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Results for "pediatric dataset": 8 found

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...

Evaluating Commercial Auto-Segmentation Software: Is Performance on Pediatric Organs-at-Risk Accurate?

Authors: Gregory T. Armstrong, James E. Bates, Lei Dong, Ralph Ermoian, Jie Fu, Christine Hill-Kayser, Rebecca M. Howell, Sharareh Koufigar, John T. Lucas, Thomas E. Merchant, Tucker J. Netherton, Sogand Sadeghi

Affiliation: Department of Radiation Oncology, University of Washington and Fred Hutchinson Cancer Center, Department of Epidemiology and Cancer Control, St. Jude Childrenโ€™s Research Hospital, St. Jude Children's Research Hospital, Department of Radiation Oncology, Fred Hutchinson Cancer Center, University of Washington, Department of Radiation Oncology, St. Jude Childrenโ€™s Research Hospital, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology, University of Washington/ Fred Hutchinson Cancer Center, Department of Radiation Oncology, University of Pennsylvania, University of Pennsylvania, Department of Radiation Oncology and Winship Cancer Institute, Emory University

Abstract Preview: Purpose: This study evaluates the adaptability and limitations of commercially available (MIM, RayStation) tools trained on predominately adult datasets (ages 20โ€“60+ years) for delineating organs at r...

Evaluation of an Adaptive Denoising Diffusion Probabilistic Model (DDPM) for Fast MRI in Radiotherapy Planning of Pediatric Brain Tumors

Authors: Chia-Ho Hua, Jirapat Likitlersuang, Jinsoo Uh

Affiliation: St. Jude Children's Research Hospital

Abstract Preview: Purpose: AI-based fast MRI, which reconstructs images from undersampled k-space data, has not yet been tailored for RT planning. This study aims to evaluate the fast MRI performance of our recently pr...

Icrp Mesh-Type Reference Computational Phantoms Representing Pregnant Women and Fetuses

Authors: Wesley E. Bolch, Chansoo Choi, Chan Hyeong Kim, Suhyeon Kim, Bangho Shin, Yeon Soo Yeom

Affiliation: Hanyang University, University of Florida, 2) Department of Radiation Convergence Engineering, Yonsei University

Abstract Preview: Purpose: Task Group 103 of International Commission on Radiological Protection (ICRP) recently released new-generation adult and pediatric mesh-type reference computational phantoms (MRCPs) via ICRP P...

Noise Sensitivity of Benchmark Whole-Body CT Segmentation Models: Totalsegmentator and Vista3D Performance on an Independent Dataset

Authors: Samuel L. Brady, Shruti Hegde, Alexander Knapp, Usman Mahmood, Joseph G. Meier, Elanchezhian Somasundaram, Zachary Taylor

Affiliation: Cincinnati Children's Hospital Medical Ctr, Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Cincinnati Children's Hospital Medical Center, Cincinnati Childrens Hospital Med Ctr

Abstract Preview: Purpose:
To assess how two benchmark multi-organ CT segmentation models respond to varying image noise levels.
Methods:
This study utilized the pediatric CT dataset from The Cancer Imaging Ar...

Performance Comparison of Artificial Intelligence-Based Auto-Segmentation Software on Pediatric CT Image Datasets for the Creation of Patient Specific Computational Phantoms

Authors: Wesley E. Bolch, Emily L. Marshall, Dhanashree Rajderkar, Wyatt Smither

Affiliation: University of Florida

Abstract Preview: Purpose: To determine the accuracy of TotalSegmentator, an AI-based automatic segmentation toolkit, on pediatric CT scans as the original software was trained on adult image datasets with a mean patie...

Supercomputing-Enabled CT Virtual Imaging Trials: A Population-Scale Pilot Study

Authors: Ehsan Abadi, Zakaria Aboulbanine, Nicholas D Felice, David Fenwick, Anuj J. Kapadia, Cindy Marie McCabe, Jayasai Ram Rajagopal, Ehsan Samei

Affiliation: Duke University, Oak Ridge National Laboratory, Center for Virtual Imaging Trials, Duke University, Clinical Imaging Physics Group, Department of Radiology, Duke University Health System

Abstract Preview: Purpose:
Virtual imaging trials (VITs) offer a computational alternative to clinical imaging trials leveraging virtual patients, scanners, and interpreters to assess imaging questions. To provide m...

To Establish Local Diagnostic Reference Levels (DRLs) for Head and Neck Computed Tomography (CT) Exams in Abuja, Nigeria, and to Investigate the Performance of Brain Metastasis (BM) and Brain Lesion (BL) Segmentation Techniques Using U-Net Models.

Authors: Nuraddeen Nasiru Garba, Kalpana M Kanal, Abdullahi Mohammed, Rabiu Nasiru, Muhammad SHAFIU Shehu, Daniel Vergara, Joseph Everett Wishart

Affiliation: AHMADU BELLO UNIVERSITY, ZARIA, University of Washington

Abstract Preview: Purpose: To establish local Diagnostic Reference Levels (DRLs) for head and neck computed tomography (CT) exams in Abuja, Nigeria, and to investigate the performance of brain metastasis (BM) and brain...