Authors: Yunxiang Li, Hua-Chieh Shao, Chenyang Shen, Jing Wang, Jiacheng Xie, Shunyu Yan, You Zhang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, The University of Texas at Dallas
Abstract Preview: Purpose: Accurate liver deformable motion tracking is essential in image-guided radiotherapy to enable precise tumor targeting during treatment. We developed a conditional point cloud diffusion model ...
Authors: Jenghwa Chang, Kuan Huang, Lyu Huang, Jason Lima, Jian Liu, Farzin Motamedi
Affiliation: Northwell, Department of Computer Science and Technology, Kean University, Physics and Astronomy, Hofstra University, Hofstra University Medical Physics Program
Abstract Preview: Title: A Deep Learning Approach to the Prediction of Gamma Passing Rates in VMAT Radiotherapy Plans for Adaptive Treatment.
Purpose: This study aims to develop a deep learning algorithm to predict ...
Authors: Wenfeng He, Tian Liu, Pretesh Patel, Richard L.J. Qiu, Keyur Shah, Tonghe Wang, Xiaofeng Yang, Chulong Zhang
Affiliation: Icahn School of Medicine at Mount Sinai, Emory University, Medical Physics Graduate Program, Duke Kunshan University, Memorial Sloan Kettering Cancer Center, Department of Radiation Oncology and Winship Cancer Institute, Emory University
Abstract Preview: Purpose: This study introduces a tracking-free approach to reconstruct 3D ultrasound (US) volumes from 2D freehand US scans. By eliminating the reliance on external tracking systems, this method aims ...
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...
Authors: Yankun Lang, Lei Ren, Leshan Sun, Liangzhong Xiang, Yifei Xu, Jie Zhang
Affiliation: University of Maryland School of Medicine, University of California, Irvine
Abstract Preview: Purpose: To achieve the full-view image from a single-view sinogram using a two-stage deep learning model for electroacoustic-tomography (EAT), which is an emerging imaging technique with significant ...
Authors: Penghao Gao, Zejun Jiang
Affiliation: Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Artificial Intelligence Laboratory, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences
Abstract Preview: Purpose: Real-time tumor tracking can effectively compensate for the impact of respiratory motion on dose distribution. We propose a patient-specific external-internal correlation model driven by opti...
Authors: Mojtaba Behzadipour, Siyong Kim, Mitchell Polizzi, Richard R. Wargo, Lulin Yuan
Affiliation: VCU Health - Department of Radiology, Virginia Commonwealth University
Abstract Preview: Purpose:
The purpose of this study is to develop a method for generating missing tissue in CT scans of patients with large body sizes, where the field of view (FOV) of the scanner fails to capture ...
Authors: Hilary P Bagshaw, Mark K Buyyounouski, Cynthia Fu-Yu Chuang, Yu Gao, Dimitre Hristov, Lianli Liu, Lawrie Skinner, Lei Xing
Affiliation: Department of Radiation Oncology, Department of Radiation Oncology, Stanford University
Abstract Preview: Purpose:
MR-guided radiation therapy has introduced a significant leap in cancer treatment by allowing adaptive treatment. The low-field MR-guided system predominantly uses the TrueFISP sequence, w...
Authors: Shanli Ding, Osama R. Mawlawi, Tinsu Pan
Affiliation: UT MD Anderson Cancer Center
Abstract Preview: Purpose:
Reliable detection of anomalies in Gamma Camera/SPECT flood images is vital for quality assurance (QA). Traditional methods relying on numerical thresholds and manual inspections often mis...
Authors: Sean P. Devan, Cory S. Knill, Charles K. Matrosic, Zheng Zhang
Affiliation: University of Michigan
Abstract Preview: Purpose: Physicists troubleshooting machine issues during patient treatments often face high-pressure situations, balancing error codes, resource constraints, and time-sensitive decisions. To streamli...
Authors: Hassan Bagher-Ebadian, Indrin J. Chetty, Mohamed Elshaikh, Ahmed I Ghanem, Mohammad M. Ghassemi, Reza Khanmohammadi, Benjamin Movsas, Shayan Siddiqui, Kundan S Thind, Jawad Turfa
Affiliation: Michigan State University, Department of Radiation Oncology,Cedars-Sinai Medical Center, Department of Radiation Oncology, Henry Ford Health-Cancer, Detroit, MI and Alexandria Department of Clinical Oncology, Faculty of Medicine, Alexandria University, Henry Ford Health
Abstract Preview: Purpose: Extracting late radiotherapy-induced toxicities from free-text notes using natural language processing is complicated by negative symptom identification, computational demands, and data priva...
Authors: Zachary Buchwald, Chih-Wei Chang, Zach Eidex, Richard L.J. Qiu, Mojtaba Safari, Shansong Wang, Xiaofeng Yang, David Yu
Affiliation: Emory University and Winship Cancer Institute, Emory University, Department of Radiation Oncology and Winship Cancer Institute, Emory University
Abstract Preview: Purpose: MRI offers excellent soft tissue contrast for diagnosis and treatment but suffers from long acquisition times, causing patient discomfort and motion artifacts. To accelerate MRI, supervised d...
Authors: Meixu Chen, Jing Wang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center
Abstract Preview: Purpose: Cancer treatment outcome prediction plays a pivotal role in guiding therapeutic decisions and optimizing patient care. Traditionally, binary prediction models have been widely used for risk s...
