activity
20142026
most citedA Cascaded Convolutional Neural Network for X-ray Low-dose CT Image Denoising

57 citations · 191 across the 22 of their papers we have counts for

collaborators

24 papers

eess.IV2026

Attention Consistent Longitudinal Medical Visual Question Answering Guided by Vision Foundation Models

Jialin Wu, Qianru Zhang, Georges El Fakhri +1

Longitudinal medical visual question answering (VQA) requires reasoning about anatomical differences between an image of a current time point and an image of a referred time point.…

eess.IV2026

An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation

Xiaofeng Liu, Qianru Zhang, Thibault Marin +4

The synergistic interpretation of anatomical information from computed tomography (CT) and metabolic information from positron emission tomography (PET) is important to oncologic i…

cs.CV2025

Power Battery Detection

Xiaoqi Zhao, Peiqian Cao, Chenyang Yu +10

Power batteries are essential components in electric vehicles, where internal structural defects can pose serious safety risks. We conduct a comprehensive study on a new task, powe…

cs.CV2024

Point-supervised Brain Tumor Segmentation with Box-prompted MedSAM

Xiaofeng Liu, Jonghye Woo, Chao Ma +2

Delineating lesions and anatomical structure is important for image-guided interventions. Point-supervised medical image segmentation (PSS) has great potential to alleviate costly…

cs.CV2024

Fair Text to Medical Image Diffusion Model with Subgroup Distribution Aligned Tuning

Xu Han, Fangfang Fan, Jingzhao Rong +4

The text to medical image (T2MedI) with latent diffusion model has great potential to alleviate the scarcity of medical imaging data and explore the underlying appearance distribut…

eess.IV2024★ 1 cited

LeqMod: Adaptable Lesion-Quantification-Consistent Modulation for Deep Learning Low-Count PET Image Denoising

Menghua Xia, Huidong Xie, Qiong Liu +9

Deep learning-based positron emission tomography (PET) image denoising offers the potential to reduce radiation exposure and scanning time by transforming low-count images into hig…

cs.SD2024

Speech motion anomaly detection via cross-modal translation of 4D motion fields from tagged MRI

Xiaofeng Liu, Fangxu Xing, Jiachen Zhuo +4

Understanding the relationship between tongue motion patterns during speech and their resulting speech acoustic outcomes -- i.e., articulatory-acoustic relation -- is of great impo…

cs.CV2024

Treatment-wise Glioblastoma Survival Inference with Multi-parametric Preoperative MRI

Xiaofeng Liu, Nadya Shusharina, Helen A Shih +3

In this work, we aim to predict the survival time (ST) of glioblastoma (GBM) patients undergoing different treatments based on preoperative magnetic resonance (MR) scans. The perso…

eess.IV2024

Disentangled Multimodal Brain MR Image Translation via Transformer-based Modality Infuser

Jihoon Cho, Xiaofeng Liu, Fangxu Xing +4

Multimodal Magnetic Resonance (MR) Imaging plays a crucial role in disease diagnosis due to its ability to provide complementary information by analyzing a relationship between mul…

physics.med-ph2023

Posterior Estimation for Dynamic PET imaging using Conditional Variational Inference

Xiaofeng Liu, Thibault Marin, Tiss Amal +3

This work aims efficiently estimating the posterior distribution of kinetic parameters for dynamic positron emission tomography (PET) imaging given a measurement of time of activit…

eess.IV2023★ 3 cited

TauPETGen: Text-Conditional Tau PET Image Synthesis Based on Latent Diffusion Models

Se-In Jang, Cristina Lois, Emma Thibault +7

In this work, we developed a novel text-guided image synthesis technique which could generate realistic tau PET images from textual descriptions and the subject's MR image. The gen…

cs.CV2023

Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI

Xiaofeng Liu, Helen A. Shih, Fangxu Xing +3

Deep learning (DL) models for segmenting various anatomical structures have achieved great success via a static DL model that is trained in a single source domain. Yet, the static…

eess.IV2023★ 6 cited

Posterior Estimation Using Deep Learning: A Simulation Study of Compartmental Modeling in Dynamic PET

