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Results for "learning algorithm": 74 found

18F-FDG PET/CT-Based Deep Radiomic Models for Enhancing Chemotherapy Response Prediction in Breast Cancer

Authors: Ke Colin Huang, Zirui Jiang, Joshua Low, Christopher F. Njeh, Oluwaseyi Oderinde, Yong Yue

Affiliation: Purdue University, Indiana University School of Medicine, Department of Radiation Oncology

Abstract Preview: Purpose: Enhancing the accuracy of tumor response predictions enables the development of tailored therapeutic strategies for patients with breast cancer (BCa). In this study, we developed deep-radiomi...

3D Image Quality Evaluation of a New CT Scanner Employing 3D Landmark Scans, Super Resolution Reconstruction, and Ag Beam Filtration

Authors: Ishika Bhaumik, John M. Boone, Michael T Corwin, Eric S Diaz, Ahmadreza Ghasemiesfe, Andrew M. Hernandez, Sarah E. McKenney, Misagh Piran, Ali Uneri, Eric L White

Affiliation: UC Davis, UC Davis Health, University of California, Johns Hopkins Univ

Abstract Preview: Purpose: A new model CT scanner (Canon Aquilion One Insight) was recently installed at our institution, and it included a 3D Landmark (3DLM) scan for automatic scan planning, a new deep learning recon...

A Clinical Evaluation of Two Commercially Available Deep-Learning Algorithms for Automated Organs at Risk Contouring

Authors: Steven DiBiase, Gurtej S. Gill, Haohua Billy Huang, Nicholas J. Lavini, Luxshan Shanmugarajah, Salar Souri, Samantha Wong

Affiliation: Stony Brook University, Northwell Health, Cornell University, NewYork-Presbyterian, New York-Presbyterian

Abstract Preview: Purpose: Clinical applications of deep learning-based algorithms have come to the radiation oncology field as organ at risk (OAR) auto contouring programs. We evaluated two of these algorithms’ (Radfo...

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 Dual Energy CT-Guided Intelligent Radiation Therapy Platform

Authors: Jiayi Chen, Manju Liu, Ning Wen, Haoran Zhang, Yibin Zhang

Affiliation: Department of Radiation Oncology, Ruijin Hospital, Department of Radiology, Ruijin Hospital Shanghai Jiaotong University School of Medicine, Duke Kunshan University, Department of Radiation Oncology,Ruijin Hospital, Shanghai Jiao Tong University School of Medicine

Abstract Preview: Purpose: This study introduces a novel Dual Energy CT (DECT)-Guided Intelligent Radiation Therapy (DEIT) platform designed to streamline and optimize the radiotherapy process. The DEIT system combines...

A Feasible, Extendable, and Low-Cost Web-Based Application to Minimize the Second-Check Workload

Authors: William N. Duggar, Li Yuan

Affiliation: University of Mississippi Medical Center

Abstract Preview: Purpose:
Radiation Oncology departments typically utilize various systems from different vendors. Ensuring the integrity and correctness of data during transfers between these systems is essential ...

A No-Reference Medical Image Quality Assessment Method Based on Automated Distortion Recognition Technology: Application to Preprocessing in MRI-Guided Radiotherapy

Authors: Jiayun Chen, Shengqi Chen, Yuan Tang, Zilin Wang, Guohua Wu, Jianan Wu

Affiliation: Cancer Hospital Chinese Academy of Medical Sciences, Shenzhen Center, School of Electronic Engineering, Beijing University of Posts and Telecommunications, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College

Abstract Preview: Purpose:
To develop a novel no-reference image quality assessment (NRIQA) method for evaluating the effectiveness of image preprocessing in MRI-guided radiotherapy (MRIgRT), thereby enhancing clini...

A Novel Feature Selection Method for Survival Prediction of Head-and-Neck Following Radiation Therapy

Authors: Xiaoying Pan, X. Sharon Qi

Affiliation: Department of Radiation Oncology, University of California, Los Angeles, School of Computer Science and technology,Xi'an University of Posts and Telecommunications

Abstract Preview: Purpose:
Survival prediction for cancer presents a substantial hurdle in personalized oncology, due to intricate, high-dimensional medical data. Our study introduces an innovative feature selection...

