arXiv subjects

Ran Hong

Publications and source records attributed to Ran Hong.

14 recordsLinked to original sources

arXiv2026

GR-Diffusion: 3D Gaussian Representation Meets Diffusion in Whole-Body PET Reconstruction

Positron emission tomography (PET) reconstruction is a critical challenge in molecular imaging, often hampered by noise amplification, structural blurring, and detail loss due to sparse sampling and the ill-posed nature of inverse problems. The three-dimensional discrete Gaussian representation (GR), which efficiently encodes 3D scenes using parameterized discrete Gaussian distributions, has shown promise in computer vision. In this work, we pro-pose a novel GR-Diffusion framework that synergistically integrates the geometric priors of GR with the generative power of diffusion models for 3D low-dose whole-body PET reconstruction. GR-Diffusion employs GR to generate a reference 3D PET image from projection data, establishing a physically grounded and structurally explicit benchmark that overcomes the low-pass limitations of conventional point-based or voxel-based methods. This reference image serves as a dual guide during the diffusion process, ensuring both global consistency and local accuracy. Specifically, we employ a hierarchical guidance mechanism based on the GR reference. Fine-grained guidance leverages differences to refine local details, while coarse-grained guidance uses multi-scale difference maps to correct deviations. This strategy allows the diffusion model to sequentially integrate the strong geometric prior from GR and recover sub-voxel information. Experimental results on the UDPET and Clinical datasets with varying dose levels show that GR-Diffusion outperforms state-of-the-art methods in enhancing 3D whole-body PET image quality and preserving physiological details.

cs.CV↗

arXiv2025

Meta-information Guided Cross-domain Synergistic Diffusion Model for Low-dose PET Reconstruction

Low-dose PET imaging is crucial for reducing patient radiation exposure but faces challenges like noise interference, reduced contrast, and difficulty in preserving physiological details. Existing methods often neglect both projection-domain physics knowledge and patient-specific meta-information, which are critical for functional-semantic correlation mining. In this study, we introduce a meta-information guided cross-domain synergistic diffusion model (MiG-DM) that integrates comprehensive cross-modal priors to generate high-quality PET images. Specifically, a meta-information encoding module transforms clinical parameters into semantic prompts by considering patient characteristics, dose-related information, and semi-quantitative parameters, enabling cross-modal alignment between textual meta-information and image reconstruction. Additionally, the cross-domain architecture combines projection-domain and image-domain processing. In the projection domain, a specialized sinogram adapter captures global physical structures through convolution operations equivalent to global image-domain filtering. Experiments on the UDPET public dataset and clinical datasets with varying dose levels demonstrate that MiG-DM outperforms state-of-the-art methods in enhancing PET image quality and preserving physiological details.

cs.CV↗

arXiv2025

LSVOS 2025 Challenge Report: Recent Advances in Complex Video Object Segmentation

This report presents an overview of the 7th Large-scale Video Object Segmentation (LSVOS) Challenge held in conjunction with ICCV 2025. Besides the two traditional tracks of LSVOS that jointly target robustness in realistic video scenarios: Classic VOS (VOS), and Referring VOS (RVOS), the 2025 edition features a newly introduced track, Complex VOS (MOSEv2). Building upon prior insights, MOSEv2 substantially increases difficulty, introducing more challenging but realistic scenarios including denser small objects, frequent disappear/reappear events, severe occlusions, adverse weather and lighting, etc., pushing long-term consistency and generalization beyond curated benchmarks. The challenge retains standard ${J}$, $F$, and ${J\&F}$ metrics for VOS and RVOS, while MOSEv2 adopts ${J\&\dot{F}}$ as the primary ranking metric to better evaluate objects across scales and disappearance cases. We summarize datasets and protocols, highlight top-performing solutions, and distill emerging trends, such as the growing role of LLM/MLLM components and memory-aware propagation, aiming to chart future directions for resilient, language-aware video segmentation in the wild.

cs.CV↗

arXiv2025

Enhancing Sa2VA for Referent Video Object Segmentation: 2nd Solution for 7th LSVOS RVOS Track

Referential Video Object Segmentation (RVOS) aims to segment all objects in a video that match a given natural language description, bridging the gap between vision and language understanding. Recent work, such as Sa2VA, combines Large Language Models (LLMs) with SAM~2, leveraging the strong video reasoning capability of LLMs to guide video segmentation. In this work, we present a training-free framework that substantially improves Sa2VA's performance on the RVOS task. Our method introduces two key components: (1) a Video-Language Checker that explicitly verifies whether the subject and action described in the query actually appear in the video, thereby reducing false positives; and (2) a Key-Frame Sampler that adaptively selects informative frames to better capture both early object appearances and long-range temporal context. Without any additional training, our approach achieves a J&F score of 64.14% on the MeViS test set, ranking 2nd place in the RVOS track of the 7th LSVOS Challenge at ICCV 2025.

cs.CV↗

arXiv2025

Pseudo-Label Enhanced Cascaded Framework: 2nd Technical Report for LSVOS 2025 VOS Track

