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Results for "qualitative evaluation": 8 found

4D CBCT Dynamic Images Recovery Using a 4D Neural Network

Authors: Ziheng Deng, Yao Hao, Runping Hou, Deshan Yang, Jun Zhao, Yufu Zhou

Affiliation: Department of Radiation Oncology, Duke University, School of Biomedical Engineering, Shanghai Jiao Tong University, Washington University School of Medicine, Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine

Abstract Preview: Purpose: 4D CBCT has been developed to provide dynamic images for image-guided radiation therapy. However, as projection data are sorted into sparse and clustered phase-specific bins, 4D CBCT images a...

A Qualitative Evaluation of the Prostate Patients Hscbct Images and Limbus Contours

Authors: Doris Dimitriadis/Dimitriadou, M. Saiful Huq, Ronald John Lalonde, Fang Li, Noor Mail, Adam Olson

Affiliation: UPMC Hillman Cancer Center, UPMC Hillman Cancer Center and University of Pittsburgh School of Medicine, UPMC

Abstract Preview: Purpose: The objective of this study was to qualitatively evaluate Prostate Hypersight Cone-Beam CT (HSCBCT) images and assess its capability for Limbus auto-contouring. The qualitative evaluation and...

A Self-Supervised Deep Learning Approach for Automatic Identification and Metal Artifact Reduction in Cone-Beam CT for Brachytherapy

Authors: Rani Anne', Wenchao Cao, Yingxuan Chen, Wookjin Choi, Firas Mourtada, Yevgeniy Vinogradskiy

Affiliation: Thomas Jefferson University

Abstract Preview: Purpose: In-room mobile cone-beam CT (CBCT) is emerging to enhance high-dose-rate (HDR) brachytherapy workflow using on-demand imaging. However, metal artifacts from X-ray markers inside gynecological...

Clinical Feasibility of a Deep-Learning-Based Auto Contouring through Qualitative and Dosimetric Assessments

Authors: Sara Endo, Takeshi Fujisawa, Hidehiro Hojo, Masaki Nakamura, Hidenobu Tachibana

Affiliation: Department of Radiation Oncology, National Cancer Center Hospital East, Radiation Safety and Quality Assurance Division, National Cancer Center Hospital East

Abstract Preview: Purpose: To assess the clinical feasibility of deep learning (DL)-based automated contouring through qualitative and quantitative assessments.

Methods: Sixty cases were chosen, including 3 OARs...

Deep Learning-Driven Comparative Analysis of CNN-Based Architectures and High-Order Vision Mamba U-Net (H-vMUNet) for MRI-Based Brain Tumor Segmentation

Authors: Sang Hee Ahn, Nalee Kim, Do Hoon Lim

Affiliation: Samsung Medical Center, Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine

Abstract Preview: Purpose: MRI offers superior soft-tissue contrast, aiding tumor localization and segmentation in radiation therapy, which traditionally relies on oncologists' expertise. This study compares CNN-based ...

Evaluation of an Offline Adaptive CBCT Planning Workflow for Halcyon with Hypersight

Authors: Michelle Alonso-Basanta, Joshua Bryer, Lei Dong, Barbara Garcia, Elissa Khoudary, Brandon M. Koger, Taoran Li, Michael Salerno, Karen Tang, Boon-Keng Kevin Teo

Affiliation: University of Pennsylvania

Abstract Preview: Purpose: The Varian HyperSight imaging solution features a workflow for planning on CBCT images (CBCTp). This study evaluates the feasibility of CBCTp images in the setting of an offline adaptive plan...

High-Quality Patchnet (HQ-PatchNet) for Synthetic CT Generation in Head & Neck Imaging

Authors: So Hyun Ahn, Chris Beltran, Byongsu Choi, Jeong Heon Kim, Jin Sung Kim, Bo Lu, Justin Chunjoo Park, Bongyong Song, Jun Tan

Affiliation: Mayo Clinic, Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Ewha Medical Research Institute, Ewha Womans University College of Medicine, UC San Diego, Yonsei University College of Medicine

Abstract Preview: Purpose:
Cone-beam computed tomography (CBCT) is widely used in IGRT for patient positioning but suffers from low resolution and poor soft tissue contrast. Synthetic CT (sCT) generated from CBCT ad...

Memory-Efficient Deep Learning for Volumetric Cone-Beam CT Image Reconstruction

Authors: Ziqi Gao, Lei Xing, Siqi Ye, S. Kevin Zhou

Affiliation: Department of Radiation Oncology, Stanford University, University of Science and Technology of China (USTC)

Abstract Preview: Purpose: To address the challenge of high memory usage in volumetric cone-beam CT (CBCT) imaging, we propose a method that combines joint reconstruction and super-resolution for sparsely sampled CBCT ...