666 citations · 799 across the 19 of their papers we have counts for
21 papers
Diffeomorphic Transformer-based Abdomen MRI-CT Deformable Image Registration
Yang Lei, Luke A. Matkovic, Justin Roper +6
This paper aims to create a deep learning framework that can estimate the deformation vector field (DVF) for directly registering abdominal MRI-CT images. The proposed method assum…
Hippocampus Substructure Segmentation Using Morphological Vision Transformer Learning
Yang Lei, Yifu Ding, Richard L. J. Qiu +6
Background: The hippocampus plays a crucial role in memory and cognition. Because of the associated toxicity from whole brain radiotherapy, more advanced treatment planning techniq…
Denoising Magnetic Resonance Spectroscopy (MRS) Data Using Stacked Autoencoder for Improving Signal-to-Noise Ratio and Speed of MRS
Jing Wang, Bing Ji, Yang Lei +3
Background: Magnetic resonance spectroscopy (MRS) enables non-invasive detection and measurement of biochemicals and metabolites. However, MRS has low signal-to-noise ratio (SNR) w…
Deep Learning-based Protoacoustic Signal Denoising for Proton Range Verification
Jing Wang, James J. Sohn, Yang Lei +5
Objective: Proton therapy offers an advantageous dose distribution compared to the photon therapy, since it deposits most of the energy at the end of range, namely the Bragg peak (…
Deep learning-based Fast Volumetric Image Generation for Image-guided Proton FLASH Radiotherapy
Chih-Wei Chang, Yang Lei, Tonghe Wang +7
Proton FLASH therapy leverages ultra-high dose-rate radiation to enhance the sparing of organs at risk without compromising tumor control probability. To prepare for the delivery o…
MRI-based Material Mass Density and Relative Stopping Power Estimation via Deep Learning for Proton Therapy
Yuan Gao, Chih-Wei Chang, Sagar Mandava +10
Magnetic Resonance Imaging (MRI) is increasingly incorporated into treatment planning, because of its superior soft tissue contrast used for tumor and soft tissue delineation versu…
Landmark Tracking in Liver US images Using Cascade Convolutional Neural Networks with Long Short-Term Memory
Yupei Zhang, Xianjin Dai, Zhen Tian +6
This study proposed a deep learning-based tracking method for ultrasound (US) image-guided radiation therapy. The proposed cascade deep learning model is composed of an attention n…
Deformable Image Registration using Unsupervised Deep Learning for CBCT-guided Abdominal Radiotherapy
Huiqiao Xie, Yang Lei, Yabo Fu +6
CBCTs in image-guided radiotherapy provide crucial anatomy information for patient setup and plan evaluation. Longitudinal CBCT image registration could quantify the inter-fraction…
Artificial Intelligence in Tumor Subregion Analysis Based on Medical Imaging: A Review
Mingquan Lin, Jacob Wynne, Yang Lei +4
Medical imaging is widely used in cancer diagnosis and treatment, and artificial intelligence (AI) has achieved tremendous success in various tasks of medical image analysis. This…
Generative Adversarial Network for Image Synthesis
Yang Lei, Richard L. J. Qiu, Tonghe Wang +3
This chapter reviews recent developments of generative adversarial networks (GAN)-based methods for medical and biomedical image synthesis tasks. These methods are classified into…
Synthetic MRI-aided Head-and-Neck Organs-at-Risk Auto-Delineation for CBCT-guided Adaptive Radiotherapy
Xianjin Dai, Yang Lei, Tonghe Wang +7
Purpose: Organ-at-risk (OAR) delineation is a key step for cone-beam CT (CBCT) based adaptive radiotherapy planning that can be a time-consuming, labor-intensive, and subject-to-va…
Knowledge-based Radiation Treatment Planning: A Data-driven Method Survey
Shadab Momin, Yabo Fu, Yang Lei +5
This paper surveys the data-driven dose prediction approaches introduced for knowledge-based planning (KBP) in the last decade. These methods were classified into two major categor…
Learning-Based Synthetic Dual Energy CT Imaging from Single Energy CT for Stopping Power Ratio Calculation in Proton Radiation Therapy
Serdar Charyyev, Tonghe Wang, Yang Lei +7
Purpose: Dual-energy CT (DECT) has been shown to derive stopping power ratio (SPR) map with higher accuracy than conventional single energy CT (SECT) by obtaining the energy depend…
Deep learning-based Real-time Volumetric Imaging for Lung Stereotactic Body Radiation Therapy: A Proof of Concept Study
Yang Lei, Zhen Tian, Tonghe Wang +5
Due to the inter- and intra- variation of respiratory motion, it is highly desired to provide real-time volumetric images during the treatment delivery of lung stereotactic body ra…
Intensity Non-uniformity Correction in MR Imaging Using Residual Cycle Generative Adversarial Network
Xianjin Dai, Yang Lei, Yingzi Liu +6
Purpose: Correcting or reducing the effects of voxel intensity non-uniformity (INU) within a given tissue type is a crucial issue for quantitative MRI image analysis in daily clini…
Medical Imaging Synthesis using Deep Learning and its Clinical Applications: A Review
Tonghe Wang, Yang Lei, Yabo Fu +3
This paper reviewed the deep learning-based studies for medical imaging synthesis and its clinical application. Specifically, we summarized the recent developments of deep learning…
Deep Learning in Multi-organ Segmentation
Yang Lei, Yabo Fu, Tonghe Wang +4
This paper presents a review of deep learning (DL) in multi-organ segmentation. We summarized the latest DL-based methods for medical image segmentation and applications. These met…
Machine Learning in Quantitative PET Imaging
Tonghe Wang, Yang Lei, Yabo Fu +3
This paper reviewed the machine learning-based studies for quantitative positron emission tomography (PET). Specifically, we summarized the recent developments of machine learning-…
Deep Learning in Medical Image Registration: A Review
Yabo Fu, Yang Lei, Tonghe Wang +3
This paper presents a review of deep learning (DL) based medical image registration methods. We summarized the latest developments and applications of DL-based registration methods…
Machine-learning-based Classification of Lower-grade gliomas and High-grade gliomas using Radiomic Features in Multi-parametric MRI
Ge Cui, Jiwoong Jeong, Bob Press +6
Objectives: Glioblastomas are the most aggressive brain and central nervous system (CNS) tumors with poor prognosis in adults. The purpose of this study is to develop a machine-lea…
Air, bone and soft-tissue Segmentation on 3D brain MRI Using Semantic Classification Random Forest with Auto-Context Model
Xue Dong, Yang Lei, Sibo Tian +7
As bone and air produce weak signals with conventional MR sequences, segmentation of these tissues particularly difficult in MRI. We propose to integrate patch-based anatomical sig…