arXiv subjects

Mengqi Wu

Publications and source records attributed to Mengqi Wu.

12 recordsLinked to original sources

arXiv2026

MEMS Fiber-Tip Photoacoustic Spectrometer for In Situ Microscale Trace Gas Sensing

To meet the stringent requirements for miniaturized and highly sensitive trace gas sensing in space-constrained scenarios, including power equipment monitoring, minimally invasive biomedical diagnostics, and in situ lithium-battery analysis, we report a MEMS-integrated fiber-tip photoacoustic spectrometer (MFPAS). The device incorporates a Fabry-Perot (F-P) photoacoustic sensor formed by directly butt-coupling a single-mode fiber (SMF) to a 3 mm x 3 mm MEMS chip with a 100-nm-thick low-pressure chemical vapor deposition (LPCVD) Si$_3$N$_4$ diaphragm. The resulting approximately 200-$\mu$m deep silicon microcavity functions simultaneously as a photoacoustic gas cell and an acoustic confinement cavity. A micro-aperture fabricated at the diaphragm periphery by focused ion beam (FIB) milling serves as both a gas diffusion channel and an acoustic high-pass filter, suppressing ambient low-frequency pressure fluctuations and stabilizing the F-P quadrature point without active servo control. In gas-phase measurements, the sensor achieves a noise-equivalent concentration (NEC) of 58.5 ppb@1s, with a rapid response time of 6 s. Benefiting from its ultra-small cavity volume of approximately 1.5 nL, the device is further adapted through structural packaging for in situ dissolved gas analysis in transformer oil, where it achieves an NEC of 230 ppb@1s and a T90 response time of 320 s in the oil phase. By combining nanoliter-scale detection volume, ppb-level sensitivity, rapid response, and wafer-scale batch fabrication compatibility, the proposed MFPAS bridges MEMS diaphragm micromachining and FIB-enabled gas exchange engineering. This design overcomes the intrinsic gas-exchange limitation of conventional sealed-diaphragm optical microphones and offers significant potential for power equipment monitoring and in situ health diagnostics.

physics.optics↗

arXiv2026

Noise-Aware Boundary-Enhanced Generative Learning for Ultrasound Speckle Reduction

Ultrasound is a non-invasive, real-time, and cost-effective imaging technique widely used in clinical diagnosis. However, its diagnostic efficacy is often compromised by inherent speckle noise that degrades image quality and obscures underlying anatomical structures. Existing speckle reduction methods tend to over-smooth tissue boundaries and generalize poorly to heterogeneous noise levels. To address these limitations, we propose a Noise-Aware Boundary-Enhanced Generative Learning (NBGL) framework for ultrasound speckle reduction, which simultaneously preserves annotated anatomical boundaries and adapts to varying noise levels. The NBGL framework consists of a speckle reduction branch and a boundary enhancement branch. The former leverages generative learning to suppress speckle noise, while the latter learns boundary-sensitive representations to preserve target anatomical structures. Furthermore, a noise-aware interaction weight generation (NIWG) module estimates the speckle noise level via 3D Laplacian filtering and a median absolute deviation estimator, and translates it into an adaptive interaction weight. This weight is incorporated into a weighted feature-wise linear modulation (wFiLM) module to adaptively modulate cross-branch feature coupling, thereby improving robustness to varying noise levels. Extensive evaluations on 141 3D transvaginal ultrasound volumes demonstrate that NBGL consistently outperforms state-of-the-art methods in speckle reduction and structural preservation across six noise levels, while maintaining consistency with annotated anatomical boundaries.

cs.CV↗

arXiv2026

Delta-Diffusion: Modeling Longitudinal Brain Amyloid-PET Trajectories via Conditional Poisson Diffusion Bridge

While longitudinal brain PET imaging is the gold standard for quantifying the spatiotemporal accumulation of Beta-amyloid, its widespread clinical utility is constrained by high operational costs and cumulative radiation risks. Recent deep generative models show promise in longitudinal image synthesis; however, they often fail to capture subtle pathological progression due to identity drift and a persistent bias toward trivially replicating baseline signal intensities rather than modeling temporal transition. To this end, we propose Delta-Diffusion, a novel progression-aware framework that redefines longitudinal PET synthesis as a conditional Poisson Diffusion Bridge (PDB) process. Unlike standard diffusion models that start from Gaussian noise, our PDB formulation is mathematically anchored to the subject's baseline PET, effectively transforming the generative task into a conditional distribution transition of the amyloid trajectory. To handle heteroscedastic nature of PET imaging, we introduce a physically-grounded Poisson perturbation within a Diffusion Transformer (DiT). This architecture uses adaptive scale-shift modulation to precisely calibrate the synthesis with the elapsed clinical interval and structural MRI context. A volume-of-interest balanced objective is designed to emphasize sparse, high-risk regions of amyloid accumulation. Validated on two cohorts with 542 subjects, Delta-Diffusion demonstrates superior performance in capturing longitudinal variations in amyloid deposition compared to state-of-the-art methods, offering a robust computational framework for tracking disease progression.