Authors: Jie Hu, Zhengdong Jiang, Nan Li, Tie Lv, Yuqing Xia, Shouping Xu, Gaolong Zhang, Wei Zhao, Changyou Zhong
Affiliation: School of Physics, Beihang University, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Radiotherapy Department of Meizhou People’s Hospital (Huangtang Hospital), UT Health San Antonio, National Cancer Center/ National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, Peopleʼs Republic of China, Department of Radiation Oncology
Abstract Preview: Purpose: Patients usually undergo cone-beam computed tomography (CBCT) scans which are used for patient set-up before radiotherapy. However, the low image quality of CBCT hinders its use in adaptive r...
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: Mehdi Amini, Minerva Becker, Simina Chiriac, Alexandre Cusin, Dimitrios Daskalou, Ghasem Hajianfar, Sophie Neveu, Marcella Pucci, Yazdan Salimi, Pascal Senn, Habib Zaidi
Affiliation: Geneva University Hospital, Division of Radiology, Diagnostic Department, Geneva University Hospitals, Service of Otorhinolaryngology-Head and Neck Surgery, Department of Clinical Neurosciences, Geneva University Hospitals
Abstract Preview: Purpose: Personalized prediction of vestibular schwannoma (VS) tumour growth is crucial for guiding patient management decisions toward observation versus intervention. This study proposes an automate...
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: Yankun Lang, Lei Ren, Dario B. Rodrigues
Affiliation: University of Maryland School of Medicine, Department of Radiation Oncology, University of Maryland School of Medicine
Abstract Preview: Purpose:
HTP of microwave (MW) phased-array systems determine MW antenna settings to maximize energy absorption (SAR in W/kg) in tumor. Conventional HTP algorithms calculate SAR based on electromag...
Authors: Yifei Hao, Wenxuan Li, Xiang Li, Tao Peng, Yulu Wu, Fang-Fang Yin, Yue Yuan, Lei Zhang, Yaogong Zhang
Affiliation: Duke University, School of Future Science and Engineering, Soochow University, Medical Physics Graduate Program, Duke Kunshan University
Abstract Preview: Purpose: Diffusion-based deep-learning frameworks have been recently used in MRI resolution enhancement, or super-resolution. Multi-contrast MRI share common anatomical structures while holding comple...
Authors: Jee Suk Chang, Hojin Kim, Jin Sung Kim, Jaehyun Seok
Affiliation: Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Department of Integrative Medicine
Abstract Preview: Purpose: This study aims to leverage 3D dose distribution data to develop a machine learning model capable of accurately predicting lymphedema occurrence in patients undergoing 3D conformal radiation ...
Authors: Liyuan Chen, Steve Jiang, Chenyang Shen
Affiliation: Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center
Abstract Preview: Purpose: Delays in radiation therapy (RT) initiation caused by conventional CT simulation processes can hinder timely treatment delivery and patient outcomes. This study proposes a Virtual Treatment S...
Authors: Kristen A. Duke, Samer Jabor, Neil A. Kirby, Parker New, Niko Papanikolaou, Arkajyoti Roy, Yuqing Xia
Affiliation: St. Mary's University, The University of Texas San Antonio, UT Health San Antonio
Abstract Preview: Purpose:
The Segment Anything Model (SAM) is a foundational box-prompt-based model for natural image segmentation. However, its applicability to zero-shot 3D medical image segmentation, particularl...
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...
Authors: Sahaja Acharya, Matthew Ladra, Junghoon Lee, Lina Mekki, Bohua Wan
Affiliation: Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Department of Biomedical Engineering, Johns Hopkins University, Department of Computer Science, Johns Hopkins University
Abstract Preview: Purpose: Cerebellar mutism syndrome (CMS) is the most frequently observed complication in children undergoing surgical resection of posterior fossa tumors. Previous work explored lesion to symptom map...
Authors: Xuelian Chen, John Ginn, Zhuhong Li, Kaizhong Shi, Chunhao Wang, Jianliang 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: Developing deep learning-based models for accurate automated breast cancer diagnosis from mammography presents significant challenges due to the small size and subtle nature of breast lesions...
Authors: Theodore Higgins Arsenault, Beatriz Guevara, Rojano Kashani, Raymond F. Muzic, Gisele Castro Pereira, Alex T. Price
Affiliation: University Hospitals Seidman Cancer Center, Case Western Reserve University Department of Biomedical Engineering
Abstract Preview: Purpose: Accurate dose prediction in radiotherapy is essential for treatment planning. This study evaluates four nnUnet-based models using the OpenKBP head and neck dataset: a baseline model (Model 1)...
Authors: Eric N Carver, Julia Marks
Affiliation: Brown University
Abstract Preview: Purpose: The clinical applicability of radiomic features is hindered by challenges in stability and reproducibility. To address this, researchers are establishing image and feature standardizations an...
Authors: Jingyun Chen, Yading Yuan
Affiliation: Columbia University Irving Medical Center, Department of Radiation Oncology
Abstract Preview: Purpose: To evaluate centralized and decentralized strategies for federated head and neck tumor segmentation on PET/CT.
Methods: We utilized training data from the HEad and neCK TumOR segmentation ...