Xiaofeng Liu, Thibault Marin, Tiss Amal +3

Background: In medical imaging, images are usually treated as deterministic, while their uncertainties are largely underexplored. Purpose: This work aims at using deep learning to…

cs.CV2022

Unsupervised Domain Adaptation for Segmentation with Black-box Source Model

Xiaofeng Liu, Chaehwa Yoo, Fangxu Xing +3

Unsupervised domain adaptation (UDA) has been widely used to transfer knowledge from a labeled source domain to an unlabeled target domain to counter the difficulty of labeling in…

physics.med-ph2022★ 5 cited

Feasibility study of clinical target volume definition for soft-tissue sarcoma using muscle fiber orientations derived from diffusion tensor imaging

Nadya Shusharina, Xiaofeng Liu, Jaume Coll-Font +5

Objective: Soft-tissue sarcoma spreads preferentially along muscle fibers. We explore the utility of deriving muscle fiber orientations from diffusion tensor MRI (DT-MRI) for defin…

physics.med-ph2021★ 14 cited

Free-breathing 3D Cardiac T1 Mapping with Transmit B1 Correction at 3T

Paul Kyu Han, Thibault Marin, Yanis Djebra +4

Purpose: To develop a cardiac T1 mapping method for free-breathing 3D T1 mapping of the whole heart at 3T with transmit B1 (B1+) correction Methods: A free-breathing, ECG-gated inv…

physics.med-ph2021★ 25 cited

Deep learning-based GTV contouring modeling inter- and intra- observer variability in sarcomas

Thibault Marin, Yue Zhuo, Rita Maria Lahoud +11

Background and purpose: The delineation of the gross tumor volume (GTV) is a critical step for radiation therapy treatment planning. The delineation procedure is typically performe…

physics.med-ph2020★ 13 cited

Motion correction for PET using subspace-based real-time MR imaging in simultaneous PET/MR

Thibault Marin, Yanis Djebra, Paul K. Han +6

Image quality of PET reconstructions is degraded by subject motion occurring during the acquisition. MR-based motion correction approaches have been studied for PET/MR scanners and…

physics.med-ph2020★ 14 cited

MR-Based PET Attenuation Correction using a Combined Ultrashort Echo Time/Multi-Echo Dixon Acquisition

Paul Kyu Han, Debra E. Horng, Kuang Gong +7

We propose a magnetic resonance (MR)-based method for estimation of continuous linear attenuation coefficients (LAC) in positron emission tomography (PET) using a physical compartm…

cs.CV2018★ 17 cited

A Sparse Non-negative Matrix Factorization Framework for Identifying Functional Units of Tongue Behavior from MRI

Jonghye Woo, Jerry L. Prince, Maureen Stone +8

Muscle coordination patterns of lingual behaviors are synergies generated by deforming local muscle groups in a variety of ways. Functional units are functional muscle groups of lo…

cs.CV2017★ 2 cited

End-to-end Lung Nodule Detection in Computed Tomography

Dufan Wu, Kyungsang Kim, Bin Dong +2

Computer aided diagnostic (CAD) system is crucial for modern med-ical imaging. But almost all CAD systems operate on reconstructed images, which were optimized for radiologists. Co…

cs.CV2017★ 34 cited

Iterative PET Image Reconstruction Using Convolutional Neural Network Representation

Kuang Gong, Jiahui Guan, Kyungsang Kim +4

PET image reconstruction is challenging due to the ill-poseness of the inverse problem and limited number of detected photons. Recently deep neural networks have been widely and su…

cs.CV2017★ 57 cited

A Cascaded Convolutional Neural Network for X-ray Low-dose CT Image Denoising

Dufan Wu, Kyungsang Kim, Georges El Fakhri +1

Image denoising techniques are essential to reducing noise levels and enhancing diagnosis reliability in low-dose computed tomography (CT). Machine learning based denoising methods…

stat.AP2014

A Bayesian spatial temporal mixtures approach to kinetic parametric images in dynamic Positron Emission Tomography

Wanchuang Zhu, Jinsong Ouyang, Yothin Rakvongthai +5

We present a fully Bayesian statistical approach to the problem of compartmental modelling in the context of Positron Emission Tomography. We cluster homogeneous region of interest…