A Real-Time Framework for Fiducial Tracking and Intrafraction Motion Assessment of Cyberknife in Stereotactic Body Radiation Therapy for Liver Cancer

Authors: Ruiyan Du, Mingzhu Li, Ying Li, Wei Liu, Shihuan Qin, Yiming Ren, Biao Tu, Hui Xu, Lian Zhang, Xiao Zhang, Zengren Zhao

Affiliation: Department of General Surgery, Hebei Key Laboratory of Colorectal Cancer Precision Diagnosis and Treatment, The First Hospital of Hebei Medical University, Medical AI Lab, The First Hospital of Hebei Medical University, Hebei Provincial Engineering Research Center for AI-Based Cancer Treatment Decision-Making, The First Hospital of Hebei Medical University, Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Department of Radiation Oncology, Mayo Clinic, Department of Oncology, The First Hospital of Hebei Medical University

Abstract Preview: Purpose: Fiducial tracking is widely used in CyberKnife to dynamically guide the gantry for moving target like liver cancer stereotactic body radiation therapy (SBRT). This study developed a robust fr...

A Semi-Automated Landmark Identification Framework for Liver MR-CT Image Pairs: Towards a Multi-Modality DIR Benchmark Dataset

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...

A Single-View-Based Electroacoustic Tomography Imaging Using Deep Learning for Electroporation Monitoring

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 ...

AI-Assisted Cellular and Organoid Analysis for Lenalidomide-Based Radioimmunotherapy Against Glioblastoma

Authors: ISAAC Amoah, Jackie Austin, Charlotte Block, Kaylee Brilz, Dylan Bui, Andrew E. Ekpenyong, Jayce Hughes, Pralhad Itani, Natasha Ratnapradipa, Sara Strom, Jacob Woolf

Affiliation: Creighton University

Abstract Preview: Purpose:
Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adults, with a median survival of approximately 15 months despite the current standard of care, which includes s...

Advancing Biodosimetry with AI: Detecting Dicentric Chromosomes Using Convolutional Neural Networks

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...

Advancing Deep Segmentation Accuracy in CBCT for Radiotherapy Via Robust Scatter Mitigation: First Results from a Pilot Trial

Authors: Cem Altunbas, Farhang Bayat, Roy Bliley, Rupesh Dotel, Brian Kavanagh, Uttam Pyakurel, Tyler Robin, Ryan Sabounchi

Affiliation: Department of Radiation Oncology, University of Colorado School of Medicine, Taussig Cancer Center, Cleveland Clinic, University of Colorado Anschutz Medical Campus

Abstract Preview: Purpose: Automatic segmentation of anatomical structures in CBCT images is key to enabling dose delivery monitoring and online plan modifications in radiotherapy. However, poor image quality can degra...

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...

Application of the Lymphodose Framework to Brain Tumors: Unveiling the Prognostic Power of Circulating Lymphocyte Doses

Authors: Sophie Bockel, Eric Deutsch, Frederic Dhermain, Ibrahima Diallo, Anh Thu Le, Elaine Limkin, Pauline Maury, Charlotte Robert, Killian Sambourg, Camilla Satragno, Cristina Veres, François de Kermenguy

Affiliation: Gustave Roussy, Département de radiothérapie, Université Paris-Saclay, Gustave Roussy, Inserm U1030, Radiothérapie Moléculaire et Innovation Thérapeutique

Abstract Preview: Purpose:
To study the correlation between the dose to circulating lymphocytes as evaluated by the LymphoDose framework and the incidence of severe radiation-induced lymphopenia (sRIL) in patients t...

Automated Case Prioritization in Breast Radiation Therapy Peer Review Rounds

Authors: Leigh A. Conroy, Thomas G Purdie, Christy Wong

Affiliation: Department of Medical Biophysics, University of Toronto, Princess Margaret Cancer Centre

Abstract Preview: Purpose: To develop a novel machine learning (ML) algorithm to evaluate and rank breast radiation therapy (RT) treatment plans based on treatment complexity for prioritization in multidisciplinary pee...