Authors: Zhaoyang Fan, Eric Nguyen, Dan Ruan, Jiayu Xiao
Affiliation: Department of Radiation Oncology, University of California, Los Angeles, Department of Radiology, University of Southern California, University of Southern California
Abstract Preview: Purpose: MR vessel wall imaging (VWI) has been shown to be effective for evaluating intracranial atherosclerosis disease. However, VWI typically also requires an MR angiography (MRA) in the same imagi...
Authors: Derek Tang, Susu Yan
Affiliation: Massachusetts General Hospital
Abstract Preview: Purpose: To evaluate the performance of a multi-task automated-segmentation and synthetic CT generation model (sCT) and investigate its application in an adaptive proton therapy workflow.
Methods: ...
Authors: Hector Andrade-Loarca, Ines Butz, Chiara Gianoli, Prof. Gitta Kutyniok, Jianfei Li, Katia Parodi, Prof. Vincenzo Patera, Angelo Schiavi, Prof. Ozan Öktem
Affiliation: Sapienza University of Rome, Department of Mathematics, Royal Institute of Technology, School of Computation, Information and Technology, Technische Universitaet Muenchen, Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Department of Mathematics, Ludwig-Maximilians-Universität (LMU) München, Department of Medical Physics, Ludwig-Maximilians-Universität (LMU) München
Abstract Preview: Purpose: To explore and demonstrate the feasibility of accurate and fast prediction of the water equivalent thickness (WET) distribution of tissue traversed by a proton imaging pencil beam, aiming at ...
Authors: Zixu Guan, Takahiro Iwai, Takashi Mizowaki, Mitsuhiro Nakamura, Michio Yoshimura
Affiliation: Kyoto University, Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University
Abstract Preview: Purpose:
The goal of this study is to develop a fully automated treatment planning approach for VMAT in pancreatic cancer that can convert patient anatomy into LINAC machine parameters. In this wor...
Authors: Mostafa Cham, Matthias K Gobbert, Zhuoran Jiang, Sina Mossahebi, Ruth Obe, Stephen W. Peterson, Jerimy C. Polf, Lei Ren, Ehsan Shakeri, Vijay Raj Sharma
Affiliation: University of Maryland School of Medicine, UMBC, University of Maryland Baltimore County, University of Maryland, Baltimore County, Stanford University, University of Maryland, School of Medine, Department of Physics, University of Cape Town, M3D, Inc, Department of Infomation Systems, UMBC
Abstract Preview: Purpose: Compton camera (CC)-based prompt gamma imaging (PGI) offers real-time proton range verification. However, its limited-angle measurements cause severe distortions in PGI, affecting its clinica...
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 ...
Authors: Xiaoyu Duan, Xiang Li, Wenbo Wan, Lei Zhang, Yiwen Zhang
Affiliation: Duke University, Medical Physics Graduate Program, Duke Kunshan University
Abstract Preview: Purpose: Breast screening has been proved to reduce breast cancer mortality by early detection and treatment for patients. Mammography is the most common and widely used technique for breast cancer sc...
Authors: Maryellen L. Giger, Fahd Hatoum, Robert Tomek, Heather M. Whitney
Affiliation: The University of Chicago
Abstract Preview: Purpose: To assess the importance of applying stratified sampling across demographic attributes (including age, sex, race, and ethnicity) when constructing training and testing datasets for ML-based d...
Authors: Xinhui Duan, Roderick W. McColl, Mi-Ae Park, Liqiang Ren, Gary Xu, Kuan Zhang, Yue Zhang
Affiliation: UT Southwestern Medical Center, Department of Radiology, UT Southwestern Medical Center, Imaging Services, UT Southwestern Medical Center
Abstract Preview: Purpose:
Image-based deep-learning noise-reduction techniques have been developed for photon-counting CT (PCCT) to improve image quality with reduced radiation dose. The denoising strength is typic...
Authors: Abid Khan, Chad Klochko, Michael J Kovalchick, Hyeok Jun Lee, Hani Nasr, Krishnan Shyamkumar, Kundan S Thind
Affiliation: Henry Ford Radiology, Wayne State University, Henry Ford Health, HFHS
Abstract Preview: Purpose: Automated vascular segmentation in interventional angiography is challenged by contrast kinetics, vessel variations, and 2D projections, limiting the effectiveness of single-model approaches....
Authors: Gregory T. Armstrong, James E. Bates, Kristy K. Brock, Laurence Edward Court, Matt Ehrhardt, Danielle Friedman, Aashish C. Gupta, Donald Hancock, Rebecca M. Howell, Cindy Im, Tera S Jones, Choonsik Lee, Wendy Leisenring, Taylor Meyers, Lindsay Morton, Chaya Moskowitz, Joe Neglia, Vikki Nolan, Caleb O'Connor, Kevin C. Oeffinger, Constance A. Owens, Arnold C. Paulino, Chelsea C. Pinnix, Sander Roberti, Cecile Ronckers, Susan A. Smith, Kumar Srivastava, Lucie Turcotte
Affiliation: Department of Medicine, Duke University School of Medicine, Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, The University of Texas MD Anderson Cancer Center, Department of Oncology, St. Jude Children’s Research Hospital, Department of Pediatrics, Memorial Sloan Kettering Cancer Center, Division of Pediatric Epidemiology and Clinical Research, University of Minnesota, Division of Childhood Cancer Epidemiology, University Medicine at Johannes Gutenberg University Mainz, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Department of Pediatrics, University of Minnesota, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Department of Biostatistics, St. Jude Children’s Research Hospital, Clinical Research Division, Fred Hutchinson Cancer Center, Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology and Winship Cancer Institute, Emory University, The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences
Abstract Preview: Purpose: To (1) develop and validate a novel anatomically realistic pediatric/adolescent population-based breast model, (2) incorporate model into an age-scalable female reference phantom, and (3) dem...