eess.IV↗

arXiv2026

Unified Multi-Site Multi-Sequence Brain MRI Harmonization Enriched by Biomedical Semantic Style

Aggregating multi-site brain MRI data can enhance deep learning model training, but also introduces non-biological heterogeneity caused by site-specific variations (e.g., differences in scanner vendors, acquisition parameters, and imaging protocols) that can undermine generalizability. Recent retrospective MRI harmonization seeks to reduce such site effects by standardizing image style (e.g., intensity, contrast, noise patterns) while preserving anatomical content. However, existing methods often rely on limited paired traveling-subject data or fail to effectively disentangle style from anatomy. Furthermore, most current approaches address only single-sequence harmonization, restricting their use in real-world settings where multi-sequence MRI is routinely acquired. To this end, we introduce MMH, a unified framework for multi-site multi-sequence brain MRI harmonization that leverages biomedical semantic priors for sequence-aware style alignment. MMH operates in two stages: (1) a diffusion-based global harmonizer that maps MR images to a sequence-specific unified domain using style-agnostic gradient conditioning, and (2) a target-specific fine-tuner that adapts globally aligned images to desired target domains. A tri-planar attention BiomedCLIP encoder aggregates multi-view embeddings to characterize volumetric style information, allowing explicit disentanglement of image styles from anatomy without requiring paired data. Evaluations on 4,163 T1- and T2-weighted MRIs demonstrate MMH's superior harmonization over state-of-the-art methods in image feature clustering, voxel-level comparison, tissue segmentation, and downstream age and site classification.

cs.CV↗

arXiv2024

Unpaired Volumetric Harmonization of Brain MRI with Conditional Latent Diffusion

Multi-site structural MRI is increasingly used in neuroimaging studies to diversify subject cohorts. However, combining MR images acquired from various sites/centers may introduce site-related non-biological variations. Retrospective image harmonization helps address this issue, but current methods usually perform harmonization on pre-extracted hand-crafted radiomic features, limiting downstream applicability. Several image-level approaches focus on 2D slices, disregarding inherent volumetric information, leading to suboptimal outcomes. To this end, we propose a novel 3D MRI Harmonization framework through Conditional Latent Diffusion (HCLD) by explicitly considering image style and brain anatomy. It comprises a generalizable 3D autoencoder that encodes and decodes MRIs through a 4D latent space, and a conditional latent diffusion model that learns the latent distribution and generates harmonized MRIs with anatomical information from source MRIs while conditioned on target image style. This enables efficient volume-level MRI harmonization through latent style translation, without requiring paired images from target and source domains during training. The HCLD is trained and evaluated on 4,158 T1-weighted brain MRIs from three datasets in three tasks, assessing its ability to remove site-related variations while retaining essential biological features. Qualitative and quantitative experiments suggest the effectiveness of HCLD over several state-of-the-arts

eess.IV↗

arXiv2024

Phase engineering of giant second harmonic generation in Bi$_2$O$_2$Se

Two-dimensional (2D) materials with remarkable second-harmonic generation (SHG) hold promise for future on-chip nonlinear optics. Relevant materials with both giant SHG response and environmental stability are long-sought targets. Here, we demonstrate the enormous SHG from the phase engineering of a high-performance semiconductor, Bi$_2$O$_2$Se (BOS), under uniaxial strain. SHG signals captured in strained 20 nm-BOS films exceed those of NbOI$_2$ and NbOCl$_2$ of similar thickness by a factor of 10, and are four orders of magnitude higher than monolayer-MoS$_2$, resulting in a significant second-order nonlinear susceptibility on the order of 1 nm V$^{-1}$. Intriguingly, the strain enables continuous adjustment of the ferroelectric phase transition across room temperature. Consequently, an exceptionally large tunability of SHG, approximately six orders of magnitude, is achieved through strain or thermal modulation. This colossal SHG, originating from the geometric phase of Bloch wave functions and coupled with sensitive tunability through multiple approaches in this air-stable 2D semiconductor, opens new possibilities for designing chip-scale, switchable nonlinear optical devices.

