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Results for "adaptive approach": 47 found

A Combination of Radiomics and Dosiomics for Gross Tumor Volume Regression in Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy (PULSAR)

Authors: Hao Peng, Yajun Yu

Affiliation: 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: Personalized ultra-fractionated stereotactic adaptive radiotherapy (PULSAR) is a novel ablative radiation dosing scheme developed by our institution. This study aims to establish a regression...

A Deep Learning Approach to the Prediction of Gamma Passing Rates in VMAT Radiotherapy Plans for Adaptive Treatment.

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 ...

A Deep Learning-Based Approach for Rapid Prediction of IMRT/VMAT Patient-Specific Quality Assurance for Online Adaptive Plans Generated with a 0.35T MR-Linac

Authors: Suman Gautam, Tianjun Ma, William Song

Affiliation: Virginia Commonwealth University

Abstract Preview: Purpose: We propose an artificial intelligence (AI)-based method to rapidly predict the patient-specific quality assurance (PSQA) results for magnetic resonance (MR)-guided online adaptive radiation th...

A Dynamic Reconstruction and Motion Estimation Framework for Cardiorespiratory Motion-Resolved Real-Time Volumetric MR Imaging (DREME-MR)

Authors: Jie Deng, Xiaoxue Qian, Hua-Chieh Shao, You Zhang

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

Abstract Preview: Purpose: Based on a 3D pre-treatment MRI scan, we developed DREME-MR to jointly reconstruct the reference patient anatomy and a data-driven, patient-specific cardiorespiratory motion model. Via a moti...

A Method to Reduce Workload in Adaptive Radiotherapy

Authors: Ramesh Boggula, Lincoln Houghton

Affiliation: Karmanos Cancer Institute, Wayne State University

Abstract Preview: Purpose: To evaluate an approach that selectively applies adaptive re-planning only when needed to reduce clinical workload while maintaining treatment quality. Daily adaptive radiotherapy (ART) has t...

A Novel Non-Measured and DVH-Based IMRT QA Framework with Machine Learning for Instant Classification of Susceptible Lung SBRT VMAT Plans

Authors: Chuan He, Anh H. Le, Iris Z. Wang

Affiliation: Roswell Park Comprehensive Cancer Center, Cedars-Sinai

Abstract Preview: Purpose: To develop a non-measured and DVH-based (NMDB) IMRT QA framework integrating machine learning (ML) to classify lung SBRT VMAT plans prone to delivery errors
Methods: 560 Eclipse AcurosXB l...

A Quantitative Analysis of Hypersight CBCT Image Quality Using a Phantom-Based Approach Under Different Scatter Conditions

Authors: Denisa R. Goia, M. Saiful Huq, Ronald John Lalonde, Fang Li, Noor Mail, Joseph Shields, Christopher Tyerech

Affiliation: UPMC Hillman Cancer Center, UPMC Hillman Cancer Center and University of Pittsburgh School of Medicine, Department of Radiation Oncology, University of Pennsylvania, UPMC

Abstract Preview: Purpose: HyperSight is a new platform for image-guided radiation therapy, offering advanced reconstruction algorithms, a large field-of-view, and rapid acquisition times. To validate the performance o...

A Quantitative Metric for Evaluating Treatment Plan Robustness in Intensity Modulated Proton Therapy

Authors: Grant Evans, Maxwell Arthur Kassel, Charles Shang, Stephen Shang

Affiliation: South Florida Proton Therapy Institute, SFPRF

Abstract Preview: Purpose: Proton pencil beam scanning therapy is particularly sensitive to field translational shifts and beam range variations, which can degradation of dose distribution and compromise the treatment....

Acute Workforce Shortages and Potential Solutions through Expanded Residency Training Infrastructure

Authors: Hania A. Al-Hallaq, Abby E. Besemer, Jay W. Burmeister, Jessica Fagerstrom, Kristi Rae Gayle Hendrickson, Neil A. Kirby, Christine M. Swanson, Christopher Watchman, Brian D Wichman

Affiliation: University of Wisconsin-Madison Department of Medical Physics, University of Washington, University of Louisville Brown Cancer Center, Banner, Karmanos Cancer Center, Gershenson ROC, Wayne State University School of Medicine, US Oncology, UT Health San Antonio, Department of Radiation Oncology and Winship Cancer Institute, Emory University

Abstract Preview: Purpose: To address the acute workforce shortage in medical physics by presenting a novel residency expansion model leveraging existing CAMPEP-accredited infrastructure and partnerships with underserv...

