arXiv subjects

Changhuei Yang

Publications and source records attributed to Changhuei Yang.

At least 19 recordsLinked to original sources

arXiv2025

Digital Modeling of Spatial Pathway Activity from Histology Reveals Tumor Microenvironment Heterogeneity

Spatial transcriptomics (ST) enables simultaneous mapping of tissue morphology and spatially resolved gene expression, offering unique opportunities to study tumor microenvironment heterogeneity. Here, we introduce a computational framework that predicts spatial pathway activity directly from hematoxylin-and-eosin-stained histology images at microscale resolution 55 and 100 um. Using image features derived from a computational pathology foundation model, we found that TGFb signaling was the most accurately predicted pathway across three independent breast and lung cancer ST datasets. In 87-88% of reliably predicted cases, the resulting spatial TGFb activity maps reflected the expected contrast between tumor and adjacent non-tumor regions, consistent with the known role of TGFb in regulating interactions within the tumor microenvironment. Notably, linear and nonlinear predictive models performed similarly, suggesting that image features may relate to pathway activity in a predominantly linear fashion or that nonlinear structure is small relative to measurement noise. These findings demonstrate that features extracted from routine histopathology may recover spatially coherent and biologically interpretable pathway patterns, offering a scalable strategy for integrating image-based inference with ST information in tumor microenvironment studies.

q-bio.QM↗

arXiv2025

Hybrid-illumination multiplexed Fourier ptychographic microscopy with robust aberration correction

Fourier ptychographic microscopy (FPM) is a powerful computational imaging modality that achieves high space-bandwidth product imaging for biomedical samples. However, its adoption is limited by slow data acquisition due to the need for sequential measurements. Multiplexed FPM strategies have been proposed to accelerate imaging by activating multiple LEDs simultaneously, but they typically require careful parameter tuning, and their lack of effective aberration correction makes them prone to image degradation. To address these limitations, we introduce hybrid-illumination multiplexed Fourier ptychographic microscopy (HMFPM), which integrates analytic aberration extraction capability with the efficiency of multiplexed illumination. Specifically, HMFPM employs a hybrid illumination strategy and a customized reconstruction algorithm with analytic and optimization methods. This hybrid strategy substantially reduces the number of required measurements while ensuring robust aberration correction and stable convergence. We demonstrate that HMFPM achieves 1.08 micrometers resolution, representing a 4-fold enhancement over the system's coherent diffraction limit, across a 1.77x1.77 millimeter square field of view using 20 measurements. HMFPM remains robust under diverse aberrations, providing up to 84 micrometers digital refocusing capability, and effectively corrects both field-dependent and scanning-induced aberrations in whole-slide pathology imaging. These results establish HMFPM as a practical, high-throughput, and aberration-free solution for biological and biomedical imaging.

physics.optics↗

arXiv2025

Digital defocus aberration interference for automated optical microscopy

Automation in optical microscopy is critical for enabling high-throughput imaging across a wide range of biomedical applications. Among the essential components of automated systems, robust autofocusing plays a pivotal role in maintaining image quality for both single-plane and volumetric imaging. However, conventional autofocusing methods often struggle with implementation complexity, limited generalizability across sample types, incompatibility with thick specimens, and slow feedback. We observed that the digitally summed Fourier spectrum of two images acquired from two-angle illumination exhibits interference-like fringe modulation when the sample is defocused. These digital fringes correlate directly with defocus through a physics-based relation. Based on this principle, we developed an automatic, efficient, and generalizable defocus detection method termed digital defocus aberration interference (DAbI). Implemented with a simple two-LED setup, DAbI can quantify the defocus distance over a range of 443 times the depth-of-field (DoF) for thin samples and 296 times for thick specimens. It can additionally extend the natural DoF of the imaging system by 20 folds when integrated with complex-field imaging. We demonstrated the versatile applications of DAbI on brightfield, complex-field, refractive index, confocal, and widefield fluorescence imaging, establishing it as a promising solution for automated, high-throughput optical microscopy.

physics.optics↗

arXiv2025

Optimized cerebral blood flow measurement in speckle contrast optical spectroscopy via refinement of noise calibration

Speckle contrast optical spectroscopy (SCOS) offers a non-invasive and cost-effective method for monitoring cerebral blood flow (CBF). However, extracting accurate CBF from SCOS necessitates precise noise pre-calibration. Errors from this can degrade CBF measurement fidelity, particularly when the overall signal level is low. Such errors primarily stem from residual speckle contrast associated with camera and shot noise, whose fluctuations exhibit a temporal structure that mimics cerebral blood volume (CBV) waveforms. We propose an optimization-based framework that performs an adaptive refinement of noise calibration, mitigating the CBV-mimicking artifacts by reducing the CBF-CBV waveform correlation. Validated on 10 human subjects, our approach effectively lowered the signal threshold for reliable CBF signal from 97 to 26 electrons per pixel for a 1920x1200 pixels SCOS system. This improvement enables more accurate and robust CBF measurements in SCOS, especially at large source-detector (SD) distances for deeper tissue interrogation.