Authors: Yuzhen Ding, Hongying Feng, Jason Michael Holmes, Baoxin Li, Wei Liu, Daniel Ma, Lisa McGee, Samir H. Patel, Jean Claude M. Rwigema, Sujay A. Vora
Affiliation: Arizona State University, Department of Radiation Oncology, Mayo Clinic, Mayo Clinic Arizona, Mayo Clinic
Abstract Preview: Purpose:
Intensity-modulated proton therapy (IMPT) is a preferred treatment modality for head and neck (H&N) cancer patients, offering precise tumor targeting while sparing surrounding organs at ri...
Authors: Jing Cai, Zhi Chen, Hong Ge, Yu-Hua Huang, Bing Li, Zihan Li, Ge Ren
Affiliation: Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital
Abstract Preview: Purpose: Algorithms based on subregional respiratory dynamics (SRD) capture spatiotemporal heterogeneity in the ventilation process, though rely on empirical modelings to map surrogate ventilation. Gi...
Authors: Lian Duan, Stephen F. Kry, Hunter S. Mehrens, Christine Peterson, Paige A. Taylor
Affiliation: The University of Texas MD Anderson Cancer Center, UT MD Anderson Cancer Center
Abstract Preview: Purpose: To develop predictive models for IROC SRS head phantom audits and to identify important factors influencing institutional performance.
Methods: The IROC SRS head phantom includes two TLDs ...
Authors: Jadon Buller, Zhuoran Jiang, Yankun Lang, Lei Ren, Leshan Sun, Liangzhong Xiang, Yifei Xu
Affiliation: University of Maryland School of Medicine, University of California, Irvine, University of California, Stanford University
Abstract Preview: Purpose: Electroacoustic tomography (EAT) and Protoacoustic (PA) imaging are novel modalities for treatment verification of electroporation and proton therapy. However, the limited acquisition angle i...
Authors: Nan Li, Yaoying Liu, Shouping Xu, Xinlei Xu, Gaolong Zhang
Affiliation: School of Physics, Beihang University, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, School of physics, Beihang University, Beihang University, Department of Radiation Oncology
Abstract Preview: Purpose:
CT simulation is essential for radiation therapy preparation but has limitations in distinguishing lesions. Contrast-enhanced CT (CECT) improves lesion detection and characterization, but ...
Authors: Shinichiro Mori, Isabella Pfeiffer, Chester R. Ramsey, Alexander Usynin
Affiliation: Thompson Proton Center, National Institutes for Quantum Science and Technology, Thompson Cancer Survival Center
Abstract Preview: Purpose: Four-dimensional CT imaging (4DCT) has become a standard tool for managing respiratory motion in radiation therapy. However, many treatment delivery systems and most diagnostic CT scanners la...
Authors: Claus Belka, Stefanie Corradini, George Dedes, Nikolaos Delopoulos, Christopher Kurz, Guillaume Landry, Ahmad Neishabouri, Domagoj Radonic, Adrian Thummerer, Niklas Wahl, Fan Xiao
Affiliation: Department of Radiation Oncology, LMU University Hospital, LMU Munich, Department of Medical Physics, LMU Munich, Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), National Center for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO)
Abstract Preview: Purpose: In MR-guided online adaptive radiotherapy, MRI lacks tissue attenuation information necessary for accurate dose calculations. Instead of using deep learning methods to generate synthetic CT i...
Authors: Sofia Beer, Menal Bhandari, Alec Block, Nader Darwish, Joseph Dingillo, Sebastien A. Gros, Hyejoo Kang, Andrew Keeler, Rajkumar Kettimuthu, Jason Patrick Luce, Ha Nguyen, John C. Roeske, George K. Thiruvathukal, Austin Yunker
Affiliation: Data Science and Learning Division, Argonne National Laboratory, Department of Radiation Oncology, Stritch School of Medicine, Loyola University Chicago, Stritch School of Medicine Loyola University Chicago, Cardinal Bernardin Cancer Center, Loyola University Chicago, Department of Computer Science, Loyola University of Chicago
Abstract Preview: Purpose: Artificial intelligence (AI) generated synthetic medical images are seeing increased use in radiology and radiation oncology. Physician observer studies are an ideal way to evaluate the usabi...
Authors: Mingli Chen, Xuejun Gu, Mahdieh Kazemimoghadam, Weiguo Lu, Qingying Wang
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: This study introduces a novel template-guided deep learning framework for primary gross tumor volume (GTVp) segmentation, addressing challenges posed by diverse tumor types and enabling a uni...
Authors: Yang Sheng, Qingrong Jackie Wu, Qiuwen Wu, Xin Wu, Dongrong Yang
Affiliation: Duke University Medical Center
Abstract Preview: Purpose:
Treatment planning for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) relies on inverse planning, an iterative and non-intuitive process of adjust...
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...
Authors: Ling Chen, Alexei V. Trofimov, Yi Wang, Dufan Wu
Affiliation: Massachusetts General Hospital, MGH
Abstract Preview: Purpose:
Selecting gaze angles of the eye is an important part of set-up of proton therapy for ocular tumors, ensuring that the treatment beam could irradiate the tumor while maximally sparing impo...