Advancing Ionizing Radiation Acoustic Imaging: A Deep Learning Approach for Denoising and Quantitative Reconstruction

Authors: Kyle Cuneo, Issam M. El Naqa, Dale W. Litzenberg, Yiming Liu, Xueding Wang, Lise Wei, Wei Zhang, Jiaren Zou

Affiliation: University of Michigan, H. Lee Moffitt Cancer Center

Abstract Preview: Purpose: To quantitatively map 3D dose deposition during radiotherapy, empowering real-time adaptive radiation treatment.

Methods: The research features reconstructing dose deposition from acou...

An Adaptive Radiotherapy Approach Sparing Preirradiated Critical Structures

Authors: Mohamed Bahaaeldin Mohamed Afifi, Lili Chen, Xiaoming Chen, Ahmed A. Eldib, Chang Ming Charlie Ma, Robert A. Price

Affiliation: Fox Chase Cancer Center, Radiological Sciences and Medical Imaging Department, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University.

Abstract Preview: Purpose: Reirradiation of recurrent cancer or a newly developed lesion in a proximal location poses a challenge for radiation treatments. This is occasionally encountered in many modern clinics and ef...

Assessment of Deep Learning Models for 3D Dose Prediction in Prostate Cancer SIB-IMRT Using MR-Linac

Authors: Hao-Wen Cheng, Jonathan G. Li, Chihray Liu, Wen-Chih Tseng, Guanghua Yan

Affiliation: University of Florida

Abstract Preview: Purpose: This study develops and evaluates deep learning (DL) models for predicting 3D dose distributions in simultaneous integrated boost (SIB) prostate cancer treatment using the Elekta Unity MR-Lin...

CBCT-Based Synthetic CT Imaging for Proton and Photon Dose Monitoring and Adaption in Supine Breast Radiotherapy

Authors: Mark E Artz, Julie Bradley, Hardev Singh Grewal, Perry B. Johnson, Christina Klassen, Raymond Mailhot Vega, Nancy Mendenhall, Jiyeon Park, Emma V. Viviers, Yawei Zhang

Affiliation: UF Health Proton Therapy Institute, University of Florida

Abstract Preview: Purpose: Verification CTs (VFCT) are used in radiotherapy to assess patient dose during treatment. However, they are time-consuming and contribute additional radiation exposure to the patient. This st...

Compressed Sensing Enhanced Radiomic Feature Selection for Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy (PULSAR)

Authors: Hao Peng, Yajun Yu

Affiliation: 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: Personalized ultra-fractionated stereotactic adaptive radiotherapy (PULSAR) is a new treatment paradigm pioneered by our institution. But the early decision-making process in PULSAR is challe...

Cross-Slice Attention for Unsupervised 3D Pelvic CBCT to CT Translation

Authors: Xu Chen, Jun Lian, Yunkui Pang, Pew-Thian Yap

Affiliation: University of North Carolina at Chapel Hill, Huaqiao University

Abstract Preview: Purpose: Unsupervised CBCT-to-CT translation in the pelvic region is essential for accurate radiotherapy delivery and adaptive image-guided interventions. However, current models for cross-modality tr...

Dose Reconstruction from Prompt-Gamma Imaging Towards Real-Time Adaptive Proton Therapy

Authors: Thomas R. Bortfeld, Prof. Elisabetta De Bernardi, Tianxue Du, Beatrice Foglia, Chiara Gianoli, Takamitsu Masuda, Katia Parodi, Marco Pinto, Boon-Keng Kevin Teo, Yunhe Xie

Affiliation: Department Of Radiation Oncology, Massachusetts General Hospital (MGH), School of Medicine and Surgery, University of Milano-Bicocca, Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Department of Medical Physics, Ludwig-Maximilians-Universität (LMU) München, National Institutes for Quantum Science and Technology (QST), University of Pennsylvania, Department of Experimental Physics - Medical Physics, Ludwig-Maximilians-Universität (LMU) München

Abstract Preview: Purpose: The full potential offered by protons in clinical practice is limited by range uncertainties. One possibility for monitoring is through secondary prompt gammas (PG). PG emission along the pen...