cond-mat.mtrl-sci↗

arXiv2024

Disentangled Latent Energy-Based Style Translation: An Image-Level Structural MRI Harmonization Framework

Brain magnetic resonance imaging (MRI) has been extensively employed across clinical and research fields, but often exhibits sensitivity to site effects arising from non-biological variations such as differences in field strength and scanner vendors. Numerous retrospective MRI harmonization techniques have demonstrated encouraging outcomes in reducing the site effects at the image level. However, existing methods generally suffer from high computational requirements and limited generalizability, restricting their applicability to unseen MRIs. In this paper, we design a novel disentangled latent energy-based style translation (DLEST) framework for unpaired image-level MRI harmonization, consisting of (a) site-invariant image generation (SIG), (b) site-specific style translation (SST), and (c) site-specific MRI synthesis (SMS). Specifically, the SIG employs a latent autoencoder to encode MRIs into a low-dimensional latent space and reconstruct MRIs from latent codes. The SST utilizes an energy-based model to comprehend the global latent distribution of a target domain and translate source latent codes toward the target domain, while SMS enables MRI synthesis with a target-specific style. By disentangling image generation and style translation in latent space, the DLEST can achieve efficient style translation. Our model was trained on T1-weighted MRIs from a public dataset (with 3,984 subjects across 58 acquisition sites/settings) and validated on an independent dataset (with 9 traveling subjects scanned in 11 sites/settings) in four tasks: histogram and feature visualization, site classification, brain tissue segmentation, and site-specific structural MRI synthesis. Qualitative and quantitative results demonstrate the superiority of our method over several state-of-the-arts.

eess.IV↗

arXiv2024

Functional Imaging Constrained Diffusion for Brain PET Synthesis from Structural MRI

Magnetic resonance imaging (MRI) and positron emission tomography (PET) are increasingly used in multimodal analysis of neurodegenerative disorders. While MRI is broadly utilized in clinical settings, PET is less accessible. Many studies have attempted to use deep generative models to synthesize PET from MRI scans. However, they often suffer from unstable training and inadequately preserve brain functional information conveyed by PET. To this end, we propose a functional imaging constrained diffusion (FICD) framework for 3D brain PET image synthesis with paired structural MRI as input condition, through a new constrained diffusion model (CDM). The FICD introduces noise to PET and then progressively removes it with CDM, ensuring high output fidelity throughout a stable training phase. The CDM learns to predict denoised PET with a functional imaging constraint introduced to ensure voxel-wise alignment between each denoised PET and its ground truth. Quantitative and qualitative analyses conducted on 293 subjects with paired T1-weighted MRI and 18F-fluorodeoxyglucose (FDG)-PET scans suggest that FICD achieves superior performance in generating FDG-PET data compared to state-of-the-art methods. We further validate the effectiveness of the proposed FICD on data from a total of 1,262 subjects through three downstream tasks, with experimental results suggesting its utility and generalizability.

eess.IV↗

arXiv2024

Iterative Learning for Joint Image Denoising and Motion Artifact Correction of 3D Brain MRI

Image noise and motion artifacts greatly affect the quality of brain MRI and negatively influence downstream medical image analysis. Previous studies often focus on 2D methods that process each volumetric MR image slice-by-slice, thus losing important 3D anatomical information. Additionally, these studies generally treat image denoising and artifact correction as two standalone tasks, without considering their potential relationship, especially on low-quality images where severe noise and motion artifacts occur simultaneously. To address these issues, we propose a Joint image Denoising and motion Artifact Correction (JDAC) framework via iterative learning to handle noisy MRIs with motion artifacts, consisting of an adaptive denoising model and an anti-artifact model. In the adaptive denoising model, we first design a novel noise level estimation strategy, and then adaptively reduce the noise through a U-Net backbone with feature normalization conditioning on the estimated noise variance. The anti-artifact model employs another U-Net for eliminating motion artifacts, incorporating a novel gradient-based loss function designed to maintain the integrity of brain anatomy during the motion correction process. These two models are iteratively employed for joint image denoising and artifact correction through an iterative learning framework. An early stopping strategy depending on noise level estimation is applied to accelerate the iteration process. The denoising model is trained with 9,544 T1-weighted MRIs with manually added Gaussian noise as supervision. The anti-artifact model is trained on 552 T1-weighted MRIs with motion artifacts and paired motion-free images. Experimental results on a public dataset and a clinical study suggest the effectiveness of JDAC in both tasks of denoising and motion artifact correction, compared with several state-of-the-art methods.

