arXiv subjects

Ge Ren

Publications and source records attributed to Ge Ren.

11 recordsLinked to original sources

arXiv2026

A unified classification-quantification framework for bubble-like nuclei within the extended quantum molecular dynamics model

A systematic study of relaxed low-energy cluster configurations for all nuclides listed in the AME2020 database is performed within the extended quantum molecular dynamics (EQMD) framework, with frictional cooling enabling stable relaxation. A unified classification-quantification framework based on the dimensionless parameters $BHTU$ is established to characterize bubble-like nuclear morphologies. The factor $B$, determined from the number of inflection points in the radial density profile, categorizes nuclei into droplet ($B=0$), bubble ($B=1$), and toroidal bubble ($B=2$). The parameter $H$ defines the degree of central density depletion, while $T$ and $U$ characterize the relative surface thickness and the relative size of the internal low-density region, respectively. Light nuclei are predominantly droplet-like with $B=0$, $H=0$, $T=1$, $U=0$. Most medium-mass nuclei have $B=1$, consistent with previous studies, especially in the vicinity of $^{40}$Ca and the neutron-rich region, where nuclei show a pronounced central hollowing with large $H$ and $U$ values, identifying them as prime candidates for experimental searches for bubble structures. Toroidal bubble nuclei ($B=2$), emerging for $Z\approx25$ and prevalent in heavy systems, display a local density minimum at intermediate radius together with a shell-like low-density region. Furthermore, bubble structures are found to be widespread in the superheavy region, in agreement with earlier studies. This parameter scheme not only reveals the morphological richness of nuclei but also establishes a predictive framework for exploring exotic nuclear shapes, thereby opening new avenues for future theoretical and experimental investigations.

nucl-th↗

arXiv2026

ScribbleDose: Scribble-Guided Dose Prediction in Radiotherapy

Anatomical structure masks are widely adopted in radiotherapy dose prediction, as they provide explicit geometric constraints that facilitate structure-dose coupling. However, conventional manual delineation of these masks requires precise annotation of structure boundaries relevant to radiotherapy, which is time-consuming and labor-intensive. To address these limitations, we propose a scribble-guided dose prediction framework that relies solely on anatomical structures annotated with sparse scribbles. Specifically, we design a Scribble Completion Module (SCM) to generate dense anatomical masks by propagating sparse scribble labels to semantically similar voxels. During the propagation process, a supervoxel-based regularization is introduced to preserve geometric boundary consistency to ensure anatomical plausibility. Furthermore, we propose a Structure-Guided Dose Generation Module (SGDGM) to strengthen the correspondence between sparse structural cues and dose distribution. Herein, the completed dense masks derived from scribbles serve as structural guidance to condition the dose prediction network. This scribble-mask-dose consistency encourages high-dose concentration within target volumes while effectively sparing surrounding organs-at-risk. Extensive experiments on the open-source GDP-HMM dataset demonstrate that the proposed method maintains superior dose prediction performance while substantially reducing annotation cost, providing a practical paradigm for dose prediction under sparse structural annotation. The code and reannotated scribbles are made publicly available at https://github.com/iCherishxixixi/ScribbleDose.

cs.CV↗

arXiv2026

Any-to-All MRI Synthesis: A Unified Foundation Model for Nasopharyngeal Carcinoma and Its Downstream Applications

Magnetic resonance imaging (MRI) is essential for nasopharyngeal carcinoma (NPC) radiotherapy (RT), but practical constraints, such as patient discomfort, long scan times, and high costs often lead to incomplete modalities in clinical practice, compromising RT planning accuracy. Traditional MRI synthesis methods are modality-specific, limited in anatomical adaptability, and lack clinical interpretability-failing to meet NPC's RT needs. Here, we developed a unified foundation model integrating contrastive visual representation learning and vision-language alignment (VLA) to enable any-to-all MRI synthesis. The model uses a contrastive encoder for modality-invariant representations and a CLIP-based text-informed decoder for semantically consistent synthesis, supporting any-to-all MRI synthesis via one unified foundation model. Trained on 40,825 images from 13 institutions, it achieves consistently high performance (average SSIM 0.90, PSNR 27) across 26 internal/external validation sites (15,748 images), with superior synthesis fidelity and robustness to noise and domain shifts. Meanwhile, its unified representation enhances downstream RT-relevant tasks (e.g., segmentation). This work advances digital medicine solutions for NPC care by leveraging foundation models to bridge technical synthesis and clinical utility.