Dosimetric Impact of Adaptive Radiotherapy with Ethos for Prostate Cancer: Localized Analysis of Bladder and Rectum across Planned, Non-Adaptive Accumulated, and Adapted Treatments

Authors: Huisi Ai, Scott Glaser, Yi Lao, Percy Lee, Sara N. Lim, An Liu, Bo Liu, Borna Maraghechi, Kun Qing, Chengyu Shi, William T. Watkins, Terence Williams, Qiuyun Xu, Jiahua Zhu

Affiliation: WashU Medicine, Graduate Program in Bioengineering, University of California San Francisco-UC Berkeley, City of Hope Orange County, Department of Radiation Oncology, City of Hope National Medical Center, Department of Radiation Oncology, City of Hope Orange County, Department of Radiation Oncology, City of Hope Medical Center

Abstract Preview: Purpose: To employ a novel surface dose mapping approach for localized assessment of the dosimetric impact of Ethos adaptive radiotherapy (ART) for prostate cancer (PC).
Methods: This study include...

Dual-Domain Neural Network Cone-Beam CT Correction for Online Adaptive Proton Therapy

Authors: Daniel H. Bushe, Arthur Lalonde, Hoyeon Lee, Harald Paganetti, Brian Winey

Affiliation: Universite de Montreal, Massachusetts General Hospital, Massachusetts General Hospital and Harvard Medical School, University of Hong Kong

Abstract Preview: Purpose: Improving the precision and fidelity of daily volumetric imaging is essential for enabling adaptive proton therapy (APT). While cone-beam CT (CBCT) provides daily volumetric imaging, their ut...

Efficient FAST-Forward Planning Strategy for X-Ray Based Online Adaptive Radiotherapy

Authors: Prasanna Alluri, Mona Arbab, Xingzhe Li, Chang-Shiun Lin, Mu-han Lin, David D.M. Parsons, Asal Rahimi, Justin D. Visak, Narine Wandrey

Affiliation: UT Southwestern Medical Center, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX

Abstract Preview: Purpose: Intelligence-Optimization-Engine (IOE) v1.0 relied heavily on planner expertise and patient-specific IMRT beam arrangements, requiring frequent revisions. While VMAT workflows offered potenti...

Enhancing Adaptive Radiotherapy Segmentation with a 3D Unet Framework and Prior Fraction Information

Authors: Jennifer L. Dolan, Chengyin Li, Parag Parikh, Doris N. Rusu, Kundan S Thind

Affiliation: Henry Ford Health, Cedars-Sinai Medical Center

Abstract Preview: Purpose: The time and resource demands of online Adaptive Radiation Therapy (ART) can limit its widespread clinical adoption and potentially impact patient throughput. To address this, we developed a ...

Enhancing Synthetic Pelvic CT Images from CBCT Using Vision Transformer with Adaptive Fourier Neural Operators

Authors: Rashmi Bhaskara, Oluwaseyi Oderinde

Affiliation: Purdue University

Abstract Preview: Purpose: This study proposes a novel approach to overcoming CBCT image quality limitations by developing an improved synthetic CT (sCT) generation method based on a CycleGAN architecture using Vision ...

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...

From Noisy Signals to Accurate Maps: Transforming Look-Locker MRI with an Intelligent T₁ Estimation

Authors: Prabhu C. Acharya, Hassan Bagher-Ebadian, Stephen L. Brown, James R. Ewing, Mohammad M. Ghassemi, Benjamin Movsas, Farzan Siddiqui, Kundan S Thind

Affiliation: Michigan State University, Oakland University, Henry Ford Health

Abstract Preview: Purpose: Accurate T1 quantification using T One by Multiple Read Out Pulse (TOMROP) sequences is essential for physiological assessments in dynamic-contrast-enhanced (DCE) MRI and T1 mapping studies. ...

Generalizable 7T T1 Map Synthesis from 1.5T and 3T T1W MRI for High-Resolution MRI-Guided Radiation Therapy

Authors: Zachary Buchwald, Chih-Wei Chang, Zach Eidex, Hui Mao, Richard L.J. Qiu, Justin R. Roper, Mojtaba Safari, Hui-Kuo Shu, Xiaofeng Yang, David Yu

Affiliation: Emory University and Winship Cancer Institute, Emory University, Department of Radiation Oncology and Winship Cancer Institute, Emory University, Emory University School of Medicine

Abstract Preview: Purpose: MRI-guided radiation therapy (MRgRT) benefits significantly from enhanced soft-tissue contrast and spatial resolution, which aid in accurately delineating tumors and organs at risk. Although ...

