arXiv subjects

Lida Zhu

Publications and source records attributed to Lida Zhu.

9 recordsLinked to original sources

arXiv2025

Experimental and numerical investigation of wavelength and resolution dependency of dynamic optical coherence tomography signals

The wavelength and system-resolution dependencies of dynamic optical coherence tomography (DOCT) are investigated experimentally and numerically. Experimental investigations demonstrate significant wavelength dependency for the DOCT values but no resolution dependency. Numerical simulations were performed using diffusion, random-ballistic motion, and mono-directional flow-based motion models. Diffusion and random-ballistic motion-based simulations show significant wavelength dependency. Additionally, small but certain resolution dependency was revealed by these simulations. Mono-directional flow simulations did not show wavelength dependency, but did demonstrate resolution dependency. The DOCT value is sensitive to both tissue dynamics and the OCT system specification. These effects should be considered when interpreting DOCT images.

physics.optics↗

arXiv2025

Image formation theory of optical coherence tomography with optical aberrations and its application for computational aberration correction

Computational corrections of defocus and aberrations in optical coherence tomography (OCT) offers a promising approach to realize high-resolution imaging with deep imaging depth, but without additional high hardware costs. However, these techniques are not well understood owing to a lack of accurate theoretical models and investigation tools. The image formation theory for OCT with optical aberrations is thus formulated here. Based on this theory, a numerical simulation method is developed, and computational refocusing and computational aberration correction (CAC) methods are designed. The CAC method based on the image formation theory is applied to simulated OCT signals and OCT images of a microparticle phantom and an in vivo human retina for simultaneous multi-depth correction of systematic aberration. The numerical simulation under the effective numerical aperture of 0.2 and 1.05 um central wavelength shows that the proposed method can obtain the Strehl ratios of more than 0.8 over a $\pm$ 100 um defocus range, while the conventional method cannot achieve this under the simulated conditions. Imaging results show that the CAC method designed based on the image formation theory can correct optical aberrations and improve the image quality more than the conventional CAC method. The proposed method improved the frequency component corresponding to the density of cone photoreceptors in OCT photoreceptor images by 1.2 to 1.4 times under the multi-depth correction. This theoretical model-based approach provides a powerful aid for understanding OCT imaging properties and processing method design.

physics.optics↗

arXiv2024

Multiple scattering suppression for in vivo optical coherence tomography measurement using B-scan-wise multi-focus averaging method

We demonstrate a method that reduces the noise caused by multi-scattering (MS) photons in an \invivo optical coherence tomography image. This method combines a specially designed image acquisition (i.e., optical coherence tomography scan) scheme and subsequent complex signal processing. For the acquisition, multiple cross-sectional images (frames) are sequentially acquired while the depth position of the focus is altered for each frame by an electrically tunable lens. In the signal processing, the frames are numerically defocus-corrected, and complex averaged. Because of the inconsistency in the MS-photon trajectories among the different electrically tunable lens-induced defocus, this averaging reduces the MS signal. This method was validated using a scattering phantom and in vivo unanesthetized small fish samples, and was found to reduce MS noise even for unanesthetized in vivo measurement.

physics.optics↗

arXiv2024

Optical-coherence-tomography-based deep-learning scatterer-density estimator using physically accurate noise model

We demonstrate a deep-learning-based scatterer density estimator (SDE) that processes local speckle patterns of optical coherence tomography (OCT) images and estimates the scatterer density behind each speckle pattern. The SDE is trained using large quantities of numerically simulated OCT images and their associated scatterer densities. The numerical simulation uses a noise model that incorporates the spatial properties of three types of noise, i.e., shot noise, relative-intensity noise, and non-optical noise. The SDE's performance was evaluated numerically and experimentally using two types of scattering phantom and in vitro tumor spheroids. The results confirmed that the SDE estimates scatterer densities accurately. The estimation accuracy improved significantly when compared with our previous deep-learning-based SDE, which was trained using numerical speckle patterns generated from a noise model that did not account for the spatial properties of noise.

physics.bio-ph↗

arXiv2024

Improving PTM Site Prediction by Coupling of Multi-Granularity Structure and Multi-Scale Sequence Representation

Protein post-translational modification (PTM) site prediction is a fundamental task in bioinformatics. Several computational methods have been developed to predict PTM sites. However, existing methods ignore the structure information and merely utilize protein sequences. Furthermore, designing a more fine-grained structure representation learning method is urgently needed as PTM is a biological event that occurs at the atom granularity. In this paper, we propose a PTM site prediction method by Coupling of Multi-Granularity structure and Multi-Scale sequence representation, PTM-CMGMS for brevity. Specifically, multigranularity structure-aware representation learning is designed to learn neighborhood structure representations at the amino acid, atom, and whole protein granularity from AlphaFold predicted structures, followed by utilizing contrastive learning to optimize the structure representations.Additionally, multi-scale sequence representation learning is used to extract context sequence information, and motif generated by aligning all context sequences of PTM sites assists the prediction. Extensive experiments on three datasets show that PTM-CMGMS outperforms the state-of-the-art methods.