cs.CV↗

arXiv2025

Atmospheric Turbulence-Resilient Long-Range Fourier Ptychography

While Fourier ptychography (FP) offers super-resolution for macroscopic imaging, its real-world application is severely hampered by atmospheric turbulence, a challenge largely unaddressed in existing macroscopic FP research operating under idealized conditions. This work establishes, to our knowledge, the first comprehensive computational framework specifically designed for turbulence mitigation in long-range FP, termed Turbulence-Mitigated FP (TMFP). Rather than correcting pupil errors, an image degradation model is developed alongside a reconstruction pipeline inspired by speckle interferometry. By taking multiple short-exposure randomly-distorted measurements and exploiting their statistical properties, the diffraction-limited sub-aperture images can be recovered for further FP reconstruction. Numerical simulations and experimental validations under optical turbulence demonstrate the method's robustness, resolution enhancement, and practicality in adverse conditions, paving the way for the reliable deployment of high-resolution macroscopic FP in real-world scenarios.

physics.optics↗

arXiv2024

Bubble $^{36}$Ar and Its New Breathing Modes

The bubble nuclei are important components of exotic nuclear structures characterized by special depletions of central densities. Focusing on bubble structures of $^{36}$Ar, the characterizations of bubble nuclei were explored with the framework of the extended quantum molecular dynamics model. Three density distribution modes were uncovered for the first time, i.e. micro-bubble, bubble, and cluster resonances, which show unique spectral signature compared to the monopole resonance spectrum as the excitation intensity was increased in bubbles. Of pivotal importance is the revelation that the bubble mode's oscillation frequency closely resembles macroscopic bubble dynamics, building a connection between classical macroscopic phenomena and the quantum complexity of the nuclear structure. The discovery marks a crucial step forward in deciphering the relationship between classical and quantum domains within the enigmatic world of atomic nuclei.

nucl-th↗

arXiv2023

Feature-oriented Deep Learning Framework for Pulmonary Cone-beam CT (CBCT) Enhancement with Multi-task Customized Perceptual Loss

Cone-beam computed tomography (CBCT) is routinely collected during image-guided radiation therapy (IGRT) to provide updated patient anatomy information for cancer treatments. However, CBCT images often suffer from streaking artifacts and noise caused by under-rate sampling projections and low-dose exposure, resulting in low clarity and information loss. While recent deep learning-based CBCT enhancement methods have shown promising results in suppressing artifacts, they have limited performance on preserving anatomical details since conventional pixel-to-pixel loss functions are incapable of describing detailed anatomy. To address this issue, we propose a novel feature-oriented deep learning framework that translates low-quality CBCT images into high-quality CT-like imaging via a multi-task customized feature-to-feature perceptual loss function. The framework comprises two main components: a multi-task learning feature-selection network(MTFS-Net) for customizing the perceptual loss function; and a CBCT-to-CT translation network guided by feature-to-feature perceptual loss, which uses advanced generative models such as U-Net, GAN and CycleGAN. Our experiments showed that the proposed framework can generate synthesized CT (sCT) images for the lung that achieved a high similarity to CT images, with an average SSIM index of 0.9869 and an average PSNR index of 39.9621. The sCT images also achieved visually pleasing performance with effective artifacts suppression, noise reduction, and distinctive anatomical details preservation. Our experiment results indicate that the proposed framework outperforms the state-of-the-art models for pulmonary CBCT enhancement. This framework holds great promise for generating high-quality anatomical imaging from CBCT that is suitable for various clinical applications.

cs.CV↗

arXiv2022

Piston sensing for sparse aperture systems via all-optical diffractive neural network

It is a crucial issue to realize real-time piston correction in the area of sparse aperture imaging. This paper introduces an optical diffractive neural network-based piston sensing method, which can achieve light-speed sensing. By using detectable intensity to represent pistons, the proposed method is capable of converting complex amplitude distribution of the imaging optical field into piston values directly. Differing from the electrical neural network, the way of intensity representation enables the method to obtain the predicted pistons without imaging acquisition and electrical processing process. The simulations demonstrate the feasibility of the method for point source, and high accuracies are achieved for both monochromatic light and broadband light. This method can greatly improve the real-time performance of piston sensing and contribute to the development of the sparse aperture system.