eess.SP↗

arXiv2025

Analytic Fourier ptychotomography for volumetric refractive index imaging

Three-dimensional (3D) refractive index (RI) tomography offers label-free, quantitative volumetric imaging but faces limitations due to optical aberrations, limited resolution, and the computational complexity inherent to existing approaches. To overcome these barriers, we propose Analytic Fourier Ptychotomography (AFP), a new computational microscopy technique that analytically reconstructs aberration-free, complex-valued 3D RI distributions without iterative optimization or axial scanning. AFP incorporates a new concept called the finite sample thickness (FST) prior, and analytically solves the inverse scattering problem through three sequential steps: complex-field reconstruction via the Kramers-Kronig relation, linear aberration correction using overlapping spectra, and analytic spectrum extension into the darkfield region. Unlike iterative reconstruction methods, AFP does not require parameter tuning or computationally intensive optimizations, which are often error-prone and non-generalizable. We experimentally demonstrate that AFP significantly enhances image quality and resolution under various aberration conditions across a range of applications. AFP corrected aberrations associated with 25 Zernike modes (with a maximal phase difference of 2.3$\pi$ and maximal Zernike coefficient value of 4), extended the synthetic numerical aperture from 0.41 to 0.99, and provided a two-fold resolution enhancement in all directions. AFP's simplicity and robustness make it an attractive imaging technology for quantitative 3D analysis in biological, microbial ecological, and medical studies.

physics.optics↗

arXiv2025

Assessing Sensitivity of Brain-to-Scalp Blood Flows in Laser Speckle Imaging by Occluding the Superficial Temporal Artery

Cerebral blood flow is a critical metric for cerebrovascular monitoring, with applications in stroke detection, brain injury evaluation, aging, and neurological disorders. Non-invasively measuring cerebral blood dynamics is challenging due to the scalp and skull, which obstruct direct brain access and contain their own blood dynamics that must be isolated. We developed an aggregated seven-channel speckle contrast optical spectroscopy system to measure blood flow and blood volume non-invasively. Each channel, with distinct source-to-detector distance, targeted different depths to detect scalp and brain blood dynamics separately. By briefly occluding the superficial temporal artery, which supplies blood only to the scalp, we isolated surface blood dynamics from brain signals. Results on 20 subjects show that scalp-sensitive channels experienced significant reductions in blood dynamics during occlusion, while brain-sensitive channels experienced minimal changes. This provides experimental evidence of brain-to-scalp sensitivity in optical measurements, highlighting optimal configuration for preferentially probing brain signals non-invasively.

physics.optics↗

arXiv2025

Five-dimensional single-shot fluorescence imaging using a polarized Fourier light-field microscope

Single-shot fluorescence imaging techniques have gained increasing interest in recent years due to their ability to rapidly capture complex biological data without the need for extensive scanning. In this letter, we introduce polarized Fourier light field microscopy (pFLFM), a novel fluorescence imaging technique that captures five-dimensional information (3D intensity and 2D polarization) in a single snapshot. This technique combines a polarization camera with an FLFM setup, significantly improving data acquisition efficiency. We experimentally validated the pFLFM system using a fluorescent Siemens star, demonstrating consistent resolution and an extended depth of field across various polarizations. Using the 5D imaging capabilities of pFLFM, we imaged plant roots and uncovered unique heterogeneities in cellulose fibril configurations across various root sections. These results not only highlight the potential of pFLFM in biological and environmental sciences, but also represent a significant advancement in the design of single-shot fluorescence imaging systems.

physics.optics↗

arXiv2025

Single-shot volumetric fluorescence imaging with neural fields

Single-shot volumetric fluorescence (SVF) imaging offers a significant advantage over traditional imaging methods that require scanning across multiple axial planes as it can capture biological processes with high temporal resolution. The key challenges in SVF imaging include requiring sparsity constraints, eliminating depth ambiguity in the reconstruction, and maintaining high resolution across a large field of view. In this paper, we introduce the QuadraPol point spread function (PSF) combined with neural fields, a novel approach for SVF imaging. This method utilizes a custom polarizer at the back focal plane and a polarization camera to detect fluorescence, effectively encoding the 3D scene within a compact PSF without depth ambiguity. Additionally, we propose a reconstruction algorithm based on the neural fields technique that provides improved reconstruction quality compared to classical deconvolution methods. QuadraPol PSF, combined with neural fields, significantly reduces the acquisition time of a conventional fluorescence microscope by approximately 20 times and captures a 100 mm$^3$ cubic volume in one shot. We validate the effectiveness of both our hardware and algorithm through all-in-focus imaging of bacterial colonies on sand surfaces and visualization of plant root morphology. Our approach offers a powerful tool for advancing biological research and ecological studies.