Automated Diagnosis of Pancreatic Cancer Using Both Radiomics and 3D-Convolutional Neural Network

Authors: Beth Bradshaw Ghavidel, Benyamin Khajetash, Yang Lei, Meysam Tavakoli

Affiliation: Icahn School of Medicine at Mount Sinai, Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Emory University, Department of Radiation Oncology, Emory University

Abstract Preview: Purpose: Pancreatic cancer is among the most aggressive types of cancer, with a five-year survival rate of approximately 10%. Recent studies show that radiomics and deep learning (DL)-based methods ar...

Automatic Specific Absorption Rate (SAR) Prediction for Hyperthermia Treatment Planning (HTP) Using Deep Learning Method

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...

Cherenkov Image Denoising with Diffusion-Based Deep-Learning for High-Fidelity Video Display of EBRT

Authors: Petr Bruza, Jeremy Eric Hallett, Brian W Pogue, Yucheng Tang, Shiru Wang

Affiliation: NVIDIA Corp, Dartmouth College, Thayer School of Engineering, Dartmouth College, University of Wisconsin-Madison, University of Wisconsin - Madison

Abstract Preview: Purpose: Cherenkov imaging allows for real-time visualization of megavoltage X-ray or electron beam delivery during radiation therapy. By using a time-gated intensified CMOS camera synchronized with a...

Clinical Assessment of Synthetic CT in MR-Only Brain Radiotherapy

Authors: Ergun E. Ahunbay, Colette Gage, Abdul Kareem Parchur, Eric S. Paulson

Affiliation: Department of Radiation Oncology, Medical College of Wisconsin

Abstract Preview: Purpose: AI-generated synthetic CT (sCT) images address challenges with prior sCT approaches, including atlas- and threshold-based methods. Commercial AI-based sCT tools have been introduced. This wor...

Commission and Clinical Implementation of the 1st Step-and-Shoot Proton Arc Therapy for Head and Neck Cancer Patient Treatment

Authors: Xiaoda Cong, Rohan Deraniyagala, Xuanfeng Ding, Xiaoqiang Li, Jian Liang, Peilin Liu, Craig Stevens, Xiangkun Xu, Weili Zheng

Affiliation: Corewell Health William Beaumont University Hospital, Corewellhealth William Beaumont University Hospital, William Beaumont University Hospital, Corewellhealth William Beaumont Hospital, Department of Radiation Oncology, Corewell Health William Beaumont University Hospital

Abstract Preview: Purpose:
Commission a step-and-shoot arc therapy(SPArc-step&shoot) for treating head-neck cancer patients as a desired interim milestone toward full dynamic treatment.
Methods:
An in-house de...

Deep Learning-Based Auto-Segmentation in Cervical High-Dose-Rate Brachytherapy with Clinical Considerations

Authors: Benjamin Haibe-Kains, Ruiyan Ni, Alexandra Rink

Affiliation: Department of Medical Biophysics, University of Toronto, University Health Network

Abstract Preview: Purpose: Accurate auto-segmentation for targets and organs-at-risk (OARs) using deep learning reduces the delineating time in radiotherapy. In high-dose-rate brachytherapy, specific clinical criteria ...

Deep Learning-Based Prompt Gamma Imaging for Proton Range Verification Using Patient Data Simulation

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...

Deep Learning-Based Ventricular Auto-Segmentation for Dosimetric Analysis in Intraventricular Tumor SRS

Authors: John Byun, Juan J Cardona, Steven D Chang, Cynthia Fu-Yu Chuang, Xuejun Gu, Yusuke Hori, Hao Jiang, Fred Lam, Lianli Liu, Weiguo Lu, David Park, Erqi Pollom, Elham Rahimy, Deyaaldeen Abu Reesh, Scott Soltys, Gregory Szalkowski, Lei Wang

Affiliation: Department of Radiation Oncology, Stanford University, Department of Neurosurgery, Stanford School of Medicine, 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:
Intraventricular tumors pose significant challenges in neurosurgery due to their complex location. Therefore, brain SRS could be a better treatment option. At our institution, some patient...

