20 citations · 56 across the 21 of their papers we have counts for
23 papers
Rewarding Reasoning, Not Answers: Fixing and Bounding Test-Time Reinforcement Learning on Medical QA
Kailong Fan, Anqi Pu, Yichen Wu +7
Test-time reinforcement learning adapts a model on its own unlabeled test set using majority-vote pseudo-labels and has shown strong results in mathematics. We show that this recip…
Manifold-Constrained PET Reconstruction with Learned Flow-Matching Priors
Hengjia Ran, Jie Luo, Yutao Zhu +3
Image reconstruction for positron emission tomography (PET) is an ill-posed Poisson inverse problem that often suffers from severe noise amplification and artifacts. In this work,…
MWF-MIMOSA for efficient simultaneous relaxometry and myelin water fraction mapping
Yuting Chen, Yohan Jun, Hyeong-Geol Shin +15
Quantitative magnetic resonance imaging (qMRI) provides improved sensitivity and specificity to tissue composition and pathological alterations compared with conventional contrast-…
MAPLE: Elevating Medical Reasoning from Statistical Consensus to Process-Led Alignment
Kailong Fan, Anqi Pu, Yichen Wu +7
Recent advances in medical large language models have explored Test-Time Reinforcement Learning (TTRL) to enhance reasoning. However, standard TTRL often relies on majority voting…
Omni-QALAS: Optimized Multiparametric Imaging for Simultaneous T1, T2 and Myelin Water Mapping
Shizhuo Li, Unay Dorken Gallastegi, Shohei Fujita +8
Purpose: To improve the accuracy of multiparametric estimation, including myelin water fraction (MWF) quantification, and reduce scan time in 3D-QALAS by optimizing sequence parame…
ALL-PET: A Low-resource and Low-shot PET Foundation Model in Projection Domain
Bin Huang, Kang Chen, Bingxuan Li +2
Building large-scale foundation model for PET imaging is hindered by limited access to labeled data and insufficient computational resources. To overcome data scarcity and efficien…
A Kernel Space-based Multidimensional Sparse Model for Dynamic PET Image Denoising
Kuang Xiaodong, Li Bingxuan, Li Yuan +6
Achieving high image quality for temporal frames in dynamic positron emission tomography (PET) is challenging due to the limited statistic especially for the short frames. Recent s…
MIMOSA: Multi-parametric Imaging using Multiple-echoes with Optimized Simultaneous Acquisition for highly-efficient quantitative MRI
Yuting Chen, Yohan Jun, Amir Heydari +8
Purpose: To develop a new sequence, MIMOSA, for highly-efficient T1, T2, T2*, proton density (PD), and source separation quantitative susceptibility mapping (QSM). Methods: MIMOSA…
Deep unrolled primal dual network for TOF-PET list-mode image reconstruction
Rui Hu, Chenxu Li, Kun Tian +3
Time-of-flight (TOF) information provides more accurate location data for annihilation photons, thereby enhancing the quality of PET reconstruction images and reducing noise. List-…
Unsupervised Learning of Hybrid Latent Dynamics: A Learn-to-Identify Framework
Yubo Ye, Sumeet Vadhavkar, Xiajun Jiang +3
Modern applications increasingly require unsupervised learning of latent dynamics from high-dimensional time-series. This presents a significant challenge of identifiability: many…
Hybrid Kinetics Embedding Framework for Dynamic PET Reconstruction
Yubo Ye, Huafeng Liu, Linwei Wang
In dynamic positron emission tomography (PET) reconstruction, the importance of leveraging the temporal dependence of the data has been well appreciated. Current deep-learning solu…
Glioblastoma Tumor Segmentation using an Ensemble of Vision Transformers
Huafeng Liu, Benjamin Dowdell, Todd Engelder +3
Glioblastoma is one of the most aggressive and deadliest types of brain cancer, with low survival rates compared to other types of cancer. Analysis of Magnetic Resonance Imaging (M…
DULDA: Dual-domain Unsupervised Learned Descent Algorithm for PET image reconstruction
Rui Hu, Yunmei Chen, Kyungsang Kim +3
Deep learning based PET image reconstruction methods have achieved promising results recently. However, most of these methods follow a supervised learning paradigm, which rely heav…
STPDnet: Spatial-temporal convolutional primal dual network for dynamic PET image reconstruction
Rui Hu, Jianan Cui, Chengjin Yu +2
Dynamic positron emission tomography (dPET) image reconstruction is extremely challenging due to the limited counts received in individual frame. In this paper, we propose a spatia…
LMPDNet: TOF-PET list-mode image reconstruction using model-based deep learning method
Chenxu Li, Rui Hu, Jianan Cui +1
The integration of Time-of-Flight (TOF) information in the reconstruction process of Positron Emission Tomography (PET) yields improved image properties. However, implementing the…
HessianFR: An Efficient Hessian-based Follow-the-Ridge Algorithm for Minimax Optimization
Yihang Gao, Huafeng Liu, Michael K. Ng +1
Wide applications of differentiable two-player sequential games (e.g., image generation by GANs) have raised much interest and attention of researchers to study efficient and fast…
TransEM:Residual Swin-Transformer based regularized PET image reconstruction
Rui Hu, Huafeng Liu
Positron emission tomography(PET) image reconstruction is an ill-posed inverse problem and suffers from high level of noise due to limited counts received. Recently deep neural net…
BUDA-SAGE with self-supervised denoising enables fast, distortion-free, high-resolution T2, T2*, para- and dia-magnetic susceptibility mapping
Zijing Zhang, Long Wang, Jaejin Cho +10
To rapidly obtain high resolution T2, T2* and quantitative susceptibility mapping (QSM) source separation maps with whole-brain coverage and high geometric fidelity. We propose Bli…
Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-Scale Generative Adversarial Network
Jianan Cui, Kuang Gong, Paul Han +2
Arterial spin labeling (ASL) magnetic resonance imaging (MRI) is a powerful imaging technology that can measure cerebral blood flow (CBF) quantitatively. However, since only a smal…
Clinically Translatable Direct Patlak Reconstruction from Dynamic PET with Motion Correction Using Convolutional Neural Network
Nuobei Xie, Kuang Gong, Ning Guo +5
Patlak model is widely used in 18F-FDG dynamic positron emission tomography (PET) imaging, where the estimated parametric images reveal important biochemical and physiology informa…
Penalized-likelihood PET Image Reconstruction Using 3D Structural Convolutional Sparse Coding
Nuobei Xie, Kuang Gong, Ning Guo +4
Positron emission tomography (PET) is widely used for clinical diagnosis. As PET suffers from low resolution and high noise, numerous efforts try to incorporate anatomical priors i…
Efficient T2 mapping with Blip-up/down EPI and gSlider-SMS (T2-BUDA-gSlider)
Xiaozhi Cao, Congyu Liao, Zijing Zhang +7
Purpose: To rapidly obtain high isotropic-resolution T2 maps with whole-brain coverage and high geometric fidelity. Methods: A T2 blip-up/down echo planar imaging (EPI) acquisition…
Recurrent Aggregation Learning for Multi-View Echocardiographic Sequences Segmentation
Ming Li, Weiwei Zhang, Guang Yang +5
Multi-view echocardiographic sequences segmentation is crucial for clinical diagnosis. However, this task is challenging due to limited labeled data, huge noise, and large gaps acr…