Authors: Ming Chao, Karyn A Goodman, Yang Lei, Tian Liu, Jing Wang, Jiahan Zhang
Affiliation: Icahn School of Medicine at Mount Sinai
Abstract Preview: Purpose: Real-time volumetric MRI is essential for motion management in MRI-guided radiotherapy (MRIgRT), yet acquiring high-quality 3D images remains challenging due to time constraints and motion ar...
Authors: Nan Li, Shouping Xu, Gaolong Zhang, Xuerong Zhang
Affiliation: Department of Radiation Oncology, HeBei YiZhou proton center, School of Physics, Beihang University, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
Abstract Preview: Purpose:
The 3D BRAVO sequence is an advanced magnetic resonance (MR) technique that allows for image reconstruction at any angle. It offers 1 mm gapless scanning and has a high signal-to-noise rat...
Authors: Pouya Azarbar, Nima Kasraie, Peyman Sheikhzadeh
Affiliation: UT Southwestern Medical Center, Shahid Beheshti University of Medical science, Imam Khomeini Hospital Complex,Tehran University of Medical Sciences
Abstract Preview: Purpose: Positron Emission Tomography (PET) is crucial for diagnosing and monitoring diseases due to its functional imaging capabilities. However, its high cost, significant radiation exposure, and li...
Authors: Enhui Chang, Yunfei Dong, Yifei Hao, Chengliang Jin, Shengsheng Lai, Yi Long, Mengni Wu, Yulu Wu, Ruimeng Yang, Zhenyu Yang, Yue Yuan, Lei Zhang, Wanli Zhang, Yaogong Zhang
Affiliation: Duke Kunshan University, Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Medical Physics Graduate Program, Duke Kunshan University
Abstract Preview: Purpose: Macrotrabecular-Massive Hepatocellular Carcinoma (MTM-HCC) is one type of liver cancer showed minimum image signature for accurate non-invasive diagnosis. This study aims to develop and evalu...
Authors: Manju Liu, Weiwei Sang, Yanyan Shi, Zhenyu Yang, Fang-Fang Yin, Chulong Zhang, Lihua Zhang, Rihui Zhang
Affiliation: Jiahui International Hospital, Jiahui International Hospital, Radiation Oncology, Duke Kunshan University, Medical Physics Graduate Program, Duke Kunshan University
Abstract Preview: Purpose: This study aims to transform cone-beam computed tomography (CBCT) images acquired from deep inspiration breath-hold (DIBH) breast cancer patients into high-fidelity synthetic CT (sCT) images....
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...
Authors: Mark Ashamalla, Renee Farrell, Jinkoo Kim, Kartik Mani, Xin Qian, Samuel Ryu, Yizhou Zhao
Affiliation: Stony Brook Medicine, Stony Brook University Hospital
Abstract Preview: Purpose: Adaptive planning is increasingly used in head and neck radiation therapy due to factors like tumor response or changes in patient anatomy. However, methods such as resimulation or offline re...
Authors: James M. Lamb, Dishane Chand Luximon, Jack Neylon, Rachel Petragallo, Moritz Ritter, Timothy Ritter
Affiliation: Department of Radiation Oncology, University of California, Los Angeles, ETH Zurich, VCU Health System, Department of Radiation Oncology, University of Colorado
Abstract Preview: Purpose: Anomalies in cone beam computed tomography (CBCT) radiotherapy image guidance can signal treatment deviations. Repetitive review of setup image registrations by humans is inefficient, prone t...
Authors: Hilary P Bagshaw, Mark K Buyyounouski, Serdar Charyyev, Xianjin Dai, PhD, Yu Gao, Thomas R. Niedermayr, Lei Xing
Affiliation: Department of Radiation Oncology, Stanford University
Abstract Preview: Purpose: Real-time transrectal ultrasound imaging is the gold standard for needle placement and treatment planning of real-time based-ultrasound-based high dose-rate (HDR) prostate brachytherapy. Cumu...
Authors: Marissa Brown, Geoffrey D. Clarke, Luke Norton
Affiliation: University of Texas Health Science Center at San Antonio
Abstract Preview: Purpose: To evaluate how different learning strategies affect convolutional neural network (CNN) estimates of the liver's intravoxel incoherent motion (IVIM) parameters.
Methods: A 3-stage U-Net wa...
Authors: Ibtisam Almajnooni, Elisabeth Weiss, Lulin Yuan
Affiliation: Virginia Commonwealth University
Abstract Preview: Purpose: We developed a deep learning neural network (DLNN) to predict the risk of radiation-induced esophagitis (RE) during lung cancer radiation therapy based on the spatial dose distribution, for t...
Authors: Minbin Chen, Xiaoyi Dai, Xiaoyu Duan, Chunhao Wang, Fan Xia, Zhenyu Yang, Fang-Fang Yin, Chulong Zhang, Rihui Zhang
Affiliation: Duke University, Duke Kunshan University, Medical Physics Graduate Program, Duke Kunshan University, The First People's Hospital of Kunshan
Abstract Preview: Purpose: Deep learning (DL)-based mammography diagnosis presents unique challenges, as accurate interpretation requires both global breast condition analysis and local lesion structural information. E...
