Authors: Mingli Chen, Xuejun Gu, Hao Jiang, Mahdieh Kazemimoghadam, Weiguo Lu, Gregory Szalkowski, Qingying Wang, Kangning Zhang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Department of Radiation Oncology, Stanford University School of Medicine
Abstract Preview: Purpose: Respiratory motion management is crucial for accurate radiation delivery to moving targets while protecting healthy tissue, relying on time-resolved volumetric imaging and continuous deformab...
Authors: Chuxiong Ding, Xuejun Gu, Heejung Kim, Weiguo Lu, Yang Kyun Park, Yulong Yan
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, University of Texas Southwestern Medical Center, Department of Radiation Oncology, Stanford University School of Medicine
Abstract Preview: Purpose: The purpose of this study was to develop and evaluate a Monte Carlo (MC) dose calculation software (DosemanCK) for secondary dose verification of CyberKnife (CK) treatment plans. Unlike tradi...
Authors: Mingli Chen, Xuejun Gu, Hao Jiang, Mahdieh Kazemimoghadam, Weiguo Lu, Qingying Wang, Kangning Zhang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Department of Radiation Oncology, Stanford University School of Medicine
Abstract Preview: Purpose:
PET is used in radiotherapy workflows for accurate target delineation. However, a separate CT scan is typically required for attenuation correction in PET imaging and for registering PET-d...
Authors: Michael Dohopolski, Xuejun Gu, Hao Jiang, Steve B. Jiang, Christopher Kabat, Jingying Lin, Weiguo Lu, Michael Tang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center, Neuralrad LLC, Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Department of Radiation Oncology, Stanford University School of Medicine
Abstract Preview: Purpose: To streamline access to clinical data stored in Oncology Information Systems such as MOSAIQ or ARIA, we developed an AI-powered chatbot capable of querying, summarizing, and interactively ans...
Authors: John Byun, Juan J Cardona, Steven D Chang, Cynthia Fu-Yu Chuang, Xuejun Gu, Yusuke Hori, Hao Jiang, Fred Lam, Lianli Liu, Weiguo Lu, David Park, Erqi Pollom, Elham Rahimy, Deyaaldeen Abu Reesh, Scott Soltys, Gregory Szalkowski, Lei Wang
Affiliation: Department of Radiation Oncology, Stanford University, Department of Neurosurgery, Stanford School of Medicine, Department of Neurosurgery, Stanford University, Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Department of Radiation Oncology, Stanford University School of Medicine
Abstract Preview: Purpose:
Intraventricular tumors pose significant challenges in neurosurgery due to their complex location. Therefore, brain SRS could be a better treatment option. At our institution, some patient...
Authors: John Byun, Steven D Chang, Cynthia Fu-Yu Chuang, Xuejun Gu, Melanie Hayden Gephart, Yusuke Hori, Fred Lam, Gordon Li, Lianli Liu, Weiguo Lu, David Park, Erqi Pollom, Elham Rahimy, Deyaaldeen Abu Reesh, Scott Soltys, Gregory Szalkowski, Lei Wang, Xianghua Ye, Kangning Zhang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Department of Neurosurgery, Stanford University, Department of Radiation Oncology, Stanford University, Department of Radiation Oncology, Stanford University School of Medicine
Abstract Preview: Purpose: Accurate and automated delineation of vestibular schwannoma (VS) volume is crucial for disease management, as both treatment approaches (stereotactic radiosurgery and invasive surgery) and mo...
Authors: Xuejun Gu
Affiliation: Department of Radiation Oncology, Stanford University School of Medicine
Abstract Preview: N/A...
Authors: Mingli Chen, Xuejun Gu, Mahdieh Kazemimoghadam, Weiguo Lu, Qingying Wang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Department of Radiation Oncology, Stanford University School of Medicine
Abstract Preview: Purpose: This study introduces a novel template-guided deep learning framework for primary gross tumor volume (GTVp) segmentation, addressing challenges posed by diverse tumor types and enabling a uni...
