Authors: Deshan Yang, Zhendong Zhang
Affiliation: Duke University, Department of Radiation Oncology, Duke University
Abstract Preview: Purpose:
The evaluation of deformable image registration (DIR) algorithms is crucial for improving accuracy and clinical adoption. However, reliable benchmarks, especially for inter-modality regist...
Authors: Adayabalam Balajee, Elijah Berberette, Maria Escalona, Dray Gentry, Chester R. Ramsey, Terri Ryan
Affiliation: ORAU, Thompson Proton Center, University of Tennessee
Abstract Preview: Purpose:
Dicentric chromosomes, characterized by two centromeres on a single chromosome, are key biomarkers in biological dosimetry for quantifying ionizing radiation exposure. However, manual dete...
Authors: Michael Cuddy, Samuel J. Fahrenholtz, Khushnood Hamdani, Saman Jirjes, Robert G. Paden, Jeremiah W. Sanders, William F. Sensakovic, Wolfgang Stefan, Jeffrey Xiao, Yuxiang Zhou
Affiliation: Mayo, Mayo Clinic Arizona, Mayo Clinic
Abstract Preview: Title: An automated system for MRI coil performance evaluation
Purpose: To develop an automated quality control (QC) system for MRI coils to assess element-level signal-to-noise ratio (SNR), ar...
Authors: Jingyun Chen, Yading Yuan
Affiliation: Columbia University Irving Medical Center, Department of Radiation Oncology
Abstract Preview: Purpose: To develop and evaluate the Scale-attention network (SANet) for automated pancreas segmentation on MR images.
Methods: To develop SANet, we extended the classic U-Net design with a dynamic...
Authors: Kyle J. Lafata, Casey Y. Lee, Xiang Li, Megan K. Russ, Zion Sheng
Affiliation: Duke University, Department of Radiation Oncology, Duke University, Clinical Imaging Physics Group, Department of Radiology, Duke University Health System
Abstract Preview: Purpose:
Traditional deep learning-based cell segmentation models face limitations, such as the need for extensive training data and retraining when encountering new cell types or domains. This stu...
Authors: Austin Castelo, Xinru Chen, Caroline Chung, Laurence Edward Court, Jaganathan A Parameshwaran, Zhan Xu, Jinzhong Yang, Yao Zhao
Affiliation: The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Department of Imaging Physics, The University of Texas MD Anderson Cancer Center
Abstract Preview: Purpose:
To develop a deep learning-based segmentation model to automatically delineate tumors from full-body PET/CT images.
Methods:
PET/CT image pairs of 91 patients were collected for this...
Authors: Majd Antaki, Rohini Bhatia, Gayoung Kim, Yosef Landman, Junghoon Lee, Akila N. Viswanathan
Affiliation: Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Physics and Advanced Development Elekta
Abstract Preview: Purpose: Brachytherapy is a standard radiation therapy approach for cervical cancer, which directly delivers radiation source to the tumor using catheters. Treatment planning requires identification o...
Authors: Hajar Moradmand, Lei Ren
Affiliation: University of Maryland School of Medicine, University of Maryland
Abstract Preview: Purpose:
The Sharp-van der Heijde (SvH) score is essential for assessing joint damage in rheumatoid arthritis (RA) from radiographic images. However, manual scoring is time-intensive and prone to v...
Authors: Rex A. Cardan, Carlos E. Cardenas, Quan Chen, Jingwei Duan, Joseph Harms, Joel A. Pogue, Richard A. Popple, Yi Rong, Dennis N. Stanley, Natalie N. Viscariello, Libing Zhu
Affiliation: Washington University in St. Louis, The University of Alabama at Birmingham, Mayo Clinic Arizona, University of Alabama at Birmingham
Abstract Preview: Purpose: Manual verification of organs-at-risk(OARs) delineations is a critical yet time-intensive process, often susceptible to unintentional oversights. To assist the reviewing process, a population...
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, ...
Authors: Kota Hirose, Daisuke Kawahara, Jokichi Kawazoe, Yuji Murakami
Affiliation: Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Graduate School of Biomedical and Health Sciences, Hiroshima University
Abstract Preview: Purpose: Synthesizing medical images can address the lack of or unscanned medical images, reducing scanner time and costs. However, paired image scarcity remains a challenge for image synthesis. We pr...
Authors: Hamdah Alanazi, Silvia Pella
Affiliation: FAU, Florida Atlantic University
Abstract Preview: Purpose: The appearance of breast cancer in the global list of most common cancers worldwide requires
research for ultimate treatment approaches including radiation therapy to reduce deaths from br...
Authors: Minbin Chen, Ke Lu, Kaizhong Shi, Chunhao Wang, Chuan Wu, Zhenyu Yang, Fang-Fang Yin, Jingtong Zhao
Affiliation: The First People's Hospital of Kunshan, Duke University, Medical Physics Graduate Program, Duke Kunshan University, Duke Kunshan University, Department of Radiation Oncology, Duke Kunshan University
Abstract Preview: Purpose: MRI-based automatic detection of brain metastases is often challenged by the small size and subtle nature of metastases. This study aimed to develop a novel deep learning-based brain metastas...
Authors: Yoonha Eo
Affiliation: Yonsei University
Abstract Preview: Purpose: To develop a fully automatic and unsupervised algorithm for estimating the Exposure Index (EI) of various Regions of Interest in X-ray imaging using advanced foundation models. Traditional EI...
Authors: Omar Awad, Alfredo Enrique Echeverria, Issam M. El Naqa, Daniel Allan Hamstra, Yiding Han, Ryan Lafratta, Abdallah Sherif Radwan Mohamed, Piyush Pathak, Zaid Ali Siddiqui, Baozhou Sun, Vincent Ugarte
Affiliation: H. Lee Moffitt Cancer Center, Harris Health, Baylor College of Medicine
Abstract Preview: Purpose:
Accurate detection and segmentation of brain metastases are critical for diagnosis, treatment planning, and follow-up imaging but are challenging due to labor-intensive manual assessments ...
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: Ilias Gatos, Stavros Grigoriadis, George C. Kagadis, Maria Karamesini, Paraskevi Katsakiori, Dimitris N. Mihailidis, Stavros Spiliopoulos, Efstratios Syrmas, Ioannis Theotokas, Stavros Tsantis, Pavlos Zoumpoulis
Affiliation: Diagnostic Echotomography, University of Pennsylvania, University of Athens, University of Patras
Abstract Preview: Purpose: To detect prostate lesions on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images which is a particularly difficult task due to the heterogeneous and inconsistent representa...