eess.IV↗

arXiv2023

Human Pose-based Estimation, Tracking and Action Recognition with Deep Learning: A Survey

Human pose analysis has garnered significant attention within both the research community and practical applications, owing to its expanding array of uses, including gaming, video surveillance, sports performance analysis, and human-computer interactions, among others. The advent of deep learning has significantly improved the accuracy of pose capture, making pose-based applications increasingly practical. This paper presents a comprehensive survey of pose-based applications utilizing deep learning, encompassing pose estimation, pose tracking, and action recognition.Pose estimation involves the determination of human joint positions from images or image sequences. Pose tracking is an emerging research direction aimed at generating consistent human pose trajectories over time. Action recognition, on the other hand, targets the identification of action types using pose estimation or tracking data. These three tasks are intricately interconnected, with the latter often reliant on the former. In this survey, we comprehensively review related works, spanning from single-person pose estimation to multi-person pose estimation, from 2D pose estimation to 3D pose estimation, from single image to video, from mining temporal context gradually to pose tracking, and lastly from tracking to pose-based action recognition. As a survey centered on the application of deep learning to pose analysis, we explicitly discuss both the strengths and limitations of existing techniques. Notably, we emphasize methodologies for integrating these three tasks into a unified framework within video sequences. Additionally, we explore the challenges involved and outline potential directions for future research.

cs.CV↗

arXiv2023

Reaction kinetics of CN + toluene and its implication on the productions of aromatic nitriles in the Taurus molecular cloud and Titan's atmosphere

Reactions between cyano radical and aromatic hydrocarbons are believed to be important pathways for the formation of aromatic nitriles in the interstellar medium (ISM) including those identified in the Taurus molecular cloud (TMC-1). Aromatic nitriles might participate in the formation of polycyclic aromatic nitrogen containing hydrocarbons (PANHs) in Titan's atmosphere. Here, ab initio kinetics simulations reveal a high efficiency of $\rm \sim10^{-10}~cm^{3}~s^{-1}$ and the competition of the different products of 30-1800 K and $10^{-7}$-100 atm of the CN + toluene reaction. In the star-forming region of TMC-1 environment, the product yields of benzonitrile and tolunitriles for CN reacting with toluene may be approximately 17$\%$ and 83$\%$, respectively. The detection of main products, tolunitriles, can serve as proxies for the undetected toluene in the ISM due to their much larger dipole moments. The competition between bimolecular and unimolecular products is extremely intense under the warmer and denser PANH forming region of Titan's stratosphere. The computational results show that the fractions of tolunitriles, adducts, and benzonitrile are 19$\%$-68$\%$, 15$\%$-64$\%$ and 17$\%$, respectively, at 150-200 K and 0.0001-0.001 atm (Titan's stratosphere). Then, benzonitrile and tolunitriles may contribute to the formation of PANHs by consecutive $\rm C_{2}H$ additions. Kinetic information of aromatic nitriles for the CN + toluene reaction calculated here helps to explain the formation mechanism of polycyclic aromatic hydrocarbons (PAHs) or PANHs under different interstellar environments and constrains corresponding astrochemical models.

astro-ph.GA↗

arXiv2023

Achieving ferroelectricity in a centrosymmetric high-performance semiconductor by strain engineering

Phase engineering by strains in 2D semiconductors is of great importance for a variety of applications. Here, we present a study of strain induced ferroelectric (FE) transition on bismuth oxyselenide (Bi$_2$O$_2$Se) films, a high-performance (HP) semiconductor for next-generation electronics. Bi$_2$O$_2$Se is non-FE at ambient. Upon a loading force $\gtrsim 400$ nN, piezoelectric force responses exhibit butterfly loops on magnitude and 180$^\textrm{o}$ phase switching. By carefully ruling out extrinsic factors, these features are attributed to a transition to FE phase. The transition is further proved by the appearance of a sharp peak on optical second harmonic generation under an uniaxial strain. Fundamentally, solids with paraelectric at ambient and FE under strains are scarce. FE transition is discussed with the help of first-principle calculations and theoretical simulations. The switching of FE polarization acts as a knob for Schottky barrier engineering at contacts and serves as basis for a memristor with a huge switching ratio of 10$^6$. Our work endows a new degree of freedom to a HP electronic/optoelectronic semiconductor and the integration of FE and HP semiconductivity paving the way for multiple exciting functionalities, including HP neuromorphic computation and bulk piezophotovoltaic.

cond-mat.mtrl-sci↗