means filter was subsequently applied to suppress background noise and enhance microbubble contrast. The proposed method improved localization accuracy and image quality, achieving a spatial resolution of 63 um, while Fourier shell correlation (1/2-bit threshold) confirmed a global resolution of approximately 59 um. Higher mean normalized cross-correlation coefficients between the microbubbles and the system point-spread function, obtained with the proposed method (approximately 0.67), compared with those without the proposed method (approximately 0.60), indicate enhanced microbubble signal quality. Furthermore, the 3-D bi-directional vessel density and flow-velocity maps were reconstructed, capturing detailed choroidal vascular and hemodynamic patterns. These results demonstrate that region-dependent SOS beamforming combined with spatiotemporal denoising enables high-resolution posterior ocular ULM and provides a practical pathway toward quantitative 3-D assessment of retinal and choroidal microvasculature for potential clinical use.

physics.med-ph↗

arXiv2024

Conditional score-based diffusion models for solving inverse problems in mechanics

We propose a framework to perform Bayesian inference using conditional score-based diffusion models to solve a class of inverse problems in mechanics involving the inference of a specimen's spatially varying material properties from noisy measurements of its mechanical response to loading. Conditional score-based diffusion models are generative models that learn to approximate the score function of a conditional distribution using samples from the joint distribution. More specifically, the score functions corresponding to multiple realizations of the measurement are approximated using a single neural network, the so-called score network, which is subsequently used to sample the posterior distribution using an appropriate Markov chain Monte Carlo scheme based on Langevin dynamics. Training the score network only requires simulating the forward model. Hence, the proposed approach can accommodate black-box forward models and complex measurement noise. Moreover, once the score network has been trained, it can be re-used to solve the inverse problem for different realizations of the measurements. We demonstrate the efficacy of the proposed approach on a suite of high-dimensional inverse problems in mechanics that involve inferring heterogeneous material properties from noisy measurements. Some examples we consider involve synthetic data, while others include data collected from actual elastography experiments. Further, our applications demonstrate that the proposed approach can handle different measurement modalities, complex patterns in the inferred quantities, non-Gaussian and non-additive noise models, and nonlinear black-box forward models. The results show that the proposed framework can solve large-scale physics-based inverse problems efficiently.

stat.ML↗

arXiv2023

Multimodal imaging of the mouse eye using visible light photoacoustic ophthalmoscopy and near-infrared-II optical coherence tomography

Non-invasive imaging plays a crucial role in diagnosing and studying eye diseases. However, existing photoacoustic ophthalmoscopy (PAOM) techniques in mice have limitations due to handling restrictions, suboptimal optical properties, limited availability of light sources and permissible light fluence at the retina. This study introduces an innovative approach that utilizes Rose Bengal, a contrast agent, to enhance PAOM contrast. This enables visualization of deeper structures like the choroidal microvasculature and sclera in the mouse eye using visible light. The integration of near-infrared-II optical coherence tomography (NIR-II OCT) provides additional tissue contrast and insights into potential NIR-II PAOM capabilities. To optimize imaging, we developed a cost-effective 3D printable mouse eye phantom and a fully 3D printable tip/tilt mouse platform. This solution elevates PAOM to a user-friendly technology, which can be used to address pressing research questions concerning several ocular diseases such as myopia, glaucoma and/or age-related macular degeneration in the future.

physics.med-ph↗

arXiv2021

Deep image prior for undersampling high-speed photoacoustic microscopy

Photoacoustic microscopy (PAM) is an emerging imaging method combining light and sound. However, limited by the laser's repetition rate, state-of-the-art high-speed PAM technology often sacrifices spatial sampling density (i.e., undersampling) for increased imaging speed over a large field-of-view. Deep learning (DL) methods have recently been used to improve sparsely sampled PAM images; however, these methods often require time-consuming pre-training and large training dataset with ground truth. Here, we propose the use of deep image prior (DIP) to improve the image quality of undersampled PAM images. Unlike other DL approaches, DIP requires neither pre-training nor fully-sampled ground truth, enabling its flexible and fast implementation on various imaging targets. Our results have demonstrated substantial improvement in PAM images with as few as 1.4$\%$ of the fully sampled pixels on high-speed PAM. Our approach outperforms interpolation, is competitive with pre-trained supervised DL method, and is readily translated to other high-speed, undersampling imaging modalities.

eess.IV↗

arXiv2018

MEMS approach to low-frequency broadband acoustic metamaterials

We experimentally demonstrate a simple micro-electro-mechnical systems (MEMS) approach to acoustic metamaterials and have observed the average increase of nearly 6 dB beyond the classic law for sound attenuation at low frequencies from 200 Hz to 1200 Hz. Here, we have also found that the MEMS metastructure, especially the cavity-backed micromembrane, contributes to 22.3% gain of sound transmission loss (STL) with a fill factor of perforation of less than 7.6%.

physics.app-ph↗