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Results for "absolute error": 69 found

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 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-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 Deep Learning-Based Method for Rapid Generation of Spot Weights in Single Field Optimization for Proton Therapy in Prostate Cancer

Authors: Yu Chang, Mei Chen

Affiliation: Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine

Abstract Preview: Purpose: Spot weights optimization, as a critical step in the proton therapy, is often time-consuming and labor-intensive. Deep learning, with its powerful learning and computational efficiency, can e...

A Ground Truth Label-Mediated Method for Improved Bone and Gas Cavity Definition for MRI-Guided Online Adaptive Radiotherapy Workflows Using Synthetic CT Images.

Authors: Benito De Celis Alonso, Braian Adair Maldonado Luna, Gerardo Uriel Perez Rojas, René Eduardo Rodríguez-Pérez, Kamal Singhrao

Affiliation: Department of Radiation Oncology, Brigham and Women's Hospital, Dana Farber Cancer Institute, Harvard Medical School, Faculty of Physics and Mathematics, Benemérita Universidad Autónoma de Puebla

Abstract Preview: Purpose: Artificial Intelligence (AI)-generated synthetic CT (sCT) images can be used to provide electron densities for dose calculation for online adaptive MRI-guided stereotactic body radiotherapy (...

A Novel Margin-Based Focal Distance Loss for Lesion Segmentation in Medical Imaging

Authors: Weiguo Lu, Hua-Chieh Shao, Guoping Xu, 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:
Neural network-based lesion segmentation remains a significant challenge due to the low contrast between lesions and surrounding tissues (high ambiguity) and the variability of lesion shap...

A Realistic Computer Simulation Study Assessing How Beam Hardening Affects the Accuracy of CT Lung Attenuation Values

Authors: Zijia Guo, Michael F. McNitt-Gray, Frederic Noo, Karl Stierstorfer

Affiliation: Siemens Healthineers, University of Utah, David Geffen School of Medicine at UCLA

Abstract Preview: Purpose: Accurately assessing lung parenchyma health is critically important in the management of patients with chronic obstructive pulmonary disease. CT attenuation values are valuable for this purpo...

A Risk Assessment Approach to Photon Reference Dosimetry Using TG-100 Methodology

Authors: Ila Farhang, Ryan D. Foster, Devin Heitz, Jordan B Lunsford, Ashkan Shafiee

Affiliation: Atrium Health Wake Forest Baptist, Atrium Health/LCI Cabarrus

Abstract Preview: Purpose: American Association of Physicists in Medicine (AAPM) Task Group (TG) -51 is a widely utilized protocol for the absolute calibration of linear accelerators. The purpose of this project was fo...

A Topological Technique to Unify Image Texture and Morphology to Enhance Radiomic Feature Representations

Authors: David Brizel, Kyle J. Lafata, Jian-Guo Liu, Yvonne M Mowery, Yvonne M Mowery, William Paul Segars, Jack B Stevens

Affiliation: Department of Physics, Duke University, Carl E. Ravin Advanced Imaging Laboratories and Center for Virtual Imaging Trials, Duke University Medical Center, Duke University, Department of Radiation Oncology, Duke University, University of Pittsburgh

Abstract Preview: Purpose: To develop a technique to quantify tumor topology using a unifying mathematical framework that integrates texture and morphology and to evaluate its feasibility as a prognostic biomarker for ...

A Tumor Tracking Method in Surface-Guided Radiotherapy

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...

A Vqvae-Based Framework with Embedded Kullback-Leibler Divergence for Stochastic and Diverse Dose Prediction

Authors: Weigang Hu

Affiliation: Fudan University Shanghai Cancer Center

Abstract Preview: Purpose: The purpose of this study is to introduce a VQVAE-based framework that addresses the limitations of conventional dose prediction methods, which rely on fixed deep learning models that produce...

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...

Automating Radiographic Sharp Score Prediction in Rheumatoid Arthritis Using Multistage Deep Learning Methods

Authors: Hajar Moradmand, Lei Ren

Affiliation: University of Maryland School of Medicine, University of Maryland

Abstract Preview: Purpose:
The Sharp-van der Heijde (SvH) score is essential for assessing joint damage in rheumatoid arthritis (RA) from radiographic images. However, manual scoring is time-intensive and prone to v...