Developing an AI-Driven Predictor for Forecasting Treatment Outcomes in Patients with Early-Stage Breast Cancer

Authors: Lucy Jiang, Chengyu Shi

Affiliation: Department of Radiation Oncology, City of Hope Orange County, Amity Regional High School (10th Grade)

Abstract Preview: Purpose: Early-stage breast cancer is common among females, with typically high local tumor control rates (LCR). Brachytherapy is a common way to achieve LCR following surgery. However, the patients m...

Development of Foundation Model for Analysis of Prostate Cancer with Mpmri

Authors: Ahmad Algohary, Adrian Breto, Quadre Emery, Radka Stoyanova

Affiliation: University of Miami, Department of Radiation Oncology, University of Miami

Abstract Preview: Purpose:
To develop a foundation model (U-Found) for multiparametric MRI (mpMRI) of the prostate by using self-supervised learning to prove the feasibility of a prostate-oriented foundation model u...

Development of a Quantitative Surface Mapping Analysis Framework Involving a Robust Mask Removal Algorithm for Improved Objective Patient Setup Assessment in Head and Neck Intensity Modulated Proton Therapy

Authors: Grant Evans, Maxwell Arthur Kassel, Charles Shang, Michael H. Shang, Stephen Shang, Timothy R Williams

Affiliation: South Florida Proton Therapy Institute, SFPRF, Department of Radiation Medicine, MedStar Georgetown University Hospital

Abstract Preview: Purpose:
Daily image guidance for head and neck intensity-modulated proton therapy (IMPT) presents significant challenges due to large target volumes and anatomical changes. Geometric deviations al...

Dual-Domain Reconstruction Network for Nonstop Gated CBCT Imaging: Application in Respiratory Gating Ablative Radiotherapy for Pancreatic Cancer

Authors: Sean L. Berry, Weixing Cai, Laura I. Cervino, Yabo Fu, Daphna Gelblum, Wendy B. Harris, Xiuxiu He, Licheng Kuo, Tianfang Li, Xiang Li, Jean M. Moran, Boris Mueller, Huiqiao Xie, Mitchell Yu, Hao Zhang

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

Abstract Preview: Purpose: Gating ablative radiotherapy for pancreatic cancer accounts for tumor movement due to respiration and typically requires 5, 15, or 25 fractions. Pretreatment imaging verification is essential...

Enhanced Lung Function Assessment through Machine Learning Analysis of 4DCT Subregional Respiratory Dynamics

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...

Enhanced Predictive Model for Toxicity and 3-Year Survival in HCC Patients Using Learning Health System Infrastructure and AI-Driven Statistical Profiling

Authors: Ameer Elaimy, Theodore Lawrence, Charles S. Mayo, Seyyedeh Azar Oliaei Motlagh, Benjamin S. Rosen

Affiliation: University of Michigan

Abstract Preview: Purpose: To analyze the impact of clinical features on short-term survival, toxicity, and poor outcomes in HCC patients treated with SBRT,using automated data aggregation and enhanced algorithms with ...

Ensuring Consistency in Digital Pathology: Medical Physics Approaches to Comparison of Scanner Contrast and Chromaticity

Authors: Diana Cardona, Casey C. Heirman, William Jeck, Kyle J. Lafata, Xiang Li, Lauren M. Neldner, Jeffrey S. Nelson, Megan K. Russ, Ehsan Samei

Affiliation: Duke University, Department of Radiation Oncology, Duke University, Department of Pathology, Duke University, Clinical Imaging Physics Group, Department of Radiology, Duke University Health System

Abstract Preview: Purpose: Medical physicists have traditionally supported radiation-based medicine, but their expertise can translate to other image-based fields including pathology. As pathology transitions to digita...

Ensuring Consistency in Digital Pathology: Medical Physics Approaches to Comparison of Scanner Sharpness and Artifact Severity

Authors: Diana Cardona, Casey C. Heirman, William Jeck, Kyle J. Lafata, Xiang Li, Lauren M. Neldner, Jeffrey S. Nelson, Megan K. Russ, Ehsan Samei

Affiliation: Duke University, Department of Radiation Oncology, Duke University, Department of Pathology, Duke University, Clinical Imaging Physics Group, Department of Radiology, Duke University Health System

Abstract Preview: Purpose: Medical physicists traditionally support radiation-based medicine, but their expertise is translatable to image-based fields like pathology. As pathology transitions to digital practices, phy...

