Yang Lei

Yang Lei
Icahn School of Medicine at Mount Sinai | MSSM · Department of Radiation Oncology

Doctor of Engineering

About

301
Publications
59,947
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7,104
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Introduction
Yang Lei currently works at the Department of Radiation Oncology, Emory University.

Publications

Publications (301)
Article
Full-text available
Background and Purpose. Although radiotherapy techniques are a primary treatment for head and neck cancer (HNC), they are still associated with substantial toxicity and side effects. Machine learning (ML) based radiomics models for predicting toxicity mostly rely on features extracted from pre-treatment imaging data. This study aims to compare diff...
Article
Full-text available
Background The widespread adoption of knowledge‐based planning in radiation oncology clinics is hindered by the lack of data and the difficulty associated with sharing medical data. Purpose This study aims to assess the feasibility of mitigating this challenge through federated learning (FL): a centralized model trained with distributed datasets,...
Article
Very fast imaging techniques can enhance the precision of image-guided radiation therapy, which can be useful for external beam radiation therapy. This work aims to develop a deep learning (DL)-based image-guide framework to enable fast volumetric image reconstruction for accurate target localization for treating lung cancer patients with gating, a...
Preprint
This study is to evaluate the impact of different normal lung volumes on radiation pneumonitis (RP) prediction in patients with locally advanced non-small-cell-lung-cancer (LA-NSCLC) receiving radiotherapy. Three dosimetric variables (V20, V5, and mean lung dose (MLD)) were calculated using treatment plans from 442 patients with LA-NSCLC enrolled i...
Article
Full-text available
Background Stereotactic body radiotherapy (SBRT) is a well‐established treatment modality for liver metastases in patients unsuitable for surgery. Both CT and MRI are useful during treatment planning for accurate target delineation and to reduce potential organs‐at‐risk (OAR) toxicity from radiation. MRI‐CT deformable image registration (DIR) is re...
Article
Full-text available
Magnetic Resonance Imaging (MRI) is increasingly being used in treatment planning due to its superior soft tissue contrast, which is useful for tumor and soft tissue delineation compared to computed tomography (CT). However, MRI cannot directly provide mass density or relative stopping power (RSP) maps, which are required for calculating proton rad...
Article
Full-text available
Background Stereotactic body radiotherapy (SBRT) is a well-established treatment modality for liver metastases in patients unsuitable for surgery. Both CT and MRI are useful during treatment planning for accurate target delineation and to reduce potential organs-at-risk (OAR) toxicity from radiation. MRI-CT deformable image registration (DIR) is re...
Article
Full-text available
Background and purpose A novel radiotracer, ¹⁸F-fluciclovine (anti-3-¹⁸F-FACBC), has been demonstrated to be associated with significantly improved survival when it is used in PET/CT imaging to guide postprostatectomy salvage radiotherapy for prostate cancer. We aimed to investigate the feasibility of using a deep learning method to automatically d...
Article
Full-text available
The hippocampus plays a crucial role in memory and cognition. Because of the associated toxicity from whole brain radiotherapy, more advanced treatment planning techniques prioritize hippocampal avoidance, which depends on an accurate segmentation of the small and complexly shaped hippocampus. To achieve accurate segmentation of the anterior and po...
Article
Full-text available
Background The number of patients undergoing proton therapy has increased in recent years. Current treatment planning systems (TPS) calculate dose maps using three-dimensional (3D) maps of relative stopping power (RSP) and mass density. The patient-specific maps of RSP and mass density were obtained by translating the CT number (HU) acquired using...
Article
Full-text available
Background While magnetic resonance imaging (MRI) provides high resolution anatomical images with sharp soft tissue contrast, magnetic resonance spectroscopy (MRS) enables non‐invasive detection and measurement of biochemicals and metabolites. However, MRS has low signal‐to‐noise ratio (SNR) when concentrations of metabolites are in the range of mi...
Article
Full-text available
Background An automated, accurate, and efficient lung four‐dimensional computed tomography (4DCT) image registration method is clinically important to quantify respiratory motion for optimal motion management. Purpose The purpose of this work is to develop a weakly supervised deep learning method for 4DCT lung deformable image registration (DIR)....
Article
In this work, we demonstrate a method for rapid synthesis of high-quality CT images from unpaired, low-quality CBCT images, permitting CBCT-based adaptive radiotherapy. We adapt contrastive unpaired translation (CUT) to be used with medical images and evaluate the results on an institutional pelvic CT dataset. We compare the method against cycleGAN...
Preprint