CNN-Based Reconstruction for 3D Scintillation Dosimetry of Proton Pencil Beams

Authors: Sam Beddar, Jason Michael Holmes, Daniel G. Robertson, James J. Sohn, Ethan D. Stolen

Affiliation: Department of Radiation Oncology, Mayo Clinic, MD Anderson Cancer Center, Department of Radiation & Cellular Oncology, University of Chicago, Department of Radiation and Cellular Oncology, University of Chicago

Abstract Preview: Purpose: Camera-based scintillation dosimetry incorporating large volumes have shown promise for fast and comprehensive evaluation of external beam treatment fields. While some efforts have been made ...

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...

Comparative Evaluation of Nn-Unet Models for Radiotherapy Dose Prediction Using the Head and Neck Cancer Patients

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)...

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...

Cycle-Consistent Multi-Task Automated Segmentation and Synthetic CT Generation Model for Adaptive Proton Therapy

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: ...

Deep Learning-Based Auto Segmentation of Oars in Head and Neck Radiation Therapy

Authors: Laila A Gharzai, Bharat B Mittal, Poonam Yadav

Affiliation: Northwestern Feinberg School of Medicine, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, Northwestern University Feinberg School of Medicine

Abstract Preview: Purpose: Multiple studies have shown the increasing role of deep learning in segmenting regions of interest. This work presents the feasibility of auto-segmenting the critical structures for head and ...

Deep Learning-Based Denoising for Template Matching in Real-Time Tumor Tracking Using Kv Scattered X-Ray Imaging

Authors: Weikang Ai, Xiaoyu Hu, Xun Jia, Kai Yang, Yuncheng Zhong

Affiliation: Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Massachusetts General Hospital, Department of Biomedical Engineering, Johns Hopkins University, Johns Hopkins University

Abstract Preview: Purpose: Real-time tumor tracking is critically important for respiratory motion management for lung cancer radiotherapy. A previously proposed application of a photon counting detector involves measu...

Deep Learning-Based Fast CBCT Imaging with Orthogonal X-Ray Projections for Gynecological Cancer Radiotherapy

Authors: Beth Bradshaw Ghavidel, Chih-Wei Chang, Yuan Gao, Priyanka Kapoor, Shaoyan Pan, Junbo Peng, Richard L.J. Qiu, Jill Remick, Justin R. Roper, Zhen Tian, Xiaofeng Yang

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

Abstract Preview: Purpose: Current cone-beam computed tomography (CBCT) typically requires no less than 200 degrees of angular projections, which prolongs scanning time and increases radiation exposure. To address thes...

Development of a Deep Learning Model for Accurate Brain Dose Prediction in Multi-Target Stereotactic Radiosurgery Plan Evaluation

Authors: Wenchao Cao, Yingxuan Chen, Haisong Liu, Wenyin Shi, Wentao Wang, Lydia J. Wilson, Zhenghao Xiao

Affiliation: Thomas Jefferson University

Abstract Preview: Purpose: Multi-target stereotactic radiosurgery (SRS) planning poses challenges due to complex geometries, small target volumes, and steep dose gradients. Achieving a balance between target coverage a...

Development of a Knowledge-Based Planning Model for Optimal Trade-Off Guidance in Locally Advanced Non-Small Cell Lung Cancer

Authors: Ming Chao, Hao Guo, Tenzin Kunkyab, Yang Lei, Tian Liu, Kenneth Rosenzweig, Robert Samstein, James Tam, Junyi Xia, Jiahan Zhang

Affiliation: Icahn School of Medicine at Mount Sinai

Abstract Preview: Purpose:
The aim of the study is to develop a trade-off prediction model to efficiently guide the treatment planning process for patients with stage III non-small cell lung cancer (NSCLC).
Metho...

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 Denoising of Low-Statistic Influence Matrices Using a Diffusion Transformer-Based Framework for Adaptive Proton Therapy

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...

Enhanced Prediction of Iroc Stereotactic Radiosurgery Phantom Audit Results with Treatment Parameters, Complexity Metrics, DVH, and Dosiomics Using Machine Learning

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 ...

Enhancing Proton Treatment and Mitigating Radiation-Induced Lung Injury Using a Novel Cycle Diffusion Approach for Lung Ventilation Estimation

Authors: Yang Lei, Haibo Lin, Tian Liu, Charles B. Simone, Shouyi Wei, Ajay Zheng

Affiliation: Icahn School of Medicine at Mount Sinai, New York Proton Center

Abstract Preview: Purpose: Radiation-induced lung injury (RILI), encompassing pneumonitis and fibrosis, represents a critical dose-limiting factor in lung cancer radiation therapy. Variability in treatment outcomes is ...