Gradient-Based Radiomics for Outcome Prediction and Decision-Making in Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy (PULSAR): A Preliminary Study

Authors: Michael Dohopolski, Jiaqi Liu, Hao Peng, Robert Timmerman, Zabi Wardak, Haozhao Zhang

Affiliation: 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:
This study introduces a gradient-based radiomics framework to enhance outcome prediction in Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy (PULSAR) for brain metastases...

Hypersight® Offline Adaptive Workflow

Authors: Doris Dimitriadis/Dimitriadou, Denisa R. Goia, M. Saiful Huq, Ronald John Lalonde, Noor Mail, Adam Olson, Tyler Wilhite

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

Abstract Preview: Purpose: The goal of the HyperSight® offline adaptive-workflow is to create a methodical approach to adaptive-radiotherapy (ART) that considers modifications in patient setup and anatomy. Through effi...

Image-Guided Adaptive Proton Therapy for Head and Neck Cancer Using a Novel Gantry-Less System

Authors: Philip Blumenfeld, Jon Feldman, Yair Hillman, Michael Marash, Aron Popovtzer, Alexander Pryanichnikov, Shimshon Winograd, Marc Wygoda, Vered Zivan

Affiliation: Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), P-Cure Ltd./Inc., Sharett Institute of Oncology, Hadassah Medical Center, Hebrew University of Jerusalem

Abstract Preview: Purpose:
Image-guided adaptive proton therapy (IGAPT) allows tailored dose adjustments to account for anatomical and physiological changes during treatment. Recent efforts have developed a cost-eff...

Improving Segmentation Precision in Prostate Cancer Adaptive Radiotherapy with the Intentional Deep Overfit Learning (IDOL) Approach

Authors: Seungryong Cho, Donghyeok Choi, Joonil Hwang, Byung-Hee Kang, Jin Sung Kim, Eungman Lee, Younghee Park

Affiliation: Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, KAIST, Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Ewha Womans University of Medicine

Abstract Preview: Purpose: Radiation therapy (RT) is critical for cancer treatment, but changes in tumor size and shape during therapy challenge precise dose delivery. Adaptive radiation therapy (ART) addresses these v...

Innovative Biological Adaptive Radiotherapy (BART) Approach for Head-and-Neck Cancer Treatment Interruptions

Authors: Nobuki Imano, Daisuke Kawahara, Akito S Koganezawa, Yuji Murakami, Ikuno Nishibuchi, Takuya Wada

Affiliation: Department of Information and Electronic Engineering, Faculty of Science and Engineering, Teikyo University, Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University

Abstract Preview: Purpose: Adaptive radiotherapy (ART) compensates for treatment plans based on anatomical changes while not considering biological effects such as interruptions during treatment. This study aims to dev...

Inter-Patient Adaptive Radiotherapy (IPART): A CT Simulation and Planning Free Approach Enabling Immediate Treatment Access for Patients.

Authors: Bin Cai, Andrew R. Godley, Brian A. Hrycushko, Heejung Kim, Mu-han Lin, David D.M. Parsons, Justin D. Visak, Da Wang, Tingliang Zhuang

Affiliation: Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, UT Southwestern Medical Center, University of Texas Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Department of Radiation Oncology, UT Southwestern Medical Center

Abstract Preview: Purpose: The standard radiation therapy workflow requires CT-simulation and planning, whether for initial treatments or re-planning due to significant anatomical changes. IPART instead uses one patien...

Knowledge-Based Online Adaptive Proton Stereotactic Ablative Radiotherapy (SABR) for Localized Prostate Cancer Using Gaussian Process Regression

Authors: Hania A. Al-Hallaq, Duncan Henry Bohannon, Chih-Wei Chang, Anees H. Dhabaan, Vishal Dhere, H Scott McGinnis, Pretesh Patel, Sagar Patel, Keyur Shah, Xiaofeng Yang, Jun Zhou

Affiliation: Emory University, Department of Radiation Oncology and Winship Cancer Institute, Emory University

Abstract Preview: Purpose: Two-fraction proton SABR is an attractive alternative to brachytherapy for localized prostate cancer. However, potential interfractional anatomical changes necessitate online adaptation, espe...