Complex Video Object Segmentation (VOS) presents significant challenges in accurately segmenting objects across frames, especially in the presence of small and similar targets, frequent occlusions, rapid motion, and complex interactions. In this report, we present our solution for the LSVOS 2025 VOS Track based on the SAM2 framework. We adopt a pseudo-labeling strategy during training: a trained SAM2 checkpoint is deployed within the SAM2Long framework to generate pseudo labels for the MOSE test set, which are then combined with existing data for further training. For inference, the SAM2Long framework is employed to obtain our primary segmentation results, while an open-source SeC model runs in parallel to produce complementary predictions. A cascaded decision mechanism dynamically integrates outputs from both models, exploiting the temporal stability of SAM2Long and the concept-level robustness of SeC. Benefiting from pseudo-label training and cascaded multi-model inference, our approach achieves a J\&F score of 0.8616 on the MOSE test set -- +1.4 points over our SAM2Long baseline -- securing the 2nd place in the LSVOS 2025 VOS Track, and demonstrating strong robustness and accuracy in long, complex video segmentation scenarios.

cs.CV↗

arXiv2025

Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction

Ultra-low-dose positron emission tomography (PET) reconstruction holds significant potential for reducing patient radiation exposure and shortening examination times. However, it may also lead to increased noise and reduced imaging detail, which could decrease the image quality. In this study, we present a Double-Constraint Diffusion Model (DCDM), which freezes the weights of a pre-trained diffusion model and injects a trainable double-constraint controller into the encoding architecture, greatly reducing the number of trainable parameters for ultra-low-dose PET reconstruction. Unlike full fine-tuning models, DCDM can adapt to different dose levels without retraining all model parameters, thereby improving reconstruction flexibility. Specifically, the two constraint modules, named the Nuclear Transformer Constraint (NTC) and the Encoding Nexus Constraint (ENC), serve to refine the pre-trained diffusion model. The NTC leverages the nuclear norm as an approximation for matrix rank minimization, integrates the low-rank property into the Transformer architecture, and enables efficient information extraction from low-dose images and conversion into compressed feature representations in the latent space. Subsequently, the ENC utilizes these compressed feature representations to encode and control the pre-trained diffusion model, ultimately obtaining reconstructed PET images in the pixel space. In clinical reconstruction, the compressed feature representations from NTC help select the most suitable ENC for efficient unknown low-dose PET reconstruction. Experiments conducted on the UDPET public dataset and the Clinical dataset demonstrated that DCDM outperforms state-of-the-art methods on known dose reduction factors (DRF) and generalizes well to unknown DRF scenarios, proving valuable even at ultra-low dose levels, such as 1% of the full dose.

cs.CV↗

arXiv2024

st-DTPM: Spatial-Temporal Guided Diffusion Transformer Probabilistic Model for Delayed Scan PET Image Prediction

PET imaging is widely employed for observing biological metabolic activities within the human body. However, numerous benign conditions can cause increased uptake of radiopharmaceuticals, confounding differentiation from malignant tumors. Several studies have indicated that dual-time PET imaging holds promise in distinguishing between malignant and benign tumor processes. Nevertheless, the hour-long distribution period of radiopharmaceuticals post-injection complicates the determination of optimal timing for the second scan, presenting challenges in both practical applications and research. Notably, we have identified that delay time PET imaging can be framed as an image-to-image conversion problem. Motivated by this insight, we propose a novel spatial-temporal guided diffusion transformer probabilistic model (st-DTPM) to solve dual-time PET imaging prediction problem. Specifically, this architecture leverages the U-net framework that integrates patch-wise features of CNN and pixel-wise relevance of Transformer to obtain local and global information. And then employs a conditional DDPM model for image synthesis. Furthermore, on spatial condition, we concatenate early scan PET images and noisy PET images on every denoising step to guide the spatial distribution of denoising sampling. On temporal condition, we convert diffusion time steps and delay time to a universal time vector, then embed it to each layer of model architecture to further improve the accuracy of predictions. Experimental results demonstrated the superiority of our method over alternative approaches in preserving image quality and structural information, thereby affirming its efficacy in predictive task.

eess.IV↗

arXiv2021

Systematic and Statistical Uncertainties of the Hilbert-Transform Based High-precision FID Frequency Extraction Method

Pulsed nuclear magnetic resonance (NMR) is widely used in high-precision magnetic field measurements. The absolute value of the magnetic field is determined from the precession frequency of nuclear magnetic moments. The Hilbert transform is widely used to extract the phase function from the observed free induction decay (FID) signal and then its frequency. In this paper, a detailed implementation of a Hilbert-transform based FID frequency extraction method is described. How artifacts and noise level in the FID signal affect the extracted phase function are derived analytically. A method of mitigating the artifacts in the extracted phase function of an FID is discussed. Correlations between noises of the phase function samples are studied for different noise spectra. We discovered that the error covariance matrix for the extracted phase function is nearly singular and improper for constructing the $\chi^2$ used in the fitting routine. A down-sampling method for fixing the singular covariance matrix has been developed, so that the minimum $\chi^2$-fit yields properly the statistical uncertainty of the extracted frequency. Other practical methods of obtaining the statistical uncertainty are also discussed.