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: Steve B. Jiang, Chien-Yi Liao, Dan Nguyen, Daniel Yang, Hengrui Zhao
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center
Abstract Preview: Purpose:
Post-operative radiotherapy for prostate cancer requires precise contouring of the clinical target volume (CTV) to account for microscopic disease that is invisible in the image. However, ...
Authors: Ryan Alden, Tithi Biswas, Kaushik Halder, Felix Maria-Joseph, Michael Mix, Rihan Podder, Tarun Kanti Podder
Affiliation: SUNY Upstate Medical University, IIT-Roorkee, University of Florida
Abstract Preview: Purpose: Early-stage NSCLC patients undergoing SBRT often die due to intercurrent illnesses. However, prediction of overall survival (OS) remains crucial due to the risk of disease recurrence. This st...
Authors: Xuezhen Feng, Li-Sheng Geng, Haoze Li, Xi Liu, Tianyu Xiong, Ruijie Yang
Affiliation: Department of Health Technology and Informatics, The Hong Kong Polytechnic University, School of Physics, Beihang University, School of Nuclear Science and Technology, University of South China, Department of Radiation Oncology, Peking University Third Hospital
Abstract Preview: Purpose: This study aimed to develop a deep learning-based algorithm for automatically delineate gross tumor volume (GTV) for lung cancer patients, alleviating the workload of radiologists and improvi...
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...
Authors: Ali Ajdari, Thomas R. Bortfeld, Christopher Bridge, Gregory Buti, Marcela Giovenco, Fredrik Lofman, Gregory C. Sharp, Helen A Shih, Tugba Yilmaz
Affiliation: Massachusetts General Hospital, RaySearch Laboratories, Department Of Radiation Oncology, Massachusetts General Hospital (MGH), Massachusetts General Hospital & Harvard Medical School, Massachusetts General Hospital and Harvard Medical School
Abstract Preview: Purpose: Defining radiation target volumes with accurate integration of the neuroanatomy is one of the major difficulties in designing glioma treatments. We developed a deep learning network for norma...
Authors: Akihiro Haga, Ren Iwasaki, Kenya Kusunose, Makoto Miyake, Kenji Moriuchi, Yasuharu Takeda, Hidekazu Tanaka, Hirotsugu Yamada
Affiliation: Department of Cardiovascular Medicine, Nephrology, and Neurology Graduate School of Medicine, University of the Ryukyus, Graduate School of Biomedical Sciences, Tokushima University, Tokushima university, Division of Cardiovascular Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, Department of Cardiology, Tenri Hospital, Department of Cardiovascular Medicine, Osaka University Graduate School of Medicine, Division of Heart Failure, Department of Heart Failure and Transplant, National Cerebral and Cardiovascular Center
Abstract Preview: Purpose: Device dependency is a significant challenge in medical AI, potentially limiting generalization performance. This study aimed to develop a robust deep learning model for predicting left ventr...
Authors: Amanda J. Deisher, Andrew YK Foong, Witold Matysiak, Jing Qian, Xueyan Tang, Erik J. Tryggestad, Mi Zhou
Affiliation: Mayo Clinic
Abstract Preview: Purpose: Phase gating is commonly employed to mitigate the impact of tumor motion in radiotherapy. Due to the machine-specific time delay between triggering and radiation delivery, the triggering sign...
Authors: Justus Adamson, John Ginn, Yongbok Kim, Ke Lu, Trey Mullikin, Xiwen Shu, Chunhao Wang, Zhenyu Yang, Jingtong Zhao
Affiliation: Duke University, Duke Kunshan University
Abstract Preview: Purpose:
To develop a knowledge-based deep model for synthetic CT (sCT) generation from a single MR volume in frameless radiosurgery (SRS), eliminating the need for CT simulation prior to the SRS d...
Authors: Mojtaba Behzadipour, Suman Gautam, Tianjun Ma, Ikchit Singh Sangha, Bongyong Song, William Song, Kumari Sunidhi
Affiliation: UC San Diego, Virginia Commonwealth University
Abstract Preview: Purpose: This study aims to develop a knowledge-based voxel-wise dose prediction system using a convolutional neural network (CNN) for high-dose-rate (HDR) prostate brachytherapy and to evaluate its p...
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...
Authors: Yan Dai, Jie Deng, Christopher Kabat, Weiguo Lu, Ying Zhang, Hengrui Zhao
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center
Abstract Preview: Purpose:
MRI-guided adaptive radiotherapy (MRgART) using MR-LINAC systems offers significant advantages for liver cancer, enabling superior tumor delineation and online plan adaptation. However, ma...
Authors: Sixue Dong, Chaosu Hu, Weigang Hu, Xiaomin Ou, Jiazhou Wang, Zhen Zhang
Affiliation: Fudan University Shanghai Cancer Center
Abstract Preview: Purpose:
This study aimed to predict the PFS of the patients who were diagnosed with hypopharyngeal cancer and received postoperative chemoradiotherapy by using multi-omics which integrating clinic...
Authors: Ming Chao, Thomas Chum, Tenzin Kunkyab, Yang Lei, Tian Liu, Richard G Stock, Hasan Wazir, Junyi Xia, Jiahan Zhang
Affiliation: Icahn School of Medicine at Mount Sinai
Abstract Preview: Purpose:
This study aims to develop effective strategies for multi-organ segmentation of pelvic cone-beam computed tomography (CBCT) images based on transformer models to facilitate adaptive radiat...