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 ...

Enhancing the CT Contrast Via Attention-Gated Contrast Enhancement Gan (AGCE-GAN)

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 ...

Evaluating Stent-Induced Dose Perturbations in Intravascular Brachytherapy with EBT4 Films

Authors: Yijian Cao, Jenghwa Chang, Nicholas Coupey, Lyu Huang, Jessica Jung

Affiliation: Northwell, Hofstra University Medical Physics Program

Abstract Preview: Purpose:
The Synergy bioabsorbable polymer (BP) stent, made of Platinum Chromium Alloy, is widely utilized in treating coronary artery disease. This study investigates dose perturbations caused by ...

Evaluating Uncertainty Estimation Models for Clinical Integration of AI-Generated Radiotherapy Dose Distributions

Authors: Jacob S. Buatti, Kristen A. Duke, Malena Fassnacht, Neil A. Kirby, Parker New, Niko Papanikolaou, Arkajyoti Roy, Yuqing Xia, Michelle de Oliveira

Affiliation: The University of Texas San Antonio, UT Southwestern Medical Center, UT Health San Antonio

Abstract Preview: Purpose:
Quantifying and visualizing uncertainty is critical for building clinical trust in AI-generated dose distributions. This study evaluates Monte Carlo Dropout (MCD), Snapshot Ensemble (SE), ...

Evaluation of MR Proton Density Fat Fraction (PDFF) for Bone Marrow Protection in RT

Authors: Li Tong, Chuyan Wang, Zhengkui Wang, Yingli Yang, Jie Zhang

Affiliation: Shanghai United imaging Healthcare Advanced Technology Research Institute, Shanghai United Imaging Healthcare Co., LTD, Department of Radiology, Ruijin Hospital, Institute for Medical Imaging Technology, Ruijin Hospital

Abstract Preview: Purpose:
Pelvic radiotherapy (RT)-induced bone marrow (BM) damage affects patient prognosis by causing hematologic toxicity. However, consensus on BM-sparing (BMS) RT is still lacking, owing to the...

Evaluation of MRI Distortion across a Multi-Site Health System and the Importance of MRI Distortion QA in Stereotactic Radiosurgery (SRS)

Authors: Emel Calugaru, Jenghwa Chang, Sean T Grace, Nicholas Harvey, Jessica Jung, Ching-Ling Teng

Affiliation: Northwell, Hofstra University, Hofstra University Medical Physics Program

Abstract Preview: Purpose: To evaluate the consistency of MRI distortion corrections across Northwell Health system and evaluate the necessity and importance of distortion QA for MRIs used for stereotactic radiosurgery...

First in-Vivo Proton Radiography Experiments for an Image-Guided Small Animal Irradiation Platform with a TimePix3-Based Detector Setup

Authors: Niels Bassler, Jonathan Bortfeldt, Carlos Granja, Guyue Hu, Margarita Kozak, Julie Lascaud, Kirsten Lauber, Grigory Liubchenko, Jasper Nijkamp, Cristina Oancea, Prasannakumar Palaniappan, Katia Parodi, Marco Pinto, Per R. Poulsen, Marco Riboldi, Brita Singers Sørensen, Matthias Würl

Affiliation: Department of Nuclear Medicine, LMU University Hospital, LMU Munich, Department of Radiation Oncology, LMU University Hospital, LMU Munich, Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Danish Centre for Particle Therapy, Aarhus University Hospital, Department of Medical Physics, Ludwig-Maximilians-Universität (LMU) München, Department of Experimental Clinical Oncology, Aarhus University, Department of Oncology, Aarhus University Hospital, Advacam

Abstract Preview: Purpose: To apply in-vivo proton radiography (pRad) to aid image registration and treatment plan refinement for a novel precision image-guided small animal proton irradiation platform.

Methods:...

Generalized 2D Cine Multi-Modal MRI-Based Dynamic Volumetric Reconstruction Using Motion-Aligned Implicit Neural Network with Spatial Prior Embedding

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...