Leveraging Codex-Based Spatial Profiling of the Tumor Microenvironment in Concurrent Radiation Therapy and Immunotherapy

Authors: Todd A Aguilera, Bassel Dawod, Sebastian Diegeler, Eslam Elghonaimy, Purva Gopal, Jiaqi Liu, Hao Peng, Arely Perez Rodriguez, Nina N. Sanford, Robert Timmerman, Megan B Wachsmann, Haozhao Zhang

Affiliation: 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

Abstract Preview: Purpose: This study pioneers the integration of CODEX (co-detection by indexing)-based spatial profiling and advanced computational techniques to investigate the tumor immune microenvironment (TIME) i...

Liver Tumor Auto-Contouring Using Recurrent Neural Networks on MRI-Linac for Adaptive Radiation Therapy

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...

MRI Radiomics-Based Machine Learning Model for Predicting BNCT Treatment Response in Glioblastoma

Authors: Huang Chi-Shiuan, Wu Chih-Chun, Hui-Yu Cathy Tsai, Chen Yan-Han, Chen Yi-Wei, Pan Yi-Ying

Affiliation: Institute of Nuclear Engineering and Science, National Tsing Hua University, Taipei Veterans General Hospital, Tri-Service General Hospital

Abstract Preview: Purpose:
This study aims to develop and validate a machine learning (ML) model based on MRI-derived radiomic features to predict progressive disease (PD) in glioblastoma (GBM) patients four months ...

Patient-Specific Deep Reinforcement Learning Framework for Automatic Replanning in Proton Therapy for Head-and-Neck Cancer

Authors: Malvern Madondo, Mark McDonald, Zhen Tian, Christopher Valdes, Ralph Weichselbaum, Xiaofeng Yang, David Yu, Jun Zhou

Affiliation: Department of Radiation & Cellular Oncology, University of Chicago, University of Chicago, Emory University, Department of Radiology, University of Chicago, Department of Radiation Oncology and Winship Cancer Institute, Emory University

Abstract Preview: Purpose: Head-and-neck (HN) cancer patients often experience significant anatomical changes during treatment course. Proton therapy, particularly intensity-modulated proton therapy (IMPT), is sensitiv...

Patient-Specific Treatment Plan Optimization through Intentional Deep Overfit Learning As a Warm Start for Longitudinal Adaptive Radiotherapy

Authors: Wouter Crijns, Frederik Maes, Loes Vandenbroucke, Liesbeth Vandewinckele

Affiliation: Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven; Department of Radiation Oncology, UZ Leuven, Department ESAT/PSI, KU Leuven; Medical Imaging Research Center, UZ Leuven, Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven

Abstract Preview: Purpose: To explore intentional deep overfit learning (IDOL) to exploit the initial treatment plan to predict an adaptive radiotherapy plan.
Methods: A conditional generative adversarial network is...

Portpy: An Open-Source Python Package to Accelerate Research in Radiotherapy Treatment Planning Optimization

Authors: Qijie Huang, Gourav Jhanwar, Saad Nadeem, Vicki Trier Taasti, Mojtaba Tefagh, Seppo Tuomaala, Masoud Zarepisheh

Affiliation: Varian Medical Systems Inc, Department of Clinical Medicine - Danish Center for Particle Therapy, Aarhus University Hospital, Memorial Sloan Kettering Cancer Center, The University of Edinburgh

Abstract Preview: Purpose:
We have developed PortPy (Planning and Optimization for Radiation Therapy in Python), a first-of-its-kind open-source package designed to accelerate research and development in radiotherap...

Preliminary Clinical Experience with MRI-Guided Online Adaptive Radiotherapy for Esophageal Cancer Patients

Authors: Ali Hosni, Oleksii Semeniuk, Andrea Shessel, Teo Stanescu

Affiliation: Princess Margaret Hospital, Princess Margaret Cancer Centre, Brown University Health

Abstract Preview: Purpose: To report on early clinical experience with a two-phase radiotherapy approach for esophageal cancer patients, utilizing CBCT-based conventional C-arm linear accelerator radiotherapy and MR-gu...

Real-Time Fully Automated IMRT Planning without Optimization Process Using a Two-Step AI Framework

Authors: Daisuke Kawahara, Takaaki Matsuura, Yuji Murakami, Ryunosuke Yanagida

Affiliation: Hiroshima High-Precision Radiotherapy Cancer Center, Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima

Abstract Preview: Purpose: In recent years, automation in radiation therapy planning using AI has gained significant attention to reduce the workload of treatment planners. Adaptive Radiation Therapy (ART), as a new fo...