q-bio.QM↗

arXiv2023

Polarization-artifact reduction and accuracy improvement of Jones-matrix polarization-sensitive optical coherence tomography by multi-focus averaging

Polarization-sensitive optical coherence tomography (PS-OCT) is a promising biomedical imaging tool for differentiation of various tissue properties. However, the presence of multiple-scattering (MS) signals can degrade the quantitative polarization measurement accuracy. We demonstrate a method to reduce MS signals and increase the measurement accuracy of Jones matrix PS-OCT. This method suppresses MS signals by averaging of multiple Jones matrix volumes measured using different focal positions. The MS signals are decorrelated among the volumes by focus position modulation and are thus reduced by averaging. However, the single scattering signals are kept consistent among the focus-modulated volumes by computational refocusing. We validated the proposed method using a scattering phantom and a postmortem medaka fish. The results showed reduced artifacts in birefringence and degree-of-polarization uniformity measurements, particularly in deeper regions in the samples. This method offers a practical solution to mitigate MS-induced artifacts in PS-OCT imaging and improves quantitative polarization measurement accuracy.

physics.optics↗

arXiv2023

ADMEOOD: Out-of-Distribution Benchmark for Drug Property Prediction

Obtaining accurate and valid information for drug molecules is a crucial and challenging task. However, chemical knowledge and information have been accumulated over the past 100 years from various regions, laboratories, and experimental purposes. Little has been explored in terms of the out-of-distribution (OOD) problem with noise and inconsistency, which may lead to weak robustness and unsatisfied performance. This study proposes a novel benchmark ADMEOOD, a systematic OOD dataset curator and benchmark specifically designed for drug property prediction. ADMEOOD obtained 27 ADME (Absorption, Distribution, Metabolism, Excretion) drug properties from Chembl and relevant literature. Additionally, it includes two kinds of OOD data shifts: Noise Shift and Concept Conflict Drift (CCD). Noise Shift responds to the noise level by categorizing the environment into different confidence levels. On the other hand, CCD describes the data which has inconsistent label among the original data. Finally, it tested on a variety of domain generalization models, and the experimental results demonstrate the effectiveness of the proposed partition method in ADMEOOD: ADMEOOD demonstrates a significant difference performance between in-distribution and out-of-distribution data. Moreover, ERM (Empirical Risk Minimization) and other models exhibit distinct trends in performance across different domains and measurement types.

cs.LG↗

arXiv2023

Multi-focus averaging for multiple scattering suppression in optical coherence tomography

Multiple scattering is one of the main factors that limits the penetration depth of optical coherence tomography (OCT) in scattering samples. We propose a method termed multi-focus averaging (MFA) to suppress the multiple-scattering signals and improve the image contrast of OCT in deep regions. The MFA method captures multiple OCT volumes with various focal positions and averages them in complex form after correcting the varying defocus through computational refocusing. Because the multiple-scattering takes different trajectories among the different focal position configurations, this averaging suppresses the multiple-scattering signal. Meanwhile, the single-scattering takes a consistent trajectory regardless of the focal position configuration and is not suppressed. Hence, the MFA method improves the signal ratio between the single- and multiple-scattering signals and improves the image contrast. A scattering phantom and a postmortem zebrafish were measured for validation of the proposed method. The results showed that the contrast of intensity images of both the phantom and zebrafish were improved using the MFA method, such that they were better than the contrast provided by the standard complex averaging method. The MFA method provides a cost-effective solution for contrast enhancement through multiple-scattering reduction in tissue imaging using OCT systems.

physics.optics↗

arXiv2022

Label-free metabolic imaging of non-alcoholic-fatty-liver-disease (NAFLD) liver by volumetric dynamic optical coherence tomography

Label-free metabolic imaging of non-alcoholic fatty liver disease (NAFLD) mouse liver is demonstrated ex vivo by dynamic optical coherence tomography (OCT). The NAFLD mouse is a methionine choline-deficient (MCD)-diet model, and two mice fed MCD diet for 1 and 2 weeks are involved in addition to a normal-diet mouse. The dynamic OCT is based on repeating raster scan and logarithmic intensity variance (LIV) analysis which enables volumetric metabolic imaging with a standard-speed (50,000 A-lines/s) OCT system. Metabolic domains associated with lipid droplet accumulation and inflammation are clearly visualized three-dimensionally. Particularly, the normal-diet liver exhibits highly metabolic vessel-like structures of peri-vascular hepatic zones. The 1-week MCD-diet liver shows ring-shaped highly metabolic structures formed with lipid droplets. The 2-week MCD-diet liver exhibits fragmented vessel-like structures associated with inflammation. These results imply that volumetric LIV imaging is useful for visualizing and assessing NAFLD abnormalities.

physics.med-ph↗