physics.optics↗

arXiv2024

Impact of Stain Variation and Color Normalization for Prognostic Predictions in Pathology

In recent years, deep neural networks (DNNs) have demonstrated remarkable performance in pathology applications, potentially even outperforming expert pathologists due to their ability to learn subtle features from large datasets. One complication in preparing digital pathology datasets for DNN tasks is variation in tinctorial qualities. A common way to address this is to perform stain normalization on the images. In this study, we show that a well-trained DNN model trained on one batch of histological slides failed to generalize to another batch prepared at a different time from the same tissue blocks, even when stain normalization methods were applied. This study used sample data from a previously reported DNN that was able to identify patients with early stage non-small cell lung cancer (NSCLC) whose tumors did and did not metastasize, with high accuracy, based on training and then testing of digital images from H&E stained primary tumor tissue sections processed at the same time. In this study we obtained a new series of histologic slides from the adjacent recuts of same tissue blocks processed in the same lab but at a different time. We found that the DNN trained on the either batch of slides/images was unable to generalize and failed to predict progression in the other batch of slides/images (AUC_cross-batch = 0.52 - 0.53 compared to AUC_same-batch = 0.74 - 0.81). The failure to generalize did not improve even when the tinctorial difference correction were made through either traditional color-tuning or stain normalization with the help of a Cycle Generative Adversarial Network (CycleGAN) process. This highlights the need to develop an entirely new way to process and collect consistent microscopy images from histologic slides that can be used to both train and allow for the general application of predictive DNN algorithms.

eess.IV↗

arXiv2024

Efficient, gigapixel-scale, aberration-free whole slide scanner using angular ptychographic imaging with closed-form solution

Whole slide imaging provides a wide field-of-view (FOV) across cross-sections of biopsy or surgery samples, significantly facilitating pathological analysis and clinical diagnosis. Such high-quality images that enable detailed visualization of cellular and tissue structures are essential for effective patient care and treatment planning. To obtain such high-quality images for pathology applications, there is a need for scanners with high spatial bandwidth products, free from aberrations, and without the requirement for z-scanning. Here we report a whole slide imaging system based on angular ptychographic imaging with a closed-form solution (WSI-APIC), which offers efficient, tens-of-gigapixels, large-FOV, aberration-free imaging. WSI-APIC utilizes oblique incoherent illumination for initial high-level segmentation, thereby bypassing unnecessary scanning of the background regions and enhancing image acquisition efficiency. A GPU-accelerated APIC algorithm analytically reconstructs phase images with effective digital aberration corrections and improved optical resolutions. Moreover, an auto-stitching technique based on scale-invariant feature transform ensures the seamless concatenation of whole slide phase images. In our experiment, WSI-APIC achieved an optical resolution of 772 nm using a 10x/0.25 NA objective lens and captures 80-gigapixel aberration-free phase images for a standard 76.2 mm x 25.4 mm microscopic slide.

physics.optics↗

arXiv2024

Correlating Stroke Risk with Non-Invasive Tracing of Brain Blood Dynamic via a Portable Speckle Contrast Optical Spectroscopy Laser Device

Stroke poses a significant global health threat, with millions affected annually, leading to substantial morbidity and mortality. Current stroke risk assessment for the general population relies on markers such as demographics, blood tests, and comorbidities. A minimally invasive, clinically scalable, and cost-effective way to directly measure cerebral blood flow presents an opportunity. This opportunity has potential to positively impact effective stroke risk assessment prevention and intervention. Physiological changes in the cerebral vascular system, particularly in response to carbon dioxide level changes and oxygen deprivation, such as during breath-holding, can offer insights into stroke risk assessment. However, existing methods for measuring cerebral perfusion reserve, such as blood flow and blood volume changes, are limited by either invasiveness or impracticality. Here, we propose a transcranial approach using speckle contrast optical spectroscopy (SCOS) to non-invasively monitor regional changes in brain blood flow and volume during breath-holding. Our study, conducted on 50 individuals classified into two groups (low-risk and higher-risk for stroke), shows significant differences in blood dynamic changes during breath-holding between the two groups, providing physiological insights for stroke risk assessment using a non-invasive quantification paradigm. Given its cost-effectiveness, scalability, portability, and simplicity, this laser-centric tool has significant potential in enhancing the pre-screening of stroke and mitigating strokes in the general population through early diagnosis and intervention.