Authors: John Byun, Steven D Chang, Mingli Chen, Cynthia Chuang, Xuejun Gu, Melanie Hayden Gephart, Yusuke Hori, Hao Jiang, Mahdieh Kazemimoghadam, Fred Lam, Gordon Li, Lianli Liu, Weiguo Lu, David Park, Erqi Pollom, Elham Rahimy, Deyaaldeen Abu Reesh, Scott Soltys, Gregory Szalkowski, Lei Wang, Qingying Wang, Zi Yang, Xianghua Ye, Kangning Zhang
Affiliation: Department of Radiation Oncology, Stanford University, Department of Neurosurgery, Stanford University, Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Department of Radiation Oncology, Stanford University School of Medicine
Abstract Preview: Purpose: Accurate prediction of pain relief is crucial in determining the clinical effectiveness of Stereotactic body radiotherapy (SBRT) regimen for spine metastases. We propose a deep-learning frame...
Authors: Mingli Chen, Xuejun Gu, Mahdieh Kazemimoghadam, Weiguo Lu, Qingying Wang, Zi Yang, Kangning Zhang, You Zhang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Department of Radiation Oncology, Stanford University School of Medicine
Abstract Preview: Purpose: Delivery efficiency and robustness are critical in spot-scanning proton arc therapy (SPAT), yet the conventional use of redundant energy layers (ELs) prolongs switching times and reduces effi...
Authors: Junwen Liu, Mengzhen Wang, Ning Wen, Jifeng Xiao, Fuhua Yan, Yanzhao Yang, Xuekun Zhang, Zheyu Zhang
Affiliation: Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Department of Radiology, Ruijin Hospital Shanghai Jiaotong University School of Medicine, Shanghai Jiaotong University, The SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Shanghai Jiaotong University Schoo of Medicine
Abstract Preview: Purpose:This study aims to develop and evaluate a large language model (LLM) fine-tuned to generate consistent and accurate impressions from imaging findings. Additionally, the study investigates the ...
Authors: Mingli Chen, Xuejun Gu, Hao Jiang, Mahdieh Kazemimoghadam, Weiguo Lu, Qingying Wang, Kangning Zhang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Department of Radiation Oncology, Stanford University School of Medicine
Abstract Preview: Purpose:
Deep learning-based automatic medical image segmentation is increasingly employed in clinical practice, significantly reducing manual workload. However, verifying segmentation results rema...
Authors: Mingli Chen, Xuejun Gu, Hao Jiang, Mahdieh Kazemimoghadam, Weiguo Lu, Qingying Wang, Kangning Zhang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Department of Radiation Oncology, Stanford University School of Medicine
Abstract Preview: Purpose:
This work demonstrates how existing software, when creatively adapted, can address a wide range of clinical challenges. By focusing on data exploration and application-specific modificatio...
Authors: Mingli Chen, Xuejun Gu, Hao Jiang, Mahdieh Kazemimoghadam, Weiguo Lu, Qingying Wang, Zi Yang, Kangning Zhang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Department of Radiation Oncology, Stanford University School of Medicine
Abstract Preview: Purpose: Dose prediction (DP) is essential in guiding radiotherapy planning. However, current DP models for intensity-modulated radiation therapy (IMRT) primarily rely on fixed-beam orientations and a...
Authors: Mingli Chen, Xuejun Gu, Hao Jiang, Mahdieh Kazemimoghadam, Weiguo Lu, Qingying Wang, Kangning Zhang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Department of Radiation Oncology, Stanford University School of Medicine
Abstract Preview: Purpose:
Converting MR images to synthetic CT (MR2sCT) is highly desirable as it streamlines the radiotherapy treatment planning workflow. This approach leverages the superior soft tissue visibilit...
Authors: Muhammad Ramish Ashraf, Kerriann Casey, Suparna Dutt, Jie Fu, Edward Elliot Graves, Xuejun Gu, Hao Jiang, Brianna Caroline Lau, Billy W Loo, Weiguo Lu, Rakesh Manjappa, Stavros Melemenidis, Erinn Bruno Rankin, Lawrie Skinner, Luis Armando Soto, Murat Surucu, Vignesh Viswanathan, Zi Yang, Amy Shu-Jung Yu
Affiliation: Department of Radiation Oncology, University of Washington and Fred Hutchinson Cancer Center, Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Department of Radiation Oncology, Stanford University School of Medicine, Department of Comparative Medicine, Stanford University School of Medicine, Department of Radiation Oncology, Stanford University Cancer Center
Abstract Preview: Purpose: The intestine is a classical preclinical model for studying radiation injury, and histological quantification of intestinal crypts is a key assay for assessing this response. However, substan...