Authors: Steve B. Jiang, Mu-Han Lin, Yu-Chen Lin, Austen Matthew Maniscalco, Dan Nguyen, David Sher, Xinran Zhong
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Department of Radiation Oncology, UT Southwestern Medical Center, UT Dallas
Abstract Preview: Purpose:
Sequential boost radiotherapy (RT) poses a challenge in allocating dose across multiple plans while protecting organs at risk (OARs). Clinicians must decide whether OAR sparing should occu...
Authors: Kaino Haipinge, Festus Laban, Wilfred Midzi, Boas J Nikanor, Monika Sakaria, Vera Uushona
Affiliation: Ministry of Health and Social Services, MATCON BASED SOLUTION, Ministry of Health And Social Services, School of Medicine, University of Namibia
Abstract Preview: Purpose: Medical Physics in namibia is emerging as a multidisciplinary career, with efforts to raise awareness about the role of medical physics in radiation safety, treatment and protection. The prof...
Authors: Daniel O Connor, Mary Feng, Hui Lin, Hengjie Liu, Xin Miao, Michael Ohliger, Jess E. Scholey, Ke Sheng, DI Xu, Wensha Yang, Yang Yang
Affiliation: UCSF, University of California, Los Angeles, Department of Radiation Oncology, University of California San Francisco, Department of Radiation Oncology, University of California, San Francisco, Department of Radiation Oncology, University of California at San Francisco, University of San Francisco, Department of Radiology, University of California, San Francisco, University of California San Francisco, Siemens Medical Solutions USA Inc.
Abstract Preview: Purpose: The scanning time for a fully sampled MRI is lengthy. Compressed sensing (CS) has been developed to minimize image artifacts in accelerated scans, but the required iterative reconstruction is...
Authors: Md Mainul Abrar, Yujie Chi
Affiliation: University of Texas at Arlington, Department of Physics, University of Texas at Arlington
Abstract Preview: Purpose: Healthcare 5.0, proposed in 2021, includes interpretable healthcare analysis as a core component. Achieving this requires the application of explainable artificial intelligence (XAI) to overc...
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: Wenchao Cao, Yingxuan Chen, Haisong Liu, Richard A. Popple, Wenyin Shi, Rodney J. Sullivan, Wentao Wang, Lydia J. Wilson, Zhenghao Xiao
Affiliation: Thomas Jefferson University, The University of Alabama at Birmingham, University of Alabama at Birmingham
Abstract Preview: Purpose: Objectively evaluating brain sparing as a plan-quality indicator for patients receiving stereotactic radiosurgery (SRS) to multiple metastases (multi-met) is complicated by variability in tar...
Authors: Wookjin Choi, Michael Dichmann, Adam Dicker, Nilanjan Haldar, Yingcui Jia, Nicole L Simone, Eugene Storozynsky, Yevgeniy Vinogradskiy, Maria Werner-Wasik
Affiliation: Thomas Jefferson University, 9Department of Radiation Oncology, Thomas Jefferson University
Abstract Preview: Purpose: Cardiotoxicity remains a significant limitation for lung cancer patients treated with radiotherapy. Pre-radiotherapy cardiac conditions increase the probability of patients developing cardiot...
Authors: Blessing Akinro, Soumyanil Banerjee, Ming Dong, Carri K. Glide-Hurst, Prashant Nagpal, Chase Ruff, Nicholas R. Summerfield, Timothy P. Szczykutowicz
Affiliation: Departments of Human Oncology and Medical Physics, University of Wisconsin-Madison, Departments of Radiology and Medical Physics, University Wisconsin-Madison, Department of Radiology, University of Wisconsin-Madison, Department of Computer Science, Wayne State University, Department of Human Oncology
Abstract Preview: Purpose: Radiation dose to coronary arteries (CAs) during thoracic radiotherapy (RT) is linked to cardiotoxicity. However, precise CA delineation for avoidance is limited by image quality and CA compl...
Authors: Hao Chen, Kai Ding, Xiaoyu Hu, Xun Jia, Heng Li, Devin Miles
Affiliation: Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Johns Hopkins University
Abstract Preview: Purpose: Accurately delivering radiation dose is critical in intensity-modulated proton therapy (IMPT), where intrafraction motion management plays a pivotal role. Our proton therapy system equipped x...
Authors: Yifei Hao, Chengliang Jin, Wenxuan Li, Bing Luo, Tao Peng, Yulu Wu, Fang-Fang Yin, Yue Yuan, Lei Zhang, Ruojun Zhou
Affiliation: School of Future Science and Engineering, Soochow University, Electrical and Computer Engineering Graduate Program, Duke Kunshan University, Medical Physics Graduate Program, Duke Kunshan University
Abstract Preview: Purpose: Federated learning is a patient privacy-protecting technique that has recently been applied in the medical field. This study aims to evaluate the performance of several deep learning networks...
Authors: Ricardo Garcia Santiago, Narges Miri, Daryl P. Nazareth, Ankit Pant, Mukund Seshadri
Affiliation: Roswell Park Comprehensive Cancer Center
Abstract Preview: Purpose: To develop a transformer-based deep learning network framework for predicting VMAT dose distributions. This can provide fast and efficient calculations with accuracies potentially comparable ...
Authors: Osama R. Mawlawi, Yiran Sun
Affiliation: RICE University, UT MD Anderson Cancer Center
Abstract Preview: Purpose: Conventional PET reconstruction methods often produce noisy images with artifacts due to data/model mismatches and inconsistencies. Recently, deep learning-based conditional denoising diffusi...