Estimation of Heart Dose in Left Breast Cancer Radiotherapy: Assessment of Vdibh Feasibility Using the Supervised Machine Learning Algorithm

Authors: Rajeev Gupta, Shriram Ashok Rajurkar, Teerthraj Verma

Affiliation: King George's Medical University, King George's Medical University, UP

Abstract Preview: Purpose:
The volunteer deep inspiration breath hold (vDIBH) technique is used to reduce the heart dose in left breast cancer radiotherapy. Many times, it is faced that despite rigorous exercise and...

Evaluating the Impact of Different Deface Algorithms on the Deep Learning Segmentation Software Performance

Authors: Ali Ammar, Quan Chen, Yi Rong, Libing Zhu

Affiliation: Mayo Clinic Arizona

Abstract Preview: Purpose: To investigate how defacing algorithms, essential for patient privacy in data sharing, impact AI-based segmentation performance in CT imaging for radiation therapy. This study evaluates wheth...

From Prediction to Practice: Performance of a Deep Learning-Based Breast Planning Algorithm

Authors: Thomas L. Hayes, Nicholas C. Koch, Han Liu, Qingyang (Grace) Shang, Benjamin J. Sintay, Caroline Vanderstraeten, David B. Wiant

Affiliation: Fuse Oncology, Cone Health, Cone Health Cancer Center

Abstract Preview: Purpose:
This study evaluates the accuracy of a deep learning-based automatic breast planning script in predicting beam energy for breast cancer treatments. The script was validated and implemented...

Gaze Angle Selection in Proton Therapy for Ocular Tumors with Machine Learning

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...

Generating Brain Pseudo-CT from PET-Only Images Using Deep Learning Method

Authors: Pouya Azarbar, Nima Kasraie, Mahsa Shahrbabki Mofrad, Peyman Sheikhzadeh

Affiliation: UT Southwestern Medical Center, Shahid Beheshti University of Medical science, Imam Khomeini Hospital Complex,Tehran University of Medical Sciences, Tehran University of Medical Science

Abstract Preview: Purpose: PET imaging become crucial in diagnosing and managing various diseases, but its key limitation is the lack of detailed anatomical information. Integrating CT-scans with PET images enhances cl...

Generation of Virtual Lung PET Images from CT Data Via Deep Learning for Accelerated Tumor Detection and Preliminary Diagnosis

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...

Iguard: A Fully Unsupervised Image-Guidance Anomaly Recognition and Detection Framework in CBCT-Guided Radiotherapy.

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...

Impact of Computed Tomography Noise Reference Levels in a Pediatric Hospital

Authors: Samuel L. Brady, Joseph G. Meier

Affiliation: Cincinnati Childrens Hospital Med Ctr

Abstract Preview: Purpose:
To establish noise reference levels for our pediatric hospital.
Methods:
Water equivalent diameter (Dw) and image noise was automatically measured by using a global noise algorithm i...

In silico Evaluation Vs Standard Phantom Evaluation of a Deep Learning Reconstruction Algorithm

Authors: Naruomi Akino, Kirsten Lee Boedeker, Ilmar Hein, Dylan Mather, Akira Nishikori, Daniel W Shin

Affiliation: Canon Medical Systems Corporation, Canon Medical Research USA

Abstract Preview: Purpose: To validate the performance a deep learning reconstruction (DLR) algorithm in an anatomical background compared to a uniform phantom background.
Methods: An analytic forward projection mod...

Incorporating Physicians’ Contouring Style into Auto-Segmentation of Clinical Target Volume for Post-Operative Prostate Cancer Radiotherapy Using a Language Encoder

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, ...

Insights into Deep Learning Auto-Segmentation for Abdominal Organs in MR-Guided Adaptive Radiation Therapy: A Single-Institution CT-MR Comparison

Authors: Asma Amjad, Renae Conlin, Eric S. Paulson, Christina M. Sarosiek

Affiliation: Department of Radiation Oncology, Medical College of Wisconsin

Abstract Preview: Purpose:
MR-guided adaptive radiation therapy (MRgART) is transforming clinical workflows, requiring fast, accurate organs-at-risk (OARs) contouring. While deep learning auto-segmentation (DLAS) of...