eess.IV↗

arXiv2024

Ptycho-endoscopy on a lensless ultrathin fiber bundle tip

Synthetic aperture radar (SAR) utilizes an aircraft-carried antenna to emit electromagnetic pulses and detect the returning echoes. As the aircraft travels across a designated area, it synthesizes a large virtual aperture to improve image resolution. Inspired by SAR, we introduce synthetic aperture ptycho-endoscopy (SAPE) for micro-endoscopic imaging beyond the diffraction limit. SAPE operates by hand-holding a lensless fiber bundle tip to record coherent diffraction patterns from specimens. The fiber cores at the distal tip modulate the diffracted wavefield within a confined area, emulating the role of the 'airborne antenna' in SAR. The handheld operation introduces positional shifts to the tip, analogous to the aircraft's movement. These shifts facilitate the acquisition of a ptychogram and synthesize a large virtual aperture extending beyond the bundle's physical limit. We mitigate the influences of hand motion and fiber bending through a low-rank spatiotemporal decomposition of the bundle's modulation profile. Our tests demonstrate the ability to resolve a 548-nm linewidth on a resolution target. The achieved space-bandwidth product is ~1.1 million effective pixels, representing a 36-fold increase compared to that of the original fiber bundle. Furthermore, SAPE's refocusing capability enables imaging over an extended depth of field exceeding 2 cm. The aperture synthesizing process in SAPE surpasses the diffraction limit set by the probe's maximum collection angle, opening new opportunities for both fiber-based and distal-chip endoscopy in applications such as medical diagnostics and industrial inspection.

physics.optics↗

arXiv2024

Length-scale study in deep learning prediction for non-small cell lung cancer brain metastasis

Deep learning assisted digital pathology has the potential to impact clinical practice in significant ways. In recent studies, deep neural network (DNN) enabled analysis outperforms human pathologists. Increasing sizes and complexity of the DNN architecture generally improves performance at the cost of DNN's explainability. For pathology, this lack of DNN explainability is particularly problematic as it hinders the broader clinical interpretation of the pathology features that may provide physiological disease insights. To better assess the features that DNN uses in developing predictive algorithms to interpret digital microscopic images, we sought to understand the role of resolution and tissue scale and here describe a novel method for studying the predictive feature length-scale that underpins a DNN's predictive power. We applied the method to study a DNN's predictive capability in the case example of brain metastasis prediction from early-stage non-small-cell lung cancer biopsy slides. The study highlights the DNN attention in the brain metastasis prediction targeting both cellular scale (resolution) and tissue scale features on H&E-stained histological whole slide images. At the cellular scale, we see that DNN's predictive power is progressively increased at higher resolution (i.e., lower resolvable feature length) and is largely lost when the resolvable feature length is longer than 5 microns. In addition, DNN uses more macro-scale features (maximal feature length) associated with tissue organization/architecture and is optimized when assessing visual fields larger than 41 microns. This study for the first time demonstrates the length-scale requirements necessary for optimal DNN learning on digital whole slide images.

eess.IV↗

arXiv2024

A compact and cost-effective laser-powered speckle visibility spectroscopy (SVS) device for measuring cerebral blood flow

In the realm of cerebrovascular monitoring, primary metrics typically include blood pressure, which influences cerebral blood flow (CBF) and is contingent upon vessel radius. Measuring CBF non-invasively poses a persistent challenge, primarily attributed to the difficulty of accessing and obtaining signal from the brain. This study aims to introduce a compact speckle visibility spectroscopy (SVS) device designed for non-invasive CBF measurements, offering cost-effectiveness and scalability while tracking CBF with remarkable sensitivity and temporal resolution. The wearable hardware has a modular design approach consisting solely of a laser diode as the source and a meticulously selected board camera as the detector. They both can be easily placed on the head of a subject to measure CBF with no additional optical elements. The SVS device can achieve a sampling rate of 80 Hz with minimal susceptibility to external disturbances. The device also achieves better SNR compared with traditional fiber-based SVS devices, capturing about 70 times more signal and showing superior stability and reproducibility. It is designed to be paired and distributed in multiple configurations around the head, and measure signals that exceed the quality of prior optical CBF measurement techniques. Given its cost-effectiveness, scalability, and simplicity, this laser-centric tool offers significant potential in advancing non-invasive cerebral monitoring technologies.