Full-text available
Background: The hippocampus plays a crucial role in memory and cognition. Because of the associated toxicity from whole brain radiotherapy, more advanced treatment planning techniques prioritize hippocampal avoidance, which depends on an accurate segmentation of the small and complexly shaped hippocampus. Purpose: To achieve accurate segmentation o...
Article
Background: The hippocampus plays a crucial role in memory and cognition. Because of the associated toxicity from whole brain radiotherapy, more advanced treatment planning techniques prioritize hippocampal avoidance, which depends on an accurate segmentation of the small and complexly shaped hippocampus. Purpose: To achieve accurate segmentatio...
Article
Full-text available
Proton therapy is a type of radiation therapy that can provide better dose distribution compared to photon therapy by delivering most of the energy at the end of range, which is called the Bragg peak (BP). The protoacoustic technique was developed to determine the BP locations in vivo, but it requires a large dose delivery to the tissue to obtain a...
Article
Full-text available
CBCTs in image-guided radiotherapy provide crucial anatomy information for patient setup and plan evaluation. Longitudinal CBCT image registration could quantify the inter-fractional anatomic changes, e.g. tumor shrinkage, daily OAR variation throughout the course of treatment. The purpose of this study is to propose an unsupervised deep learning b...
Preprint
Background: Magnetic resonance spectroscopy (MRS) enables non-invasive detection and measurement of biochemicals and metabolites. However, MRS has low signal-to-noise ratio (SNR) when concentrations of metabolites are in the range of the million molars. Standard approach of using a high number of signal averaging (NSA) to achieve sufficient NSR com...
Article
Background: Magnetic resonance spectroscopy (MRS) enables non-invasive detection and measurement of biochemicals and metabolites. However, MRS has low signal-to-noise ratio (SNR) when concentrations of metabolites are in the range of the million molars. Standard approach of using a high number of signal averaging (NSA) to achieve sufficient NSR co...
Article
Full-text available
Purpose Radiation damage on neurovascular bundles (NVBs) may be the cause of sexual dysfunction after radiotherapy for prostate cancer. However, it is challenging to delineate NVBs as organ‐at‐risks from planning CTs during radiotherapy. Recently, the integration of MR into radiotherapy made NVBs contour delineating possible. In this study, we aim...
Article
Full-text available
Purpose The long acquisition time of CBCT discourages repeat verification imaging, therefore increasing treatment uncertainty. In this study, we present a fast volumetric imaging method for lung cancer radiation therapy using an orthogonal 2D kV/MV image pair. Methods The proposed model is a combination of 2D and 3D networks. The proposed model co...
Article
Full-text available
This study proposed a deep learning-based tracking method for ultrasound-guided radiation therapy. The proposed cascade deep learning model is composed of an attention network, a mask region-based convolutional neural network (mask R-CNN), and a long short-term memory (LSTM) network. The attention network learns a mapping from a US image to a suspe...
Article
Full-text available
Background Manual contouring is very labor‐intensive, time‐consuming, and subject to intra‐ and inter‐observer variability. An automated deep learning approach to fast and accurate contouring and segmentation is desirable during radiotherapy treatment planning. Purpose This work investigates an efficient deep‐learning‐based segmentation algorithm...
Article
Full-text available
Radiotherapy (RT) doses to cardiac substructures from the definitive treatment of locally advanced non-small cell lung cancers (NSCLC) have been linked to post-RT cardiac toxicities. With modern treatment delivery techniques, it is possible to focus radiation doses to the planning target volume while reducing cardiac substructure doses. However, it...
Article
Purpose/Objective(s) The application of MRI significantly improves the accuracy and reliability of target delineation for many disease sites in radiotherapy (RT) due to its superior soft tissue contrast as compared with CT. One common practice in acquiring the MR images is to use thick slices while keeping the in-plane resolution, especially for th...
Article
Purpose/Objective(s) Molecular imaging with novel PET radiotracers has been shown to significantly impact radiotherapy decision making, target definition, and cancer control in the setting of prostate cancer (PCa) recurrent after prostatectomy. We propose a deep learning-based method to automatically segment pelvic node and prostate bed lesions on...
Preprint
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 (BP). Protoacoustic technique was developed to in vivo determine the BP locations. However, it requires large dose delivery to the tissue to obtain an averaged acoustic si...
Conference Paper
Purpose: Dual-energy CT (DECT) has been shown to derive a stopping-power-ratio (SPR) map with higher accuracy than conventional single-energy CT (SECT) by obtaining energy dependence of photon interactions. However, DECT is not as widely implemented as SECT in proton radiotherapy simulation. This work presents a learning-based method to synthesize...