Integrating Large Kernel Attention Mechanism into Deep Learning Model for Automatic and Auccrate Segmentation of Gross Tumor Volume in Lung Cancer Patients

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...

Investigation and Machine-Learning Modeling of Dosimetric Discrepancies in Eclipse-Calculated Head and Neck Treatment Plans

Authors: Andres Portocarrero Bonifaz, Ian Schreiber

Affiliation: CARTI Cancer Center

Abstract Preview: Purpose: To explore how calculation grid resolution, along with other planning factors, affects head and neck dose calculation accuracy and contributes to potential discrepancies in the Eclipse Treatm...

Is Simplicity Even Better: Deep Learning Algorithms for Breath Motion Phase Prediction in Motion Management

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...

Key Tumor Volume Zones for Advancing the Radiomics-Based Distant Recurrence Prediction

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: Radiomics feature-based model for predicting distant recurrence can potentially provide critical insight for clinical decision-making and assistance in treatment strategies. This study focuse...

Latent Diffusion for 3D CT Reconstruction from Biplanar X-Rays

Authors: Guha Balakrishnan, Osama R. Mawlawi, Yiran Sun, Ashok Veeraraghavan

Affiliation: RICE University, UT MD Anderson Cancer Center

Abstract Preview: Purpose:
Previous deep learning (DL) techniques such as X2CT-GAN [1] has shown great promise in reconstructing realistic CT volume from biplanar X-rays, however they introduce numerous artifacts in...

Lymphocytic Feature Characterization Using a Deep Learning Algorithm on Post-Radiation Lymph Nodes

Authors: Rico Castillo, Katherine Gonzalez, Casey C. Heirman, Kyle J. Lafata, Casey Y. Lee, Xiang Li, Yvonne M Mowery, Yvonne M Mowery, Daniel Murphy, Allison Pittman, Ashlyn G. Rickard

Affiliation: Duke University, Department of Radiation Oncology, Duke University, University of Pittsburgh

Abstract Preview: Purpose: To evaluate the ability of a deep learning model to identify pathomic features in lymph nodes of preclinical head and neck squamous cell carcinoma (HNSCC) models as surrogates for predicting ...

Machine Learning Model for Early Prediction of Chemoradiotherapy Response in Oropharyngeal Cancer Patients

Authors: Waleed Mutlaq Almutairi, Ke Colin Huang, Vishwas Mukundan, Christopher F. Njeh, Oluwaseyi Oderinde, Yong Yue

Affiliation: Purdue University, Indiana University School of Medicine, Department of Radiation Oncology, Advanced Molecular Imaging in Radiotherapy (AdMIRe) Research Laboratory, Purdue University, West Lafayette, Indiana, USA

Abstract Preview: Purpose:
This study aimed to develop a machine learning (ML) model for early prediction of chemoradiotherapy (CRT) response in order to enhance personalized treatment selection for oral or orophary...

Multi-Modality Artificial Intelligence for Involved-Site Radiation Therapy: Clinical Target Volume Delineation in High-Risk Pediatric Hodgkin Lymphoma

Authors: Tyler J Bradshaw, Sharon M Castellino, Steve Y Cho, David Hodgson, Bradford S Hoppe, Kara M Kelly, Andrea Lo, Sarah Milgrom, Xin Tie

Affiliation: Department of Radiation Oncology, University of Toronto, Department of Radiology, University of Wisconsin, University of Colorado Anschutz, Department of Medical Physics, University of Wisconsin, Department of Radiation Oncology, Mayo Clinic, Department of Radiation Oncology, BC Cancer, Vancouver Center, Department of Radiology, University of Wisconsin - Madison, Roswell Park Comprehensive Cancer Center, Emory University School of Medicine

Abstract Preview: Purpose: Clinical target volume (CTV) delineation for involved-site radiation therapy (ISRT) in Hodgkin lymphoma (HL) is time-consuming due to the need to analyze multi-time-point PET/CT scans co-regi...

