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Results for "Harini Veeraraghavan": 11 found

Aapm Seed Grant Session

Authors: Meixu Chen, Zhuoran Jiang, Benjamin P. Lopez, Poliana Marinello, Harini Veeraraghavan

Affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, UT MD Anderson Cancer Center, Stanford University, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, UT Southwestern Medical Center

Abstract Preview: N/A...

Cloud Workflow AI Apps for Radiotherapy Image Analysis Using Pycerr and Seven Bridges-Cancer Genomics Cloud

Authors: Aditya P. Apte, Joseph O. Deasy, Sharif F. Elguindi, Aditi Iyer, Jue Jiang, Eve Marie LoCastro, Jung Hun Oh, Amita Shukla-Dave, Harini Veeraraghavan

Affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Memorial Sloan Kettering Cancer Center

Abstract Preview: Purpose: We present publicly shareable applications (apps) for AI-based radiotherapy segmentation workflows with pyCERR on Seven Bridges Cancer Genomics Cloud-based platform (CGC-SB)
Methods: Runni...

Comparison of AI-Based and Ants for Longitudinal Deformable Image Registration in Head and Neck Cancer

Authors: Aditya P. Apte, Joseph O. Deasy, Jue Jiang, Nancy Lee, Sudharsan Madhavan, Nishant Nadkarni, Lopamudra Nayak, Harini Veeraraghavan, Wei Zhao

Affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Memorial Sloan Kettering Cancer Center

Abstract Preview: Purpose: To track early response to radiotherapy using digital twins, it is crucial to quantify tumor volume and mass changes. Traditional tumor detection methods, particularly in image registration, ...

Foundation Models with Balanced Data Sampling Enhance Auto-Segmentation for Cardiac Substructures

Authors: Chloe Min Seo Choi, Nikhil Mankuzhy, Aneesh Rangnekar, Andreas Rimner, Maria Thor, Harini Veeraraghavan, Abraham Wu

Affiliation: Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Memorial Sloan Kettering Cancer Center

Abstract Preview: Purpose: Cardiac substructure irradiation predisposes patients for poor outcomes in thoracic radiation therapy. A deep learning model was developed to segment the cardiac substructures invariant to co...

Integrating Multiple Modalities with Pretrained Swin Foundation Model for Head and Neck Tumor Segmentation

Authors: Jue Jiang, Aneesh Rangnekar, Shiqin Tan, Harini Veeraraghavan

Affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Weill Cornell Graduate School of Medical Sciences

Abstract Preview: Purpose: Clinicians often use information from FDG-PET and CT to interpret and delineate gross tumor (GTVp) and nodal (GTVn) volumes for radiotherapy planning in head and neck (HN) cancer patients. He...

Inter-Patient Registration Methods for Voxel-Based Analysis in Lung Cancer

Authors: Chloe Min Seo Choi, Joseph O. Deasy, Jue Jiang, Sudharsan Madhavan, Nikhil Mankuzhy, Nishant Nadkarni, Andreas Rimner, Maria Thor, Harini Veeraraghavan, Abraham Wu

Affiliation: University of Freibrug, Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Memorial Sloan Kettering Cancer Center

Abstract Preview: Purpose: Voxel-based analysis (VBA) requires accurate topology-preserving inter-patient deformable image registration (DIR). This study assessed whether guiding a DIR method with geometric priors of t...

Multi-modal AI for Longitudinal Response Assessment

Authors: Harini Veeraraghavan

Affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center

Abstract Preview: N/A...

Multimodal Framework for Predicting Radiation-Induced Severe Acute Esophagitis in Esophageal Cancer

Authors: Yeona Cho, Chloe Min Seo Choi, Joseph O. Deasy, Jue Jiang, Jihun Kim, Jin Sung Kim, Nikhil Mankuzhy, Aneesh Rangnekar, Andreas Rimner, Maria Thor, Harini Veeraraghavan, Abraham Wu

Affiliation: University of Freibrug, Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Memorial Sloan Kettering Cancer Center, Yonsei University

Abstract Preview: Purpose: We hypothesized that combining clinical, imaging, and radiotherapy dose-distribution features could increase predictive model accuracy in radiation-induced severe acute esophagitis (SAE) in e...

Patient-Specific Imaging Modality Agnostic Virtual Digital Twins Modeling Temporally Varying Digestive Motion

Authors: James M. Balter, Lando S. Bosma, Jorge Tapias Gomez, Nishant Nadkarni, Mert R Sanbuncu, William Paul Segars, Ergys D. Subashi, Neelam Tyagi, Harini Veeraraghavan

Affiliation: University of Michigan, The University of Texas MD Anderson Cancer Center, Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Carl E. Ravin Advanced Imaging Laboratories and Center for Virtual Imaging Trials, Duke University Medical Center, Cornell University, University Medical Center Utrecht, Memorial Sloan Kettering Cancer Center

Abstract Preview: Purpose: Develop patient-specific virtual digital twin (VDT) cohorts modeling physically realistic spatio-temporal gastrointestinal (GI) organs (stomach and duodenum) digestive motion.
Methods: Pat...

Python-Native Cerr for Cloud-Based Medical Image Analyses

Authors: Aditya P. Apte, Joseph O. Deasy, Sharif Elguindi, Aditi Iyer, Jue Jiang, Eve Marie LoCastro, Jung Hun Oh, Amita Shukla-Dave, Harini Veeraraghavan

Affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Memorial Sloan Kettering Cancer Center

Abstract Preview: Purpose: We present port of popular Computational Environment for Radiological Research software platform to Python programming language to cater to cloud-based analyses.
Methods: The components of...

Segmentation Regularized Registration Training Improves Multi-Domain Generalization of Deformable Image Registration for MR-Guided Prostate Radiotherapy

Authors: Lando S. Bosma, Victoria Brennan, Nicolas Cote, ChengCheng Gui, Nima Hassan Rezaeian, Jue Jiang, Sudharsan Madhavan, Josiah Simeth, Neelam Tyagi, Harini Veeraraghavan, Michael J Zelefsky

Affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, NYU Langone Health, University Medical Center Utrecht, Memorial Sloan Kettering Cancer Center

Abstract Preview: Purpose: Deep learning-based deformable image registration (DIR) models often lack robustness when applied to datasets with differing imaging characteristics. We aimed to (1) improve registration netw...