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Results for "Kangning Zhang": 9 found

A Diffusion-Based AI Framework for Continuous Deformable Image Registration and Time-Resolved Dynamic CT Generation

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...

CT-Free PET Imaging: Synthetic CT Generation for Efficient and Accurate PET-Based Planning

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...

Development and Validation of a Deep Learning-Based Auto-Segmentation Module for Vestibular Schwannoma

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...

LLM-Enhanced Multi-Modal Framework for Predicting Pain Relief of Stereotactic Body Radiotherapy for Spine Metastases Using Clinical Factors and Imaging Reports

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...

Mid-Range Planning for Efficient and Robust Proton Arc Therapy

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...

Nnae: Automating Anomaly Detection and Quality Assurance in Medical Image Segmentation

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...

Teaching an Old Dog New Tricks: Unlocking Hidden Potential in Existing Frameworks for Versatile Radiotherapy Applications

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...

Unidose: A Universal Framework for IMRT Dose Prediction

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...

Universal MR-to-Synthetic CT: A Streamlined Framework for MR-Only Radiotherapy Planning

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...