Multimodal Data Integration with Machine Learning for Predicting PARP Inhibitor Efficacy and Prognosis in Ovarian Cancer

Authors: Qianxi Ni, Xian Xiong

Affiliation: The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University

Abstract Preview: Purpose:
Poly(ADP)-ribose polymerase inhibitors (PARPi) have brought a significant breakthrough in the maintenance treatment of ovarian cancer. However, beyond BRCA mutation/HRD, the direct impact ...

Non-Contact Blood Pressure Estimation Using Remote Photoplethysmography Signals Extracted from Facial Video: A Deep Learning Approach

Authors: Mavlonbek Khomidov, Jong-Ha Lee

Affiliation: Department of Biomedical Engineering, Keimyung University, Department of Computer Engineering, Keimyung University

Abstract Preview: Purpose: In this research, we aim to estimate blood pressure using remote photoplethysmography (rPPG) signal extracted from facial video. This method provides non-invasive and contactless, continuous ...

Optimizing Timing of Physics Consults for Proton Prostate Therapy: Improving Patient Experience and Operational Efficiency

Authors: Charles D. Bloch, Stephen R. Bowen, Bing-Hao Chiang, Alex Egan, Eric C. Ford, Sharareh Koufigar, Dominic A. Maes, Juergen Meyer, Sharon Pai, Frank Rafie, Rajesh Regmi, Jatinder Saini, George A. Sandison, Marco Schwarz, Bishwambhar Sengupta, Tony P. Wong

Affiliation: Department of Radiation Oncology, Fred Hutchinson Cancer Center, University of Washington, Department of Physics, University of Washington

Abstract Preview: Purpose: This study aimed to optimize the strategy and timing of physics consults for proton prostate patients to improve the patient experience and resource utilization in our radiation oncology depa...

Optimizing Vessel Wall Visualization Using a Novel Black-Blood CT Technique on Craniocervical CT Angiography

Authors: Xiaohu Li, Jianjun Shen, Guozhi Zhang, Sihua Zhong, Jingjie Zhou

Affiliation: United Imaging Healthcare

Abstract Preview: Purpose:
Visualization of carotid artery vessel wall on computed tomography angiography (CTA) imaging is challenging. This study aims to develop a novel post-processing technique, black-blood compu...

Patient-Specific Coronary Artery Habitat Model for Enhanced Cardiac Sparing

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...

Performance Evaluation of CT-Based Lung Tumor Classification Deep Learning Algorithms Under Centralized and Federated Learning Frameworks

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...

Performance Evaluation of Patient Demographics Model-Based Liver Volumetry

Authors: Yasaman Anbari, Srinivas Cheenu Kappadath, Benjamin P. Lopez, Armeen Mahvash, Ali Yousefi

Affiliation: University of Houston, UT MD Anderson Cancer Center

Abstract Preview: Purpose: Patient-demographics-model-based liver volumetry is well-established for determining the future liver remnant following hepatectomy. We used gold-standard CT liver segmentation to validate th...

Physics and Geometry Input-Based Neural Network Dose Engine

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 ...

Predicting CBCT-Based Adaptive Radiation Therapy Session Duration Using Machine Learning

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...

Predicting Hematologic Toxicity in Advanced Cervical Cancer Patients Using Interpretable Machine Learning Models Based on Radiomics and Dosimetrics

Authors: Qianxi Ni, Qionghui Zhou

Affiliation: The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University

Abstract Preview: Purpose:
This study aims to develop and evaluate interpretable machine learning models that use radiomic and dosimetric features to predict HT in advanced cervical cancer patients.
Methods:
R...

Predicting Proton Therapy Dose Delivery Accuracy: A Machine Learning Approach Using Iroc’s Proton Phantom Data

Authors: Lian Duan, Stephen F. Kry, Hunter S. Mehrens, Paige A. Taylor

Affiliation: The University of Texas MD Anderson Cancer Center, UT MD Anderson Cancer Center

Abstract Preview: Purpose: To develop a machine learning model for predicting dose delivery accuracy and identifying its key factors in IROC’s proton phantom program.
Methods: IROC’s proton QA program has six proton...