Conference Paper
Purpose: Proton radiography (PRG) is very valuable for beam’s-eye-view target localization in proton radiotherapy, especially for shoot-through proton FLASH. However, PRG has very poor contrast and spatial resolution. To deal with this issue, we propose a machine-learning-based method to use kV digitally reconstructed radiograph (DRR) to improve PR...
Article
Full-text available
Objective: This work aims to develop an automated segmentation method for the prostate and its surrounding organs-at-risk (OAR) in pelvic computed tomography to facilitate prostate radiation treatment planning. Approach: In this work, we propose a novel deep-learning algorithm combining a U-shaped convolutional neural network (CNN) and vision tr...
Article
Full-text available
Background Multimodality positron emission tomography/computed tomography (PET/CT) imaging combines the anatomical information of CT with the functional information of PET. In the diagnosis and treatment of many cancers, such as non‐small cell lung cancer (NSCLC), PET/CT imaging allows more accurate delineation of tumor or involved lymph nodes for...
Preprint
Magnetic Resonance Imaging (MRI) is increasingly incorporated into treatment planning, because of its superior soft tissue contrast used for tumor and soft tissue delineation versus computed tomography (CT). However, MRI cannot directly provide mass density or relative stopping power (RSP) maps required for proton radiotherapy dose calculation. To...
Preprint
Full-text available
Objective: FLASH radiotherapy leverages ultra-high dose-rate radiation to enhance the sparing of organs at risk without compromising tumor control probability. This may allow dose escalation, toxicity mitigation, or both. To prepare for the ultra-high dose-rate delivery, we aim to develop a deep learning (DL)-based image-guide framework to enable f...
Preprint
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 network, a mask region-based convolutional neural network (mask R-CNN), and a long short-term memory (LSTM) network. The attention network learns a mapping from a US image...
Preprint
CBCTs in image-guided radiotherapy provide crucial anatomy information for patient setup and plan evaluation. Longitudinal CBCT image registration could quantify the inter-fractional anatomic changes. The purpose of this study is to propose an unsupervised deep learning based CBCT-CBCT deformable image registration. The proposed deformable registra...
Article
Full-text available
Purpose A quality assurance (QA) CT scans are usually acquired during cancer radiotherapy to assess for any anatomical changes, which may cause an unacceptable dose deviation and therefore warrant a replan. Accurate and rapid deformable image registration (DIR) is needed to support contour propagation from the planning CT (pCT) to the QA CT to faci...
Article
Full-text available
Proton therapy requires accurate dose calculation for treatment planning to ensure the conformal doses are precisely delivered to the targets. The conversion of CT numbers to material properties is a significant source of uncertainty for dose calculation. The aim is to develop a physics-informed deep learning (PIDL) framework to derive accurate mas...
Article
Full-text available
Purpose Dose escalation to dominant intraprostatic lesions (DILs) is a novel treatment strategy to improve the treatment outcome of prostate radiation therapy. Treatment planning requires accurate and fast delineation of the prostate and DILs. In this study, a 3D cascaded scoring convolutional neural network is proposed to automatically segment the...
Article
Full-text available
Current segmentation practice for thoracic cancer RT considers the whole heart as a single organ despite increased risks of cardiac toxicities from irradiation of specific cardiac substructures. This may be due to time consuming process of manually segmenting up to 15 cardiac substructures that can have large anatomic variations from one patient to...
Article
Full-text available
Accurate segmentation of glioma and its subregions plays an important role in radiotherapy treatment planning. Due to a very populated multiparameter magnetic resonance imaging (mpMRI) image, manual segmentation task can be very time-consuming, meticulous, and prone to subjective errors. Here, we propose a novel deep learning (DL) framework based o...
Article
Full-text available
Purpose Gadolinium‐based contrast agents (GBCAs) are widely administrated in MR imaging for diagnostic studies and treatment planning. Although GBCAs are generally thought to be safe, various health and environmental concerns have been raised recently about their use in MR imaging. The purpose of this work is to derive synthetic contrast enhance MR...
Article
Full-text available
Artificial intelligence (AI) is a broad term that generally refers to the artificial creation of human-like intelligence that can learn, perceive and plan. To date, AI techniques in medical domains have sparked tremendous innovations. With the continuous development of large datasets and computer science, AI has become a promising avenue in medicin...