physics.optics↗

arXiv2022

Extracting lung function-correlated information from CT-encoded static textures

The inherent characteristics of lung tissues, which are independent of breathing manoeuvre, may provide fundamental information on lung function. This paper attempted to study function-correlated lung textures and their spatial distribution from CT. 21 lung cancer patients with thoracic 4DCT scans, DTPA-SPECT ventilation images (V), and available pulmonary function test (PFT) measurements were collected. 79 radiomic features were included for analysis, and a sparse-to-fine strategy including subregional feature discovery and voxel-wise feature distribution study was carried out to identify the function-correlated radiomic features. At the subregion level, lung CT images were partitioned and labeled as defected/non-defected patches according to reference V. At the voxel-wise level, feature maps (FMs) of selected feature candidates were generated for each 4DCT phase. Quantitative metrics, including Spearman coefficient of correlation (SCC) and Dice similarity coefficient (DSC) for FM-V spatial agreement assessments, intra-class coefficient of correlation (ICC) for FM robustness evaluations, and FM-PFT comparisons, were applied to validate the results. At the subregion level, eight function-correlated features were filtered out with medium-to-large statistical strength (effect size>0.330) to differentiate defected/non-defected lung regions. At the voxel-wise level, FMs of candidates yielded moderate-to-strong voxel-wise correlations with reference V. Among them, FMs of GLDM Dependence Non-uniformity showed the highest robust (ICC=0.96) spatial correlation, with median SCCs ranging from 0.54 to 0.59 throughout ten phases. Its phase-averaged FM achieved a median SCC of 0.60, the median DSC of 0.60/0.65 for high/low functional lung volumes, respectively, and the correlation of 0.646 between the spatially averaged feature values and PFT measurements.

physics.med-ph↗

arXiv2021

Automatically Lock Your Neural Networks When You're Away

The smartphone and laptop can be unlocked by face or fingerprint recognition, while neural networks which confront numerous requests every day have little capability to distinguish between untrustworthy and credible users. It makes model risky to be traded as a commodity. Existed research either focuses on the intellectual property rights ownership of the commercialized model, or traces the source of the leak after pirated models appear. Nevertheless, active identifying users legitimacy before predicting output has not been considered yet. In this paper, we propose Model-Lock (M-LOCK) to realize an end-to-end neural network with local dynamic access control, which is similar to the automatic locking function of the smartphone to prevent malicious attackers from obtaining available performance actively when you are away. Three kinds of model training strategy are essential to achieve the tremendous performance divergence between certified and suspect input in one neural network. Extensive experiments based on MNIST, FashionMNIST, CIFAR10, CIFAR100, SVHN and GTSRB datasets demonstrated the feasibility and effectiveness of the proposed scheme.

cs.HC↗

arXiv2019

Translation position extracting in incoherent Fourier ptychography

Incoherent Fourier ptychography (IFP) is a newly developed super-resolution method, where accurate knowledge of translation positions is essential for image reconstruction.To release this limitation, we propose a preprocessing algorithm capable of extracting translation positions of the structure light directly from raw images of IFP, termed translation position extracting (TPE). TPE mainly involves two steps. First, the speckle parts mixed in the acquired intensities, in which the illumination motion is encoded, are isolated by intensity averaging and division. Then the cross-correlations of the speckle dataset are computed to determine the shift positions. TPE-IFP improves the previous IFP by removal of the requirement for prior knowledge of translation positions. Its effectiveness is demonstrated by obtaining high-quality super-resolution images in absence of location information in both simulations and experiments. By further relaxing the practical conditions, the proposed TPE may accelerate the applications of IFP. What is more, as a preprocessing approach, TPE might also contribute to the estimation of pattern positions for the similar speckle-based imaging.

eess.IV↗

arXiv2017

Satellite-Based Entanglement Distribution Over 1200 kilometers

Long-distance entanglement distribution is essential both for foundational tests of quantum physics and scalable quantum networks. Owing to channel loss, however, the previously achieved distance was limited to ~100 km. Here, we demonstrate satellite-based distribution of entangled photon pairs to two locations separated by 1203 km on the Earth, through satellite-to-ground two-downlink with a sum of length varies from 1600 km to 2400 km. We observe a survival of two-photon entanglement and a violation of Bell inequality by 2.37+/-0.09 under strict Einstein locality conditions. The obtained effective link efficiency at 1200 km in this work is over 12 orders of magnitude higher than the direct bidirectional transmission of the two photons through the best commercial telecommunication fibers with a loss of 0.16 dB/km.

quant-ph↗