Scoring Functions for Reinforcement Learning in Accelerated Partial Breast Irradiation Treatment Planning

Authors: Rafe A. McBeth, Kuancheng Wang, Ledi Wang

Affiliation: Department of Radiation Oncology, University of Pennsylvania, Georgia Institute of Technology, University of Pennsylvania

Abstract Preview: Purpose:
The integration of AI in clinical workflows presents unprecedented opportunities to enhance treatment quality in radiation oncology, yet it also demands innovative approaches to address th...

Standardized MRI-CT Hybrid Workflow for High-Dose-Rate Image-Guided Adaptive Brachytherapy in Cervical Cancer: Aapm TG-303 Implementation

Authors: Kim Creach, Kim Howard, Julius G. Ojwang, Richard A. Shaw, Neelu Soni

Affiliation: Mercy Hospital Springfield

Abstract Preview: Purpose: To present a standardized MRI-CT hybrid workflow for High-Dose-Rate (HDR) Image-Guided Adaptive Brachytherapy (IGBT) in cervical cancer, aligned with AAPM TG-303, as a model to assist with im...

Streamlining Quad-Shot Radiotherapy: Automated Workflow Enables Same-Day Treatment for Palliative Lung Cancer

Authors: Laura I. Cervino, Sharif Elguindi, Yu-Chi Hu, Licheng Kuo, Xiaoning Liu, Jennifer Ma, Pengpeng Zhang

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

Abstract Preview: Purpose:
Quad-shot radiotherapy (QSRT) is an emerging paradigm in palliative cancer treatment. This study aimed to streamline the QSRT workflow using automation to improve efficiency while maintain...

Time-Resolved CBCT: A Novel Solution for Motion Management

Authors: Bulent Aydogan, Xiaochuan Pan, Erik Pearson, Zheng Zhang

Affiliation: The University of Chicago

Abstract Preview: Purpose: Motion management remains one of the significant challenges in external beam radiation therapy (RT). We are developing time-resolved cone-beam computed tomography (trCBCT) for imaging during ...

Universal Anatomical Mapping and Patient-Specific Prior Implicit Neural Representation for MRI Super-Resolution

Authors: Jie Deng, Yunxiang Li, You Zhang

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

Abstract Preview: Purpose: Magnetic Resonance Imaging (MRI) has exceptional soft tissue contrast and an essential role in radiotherapy. The introduction of clinical MR-LINACs has enabled adaptive radiotherapy (ART) usi...

Using Radiotherapy Dose Accumulation Routine (RADAR) for Deformable Offline Adaptive Head and Neck Dose Accumulation

Authors: Shira Abraham, Eric Aliotta, Michalis Aristophanous, Laura I. Cervino, Yu-Chi Hu, Phillip G. Lichtenwalner, Chuan Zeng, Pengpeng Zhang

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

Abstract Preview: Purpose:
We have developed a tool that uses deformable image registrations to perform deformable dose accumulation. In this work we evaluate use of this tool for adaptive head and neck dose accumul...

Validating a Pre-Existing CT HU-to-Mass Density Curve for Direct Dose Calculation on High Quality Cbcts

Authors: Casey E. Bojechko, Tricia Chinnery, Grace Gwe-Ya Kim, Xenia Ray

Affiliation: University of California San Diego

Abstract Preview: Purpose: To evaluate the use of a calibration curve generated from the CT simulator for direct dose calculation on cone beam computed tomography (CBCT) images taken with the on-board imaging system. T...

Weak-to-Strong Generalization for Interpretable Deep Learning-Based Histological Image Classification Guided By Hand-Crafted Features

Authors: Mark Anastasio, Zong Fan, Hua Li, Changjie Lu, Lulu Sun, Xiaowei Wang, Zhimin Wang, Michael Wu

Affiliation: University of Illinois at Urbana-Champaign, University of Illinois at Chicago, Washington University School of Medicine, University of Illinois Urbana-Champaign, Washington University in St. Louis, University Laboratory High School

Abstract Preview: Purpose: Histological whole slide images (WSIs) are vital in clinical diagnosis. Although deep learning (DL) methods have achieved great success in this task, they often lack interpretability. Traditi...