physics.ins-det↗

arXiv2019

Magnetic Field Measurement and Analysis for the Muon g-2 Experiment

The Muon g-2 Experiment (E989) at Fermilab measures the muon magnetic anomaly, aiming to resolve the greater than 3$\sigma$ discrepancy between the previous measurement and the Standard Model calculation with an improved precision of 140 part-per-billion (ppb). In E989, the muon beam is stored in a ring magnet. The spin precession frequency $\omega_{a}$ is measured by counting the decay positrons in 24 calorimeters, and the magnetic field is measured by nuclear magnet resonance (NMR) probes. An in-vacuum field scanning system consisting of NMR probes and read-out electronics has been implemented to measure the magnetic field applied to the muon beam. An additional 378 NMR probes, placed at fixed locations outside the vacuum chamber, monitor the field drift in between field scans. A high-accuracy probe was designed for calibrating the probes in the scanner. In this presentation, the magnetic field measurement hardware system and analysis methods will be described in detail. The progress of the Run-1 data analysis and improvements in Run-2 will be presented as well.

physics.ins-det↗

arXiv2018

Experiences from the Commissioning and First Physics Run of the Fermilab Muon g-2 Experiment

The Muon g-2 Experiment (E989) at Fermilab is seeking to measure the anomalous magnetic moment of muon ($a_{\mu}$) with a precision of 140 parts-per-billion (ppb) and aiming to resolve the discrepancy between the E821 measurement and the Standard Model calculation of $a_{\mu}$. In E989, the muon beam is stored in a ring magnet. The anomalous spin precession frequency $\omega_{a}$ is measured by counting decay positrons in 24 calorimeters, and the magnetic field is measured by nuclear magnetic resonance (NMR) probes. Improvements in this experiment with respect to its predecessor and the progress achieved in the commissioning run and the first physics run, Run-1, are presented.

physics.ins-det↗

arXiv2017

Charge-state distribution of Li ions from the $\beta$ decay of laser-trapped $^{6}$He atoms

Background: The accurate determination of atomic final states following nuclear $\beta$ decay plays an important role in many experiments. In particular, the charge state distributions of ions following nuclear $\beta$ decay are important for determinations of the $\beta-\nu$ angular correlation with improved precision. Purpose: Our measurement aims at providing benchmarks to test theoretical calculations. Method: The kinematics of Li$^{n+}$ ions produced following the $\beta$ decay of $^6{\rm He}$ within an electric field were measured using $^6{\rm He}$ atoms in the metastable $(1s2s,~{^3S_1})$ and in the $(1s2p,~{^3P_2})$ states confined by a magneto-optical trap. The electron shake-off probabilities were deduced including their dependence on ion energy. Results: We find significant discrepancies on the fractions of Li ions in the different charge states with respect to a recent calculation and discuss a plausible explanation. We also point out that there is no explanation for a large discrepancy between the same calculation and a previous measurement of the Li-ion energy dependence of the charge distribution from decays of the electronic ground state.

nucl-ex↗

arXiv2016

High accuracy position response calibration method for a micro-channel plate ion detector

We have developed a position response calibration method for a micro-channel plate (MCP) detector with a delay-line anode position readout scheme. Using an {\em in situ} calibration mask, an accuracy of 8~$\mu$m and a resolution of 85~$\mu$m (FWHM) have been achieved for MeV-scale $\alpha$ particles and ions with energies of $\sim$10~keV. At this level of accuracy, the difference between the MCP position responses to high-energy $\alpha$ particles and low-energy ions is significant. The improved performance of the MCP detector can find applications in many fields of AMO and nuclear physics. In our case, it helps reducing systematic uncertainties in a high-precision nuclear $\beta$-decay experiment.

physics.ins-det↗

arXiv2016

Helicity and nuclear $\beta$ decay correlations

We present simple derivations of nuclear $\beta$-decay correlations with an emphasis on the special role of helicity. This provides a good opportunity to teach students about helicity and chirality in particle physics through exercises using simple aspects of quantum mechanics. In addition, this paper serves as an introduction to nuclear $\beta$-decay correlations from both a theoretical and experimental vantage. This article can be used to introduce students to ongoing experiments searching for hints of new physics in the low-energy precision frontier.

physics.ed-ph↗

arXiv2013

Limits on tensor type weak currents from nuclear and neutron $β$~decays

The limits on time-reversal invariant tensor-type weak currents from nuclear and neutron $β$ decays are evaluated including most recent experimental data. We find that $ -0.14 \times 10^{-2} < (C_T + C^\prime_T)/C_A < 1.4 \times 10^{-2} $ and $ -0.16 < (C_T - C^\prime_T)/C_A < 0.16$ (90% C.L.), while for the case $C_T = C^\prime_T$ the limits are $ | C_T/C_A | < 3.7 \times 10^{-3} $. These limits are shown to be more stringent than those from recent measurements of the radiative pion decay. In addition, the sensitivity of future $10^{-3}$-level correlation measurements is investigated.

nucl-ex↗