Authors: Hilary P Bagshaw, Mark K Buyyounouski, Xianjin Dai, PhD, Praveenbalaji Rajendran, Lei Xing, Yong Yang
Affiliation: Department of Radiation Oncology, Stanford University, Massachusetts General Hospital, Harvard Medical School
Abstract Preview: Purpose: Artificial intelligence (AI)-driven methods have transformed dose prediction, streamlining the automation of radiotherapy treatment planning. However, traditional approaches depend exclusivel...
Authors: John Ginn, Zhuoyun Huang, Yongbok Kim, Ke Lu, Chunhao Wang, Yibo Xie, Zhenyu Yang, Jingtong Zhao
Affiliation: Duke University, Duke Kunshan University
Abstract Preview: Purpose: This study aims to develop and validate a machine learning model for predicting V60%, a critical dosimetric metric in LINAC-based Single-Isocenter-Multiple-Targets (SIMT) stereotactic radiosu...
Authors: Leslie Harrell, Sanjay Maraboyina, Romy Megahed, Maida Ranjbar, Xenia Ray, Pouya Sabouri
Affiliation: Department of Radiation Oncology, University of Arkansas for Medical Sciences (UAMS), University of California San Diego
Abstract Preview: Purpose: Real-time adaptive radiation therapy (ART) dynamically modifies patients’ treatment plan during delivery to account for anatomical and physiological variations. Addressing ART planning time v...
Authors: Abdullah Hidayat, Wazir Muhammad
Affiliation: Florida Atlantic University
Abstract Preview: Purpose: This study aims to predict Head and Neck cancer using an artificial neural network (ANN) through readily available basic health data. The goal is to uncover hidden patterns and predictors in ...
Authors: Steve B. Jiang, Dan Nguyen, Chenyang Shen, Fan-Chi F. Su, Jiacheng Xie, Shunyu Yan, Daniel Yang, Ying Zhang, You Zhang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, The University of Texas at Dallas
Abstract Preview: Purpose: Fast dose verification is essential for the safety and efficiency of MR-guided adaptive radiotherapy (ART) as patients anxiously waiting on the treatment couch. Conventional tools often requi...
Authors: Yankui Chang, Shijun Li, Xi Pei, Ripeng Wang, Xuanhe Wang, X. George Xu, Qing Zhang, Jingfang Zhao
Affiliation: University of Science and Technology of China, Shanghai proton and heavy ion center, School of Nuclear Science and Technology, University of Science and Technology of China, Anhui Wisdom Technology Co., Ltd.
Abstract Preview: Purpose:
This paper describes disruptive methods using both GPU-based MC simulation and deep-learning (DL)-based MC denoising algorithms, as well as clinical tests involving more than 560 patient p...
Authors: Daniel A. Alexander, Jonathan Baron, Brook Kennedy Byrd, William Ross Green, Bolin Li, Rafe A. McBeth, Abigail Pepin, Steven Philbrook
Affiliation: Department of Radiation Oncology and Applied Sciences, Department of Radiation Oncology, University of Pennsylvania, Thayer School of Engineering, University of Pennsylvania
Abstract Preview: Purpose: As accelerated partial breast irradiation (APBI) gains traction, the prospect of a rapid sim-to-completion of treatment workflow is an attractive option for patients. While OAR autocontouring...
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...
Authors: Rituparna Basak, Maede Boroji, Renee F Cattell, Vahid Danesh, Imin Kao, Kartik Mani, Xin Qian, Samuel Ryu, Tiezhi Zhang
Affiliation: Stony Brook Medicine, Stony Brook University, Washington University in St. Louis, Stony Brook University Hospital
Abstract Preview: Purpose: Fundamental qualitative characteristics physicians use to differentiate skin lesion subtypes include asymmetry, border irregularity, and color. Radiomic features have potential to quantify th...
Authors: Hoyeon Lee
Affiliation: University of Hong Kong
Abstract Preview: Purpose: Deep-learning approaches are widely investigated for Cone-Beam CT (CBCT) scatter correction to improve the quality of the linear-accelerator mounted CBCT. This study aims to optimize the deep...
Authors: Guangjun Li, Ying Song, Huanan Tang, Tianxiong Wu, Qiuyi Ye, Wei Zhang
Affiliation: West China Second Hospital of Sichuan University, United Imaging Healthcare, West China Hospital of Sichuan University
Abstract Preview: Purpose:
To propose a general low-dose reconstruction model for ultra-sparse-view cone-beam CT (CBCT) and evaluate its clinical application in improving image quality and reducing radiation dose fo...
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...
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...
Authors: Jie Hu, Nan Li, Chuanbin Xie, Shouping Xu, Xinlei Xu, Gaolong Zhang, Zhilei Zhang
Affiliation: School of Physics, Beihang University, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Department of Radiation Oncology, the First Medical Center of the People's Liberation Army General Hospital, National Cancer Center/ National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, Peopleʼs Republic of China, Department of Radiation Oncology, School of Physics, Beihang University, Beijing, 102206, Peopleʼs Republic of China
Abstract Preview: Purpose: This study aims to synthesize CT images for MRI-only radiation therapy using a deep learning approach that integrates information from the T1- and T2-weighted MRI sequence.
Methods: 97 hea...