physics.med-ph↗

arXiv2023

FPM-INR: Fourier ptychographic microscopy image stack reconstruction using implicit neural representations

Image stacks provide invaluable 3D information in various biological and pathological imaging applications. Fourier ptychographic microscopy (FPM) enables reconstructing high-resolution, wide field-of-view image stacks without z-stack scanning, thus significantly accelerating image acquisition. However, existing FPM methods take tens of minutes to reconstruct and gigabytes of memory to store a high-resolution volumetric scene, impeding fast gigapixel-scale remote digital pathology. While deep learning approaches have been explored to address this challenge, existing methods poorly generalize to novel datasets and can produce unreliable hallucinations. This work presents FPM-INR, a compact and efficient framework that integrates physics-based optical models with implicit neural representations (INR) to represent and reconstruct FPM image stacks. FPM-INR is agnostic to system design or sample types and does not require external training data. In our demonstrated experiments, FPM-INR substantially outperforms traditional FPM algorithms with up to a 25-fold increase in speed and an 80-fold reduction in memory usage for continuous image stack representations.

physics.optics↗

arXiv2023

High-resolution, large field-of-view label-free imaging via aberration-corrected, closed-form complex field reconstruction

Computational imaging methods empower modern microscopy with the ability of producing high-resolution, large field-of-view, aberration-free images. One of the dominant computational label-free imaging methods, Fourier ptychographic microscopy (FPM), effectively increases the spatial-bandwidth product of conventional microscopy by using multiple tilted illuminations to achieve high-throughput imaging. However, its iterative reconstruction method is prone to parameter selection, can be computationally expensive and tends to fail under excessive aberrations. Recently, spatial Kramers-Kronig methods show it is possible to analytically reconstruct complex field but lacks the ability of correcting aberrations or providing extended resolution enhancement. Here, we present a closed-form method, termed APIC, which weds the strengths of both methods. A new analytical phase retrieval framework is established in APIC, which demonstrates, for the first time, the feasibility of analytically reconstructing the complex field associated with darkfield measurements. In addition, APIC can analytically retrieve complex aberrations of an imaging system with no additional hardware. By avoiding iterative algorithms, APIC requires no human designed convergence metric and always obtains a closed-form complex field solution. The faithfulness and correctness of APIC's reconstruction are guaranteed due to its analytical nature. We experimentally demonstrate that APIC gives correct reconstruction result while FPM fails to do so when constrained to the same number of measurements. Meanwhile, APIC achieves 2.8 times faster computation using image tile size of 256 (length-wise). We also demonstrate APIC is unprecedentedly robust against aberrations compared to FPM - APIC is capable of addressing aberration whose maximal phase difference exceeds 3.8$π$ when using a NA 0.25 objective in experiment.

physics.optics↗

arXiv2023

Interferometric speckle visibility spectroscopy (iSVS) for measuring decorrelation time and dynamics of moving samples with enhanced signal-to-noise ratio and relaxed reference requirements

Diffusing wave spectroscopy (DWS) is a group of techniques used to measure the dynamics of a scattering medium in a non-invasive manner. DWS methods rely on detecting the speckle light field from the moving scattering media and measuring the speckle decorrelation time to quantify the scattering mediums dynamics. For DWS, the signal-to-noise (SNR) is determined by the ratio between measured decorrelation time to the standard error of the measurement. This SNR is often low in certain applications because of high noise variances and low signal intensity, especially in biological applications with restricted exposure and emission levels. To address this photon-limited signal-to-noise ratio problem, we investigated, theoretically and experimentally, the SNR of an interferometric speckle visibility spectroscopy (iSVS) compared to more traditional DWS methods. We found that iSVS can provide excellent SNR performance through its ability to overcome camera noise. We also proved iSVS system has more relaxed constraints on the reference beam properties than most other interferometric systems. For an iSVS to function properly, we simply require the reference beam to exhibit local temporal stability, while incident angle, reference phase, and intensity uniformity do not need to be constrained. This flexibility can potentially enable more unconventional iSVS implementation schemes.

eess.IV↗

arXiv2021

Roadmap on Wavefront Shaping and deep imaging in complex media

The last decade has seen the development of a wide set of tools, such as wavefront shaping, computational or fundamental methods, that allow to understand and control light propagation in a complex medium, such as biological tissues or multimode fibers. A vibrant and diverse community is now working on this field, that has revolutionized the prospect of diffraction-limited imaging at depth in tissues. This roadmap highlights several key aspects of this fast developing field, and some of the challenges and opportunities ahead.

physics.optics↗