Prior-Adapted Progressive Motion-Resolved CBCT Reconstruction Using a Dynamic Reconstruction and Motion Estimation Method

Authors: Hua-Chieh Shao, You Zhang, Ruizhi Zuo

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: Cone-beam CT (CBCT) provides on-board patient anatomy for image guidance and treatment adaptation in radiotherapy. However, to compensate for respiration-induced anatomical motion, motion-res...

Real-Time 3D Dose Verification for MR-Guided Online Adaptive Radiotherapy (ART) Via Geometry-Encoded Deep Learning Framework

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...

Real-Time Proton and Carbon Ion Monte Carlo Dose Calculation through GPU-Acceleration and DL-Based Denoising Algorithms

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...

Respiratory Monitoring in Human Subjects Using a Low-Cost Optical Imaging System Prototype

Authors: Marian Axente, Mandeep Kaur

Affiliation: Emory University

Abstract Preview: Purpose: To validate a low-cost optical imaging system for respiratory monitoring by comparing its accuracy and feasibility against the clinical gold standard in human subjects.
Methods: Following ...

Segmentation Regularized Registration Training Improves Multi-Domain Generalization of Deformable Image Registration for MR-Guided Prostate Radiotherapy

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...

Spatial Dosimetric-Based Prediction of Long-Term Urinary Toxicity after Permanent Prostate Brachytherapy

Authors: Rajeev K. Badkul, Ronald C Chen, Ying Hou, Harold Li, Chaoqiong Ma, Jufri Setianegara

Affiliation: Department of Radiation Oncology, University of Kansas Medical Center

Abstract Preview: Purpose:
Postimplant urinary toxicity is common in prostate low-dose-rate (LDR) brachytherapy. We developed a machine learning (ML) model to explore the correlation between spatial dose distributio...

Task-Specific Deep-Neural-Network Architecture Optimization for CBCT Scatter Correction

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...

Two-Stage Clustering and Auto Machine Learning to Predict Chemoradiation Response in Tumor Subregions on FDG PET for La-NSCLC

Authors: Stephen R. Bowen, Shijun Chen, Chunyan Duan, Daniel S. Hippe, Qiantuo Liu, Qianqian Tong, Jiajie Wang, Shouyi Wang, Faisal Yaseen

Affiliation: The University of Texas at Austin, Tongji University, University of Washington, Department of Radiation Oncology, Fred Hutchinson Cancer Center, University of Washington, Fred Hutchinson Cancer Center, University of Texas at Arlington

Abstract Preview: Purpose: Tumor subregion clustering and prediction of region-specific response can augment assessments and adaptive treatment decisions. A modeling framework was constructed to predict chemoradiation ...

Using Machine Learning to Predict Esophagitis Risk in Lung Cancer Radiotherapy Based on Clinical and Dosimetric Factors

Authors: Ibtisam Almajnooni, Siyong Kim, Nathaniel Miller, Elisabeth Weiss, Lulin Yuan

Affiliation: Virginia Commonwealth University

Abstract Preview: Purpose: Radiation-induced esophagitis (RE) is a common concern in lung cancer IMRT. Recent studies have indicated that the risk of radiation side effects varies greatly with patients’ baseline clinic...

VMAT Machine Parameter Optimization Using Policy Gradient Reinforcement Learning

Authors: Avinash Mudireddy, Nathan Shaffer, Joel J. St-Aubin

Affiliation: University of Iowa

Abstract Preview: Purpose: This work demonstrates preliminary results in training a reinforcement learning (RL) network to perform VMAT machine parameter optimization.
Methods: We implemented a policy gradient RL al...

Validation of a Simulation Tool and in-Silico Assessment of Low Contrast Detectability for Super-Resolution Deep Learning Reconstruction

Authors: Naruomi Akino, Kirsten Lee Boedeker, Ilmar Hein, Akira Nishikori, Daniel W Shin

Affiliation: Canon Medical Systems Corporation, Canon Medical Research USA

Abstract Preview: Purpose: To validate a simulation tool using physics-based image quality metrics in both phantom and patient data, and to assess the low contrast detectability (LCD) of Super Resolution-Deep Learning ...