Generating Synthetic Positron Emission Tomography from Computed Tomography Using Lightweight Diffusion Model for Head and Neck Cancer

Authors: Rashmi Bhaskara, Shravan Bhavsar, Ananth Grama, Oluwaseyi Oderinde, Shourya Verma

Affiliation: Purdue University

Abstract Preview: Generating Synthetic Positron Emission Tomography from Computed Tomography using Lightweight Diffusion Model for Head and Neck Cancer
Purpose: To generate synthetic PET tumor avidity segments direc...

High-Fidelity Synthetic CT Generation from CBCT for Dibh Breast Cancer Patients Using Shortest Path Regularization

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....

Image Similarity Measurement Based on Handcrafted and Deep Learning Radiomics

Authors: John Ginn, Chenlu Qin, Deshan Yang

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

Abstract Preview: Purpose: Clinical implementation of auto-segmentation tools has been hindered by poor interpretability and generalizability of AI models, necessitating the development of automated contour quality ass...

Impact of Six Degrees of Freedom Intrafraction Motion on Target Coverage for Single-Isocenter Multi-Target Stereotactic Radiosurgery

Authors: Anupama Chundury, Hyejoo Kang, John C. Roeske, Iris A. Rusu

Affiliation: Loyola University Medical Center, 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

Abstract Preview: Purpose: To quantify the dosimetric impact of six degrees of freedom (6DoF) intrafractional errors on gross tumor volume (GTV) and planning target volumes (PTV) for single-isocenter multi-target stere...

Initial Three-Year Machine Performance Assessment of a Superficial Radiation Therapy Unit

Authors: Vibha Chaswal, Stephen D. Davis, Alonso N. Gutierrez, Noah S. Kalman, Yongsook C. Lee, William Romaguera, Ranjini P. Tolakanahalli, D. Jay J. Wieczorek

Affiliation: Miami Cancer Institute, Baptist Health South Florida

Abstract Preview: Purpose: To report machine performance of our superficial radiation therapy unit (SRT-100 Vision, Sensus Healthcare, Boca Raton, FL) for the first three years after commissioning of the unit.
Metho...

Inter-Machine Harmonization in Echocardiographic Videos for Predicting Left Ventricular Ejection Fraction

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...

Knowledge-Based Deep Residual U-Net for Synthetic CT Generation Using a Single MR Volume for Frameless Radiosurgery

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...

Lu-177 Recovery Coefficients Using Tc-99m Surrogate Method: Intra- & Inter-Scanner Comparison

Authors: Ben Piacitelli, Celeste Winters

Affiliation: OHSU, Department of Diagnostic Radiology, Oregon Health & Science University

Abstract Preview: Purpose: To compare recovery coefficients measured with Lu-177 and Tc-99m with the goal of assessing accuracy of the Tc-99m surrogate method and comparing RC reproducibility between scanners of the sa...

Motion Correction-Driven Patient-Specific 2D Cine MRI-Based Dynamic Volumetric Reconstruction for MRI-Guided Radiotherapy Intra-Fractional Motion Monitoring

Authors: Karyn A Goodman, Yang Lei, Tian Liu, D. Michael Lovelock, Charlotte Elizabeth Read, Jing Wang, Jiahan Zhang

Affiliation: Icahn School of Medicine at Mount Sinai

Abstract Preview: Purpose: Real-time volumetric MRI is essential for precise motion management in MRI-guided radiotherapy (MRIgRT). While 2D Cine MRI offers high temporal resolution for motion tracking, it inherently l...

Multi-Institutional Evaluation of Radiographic Automatic Exposure Control Performance Testing: Methodology, Outcomes, and Failure Rates

Authors: Caroline Cheney, Nicole M. Lafata, Zaiyang Long, Dustin W. Lynch, Erin B. Macdonald, Andy Madore, Douglas E. Pfeiffer, Colin Schaeffer, Alexander W. Scott

Affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, UVA Health, Duke University Health System, Mayo Clinic, Henry Ford Health, Boulder Community Health, Varian, Cedars-Sinai Medical Center

Abstract Preview: Purpose: This work evaluates the significance of performing established radiography automatic exposure control (AEC) tests by comparing testing methodology, outcomes, and failure rates across multiple...

Multi-Path Deep Learning Model for Predicting Post-Radiotherapy Functional Liver Imaging in Patients with Hepatocellular Carcinoma

Authors: Smith Apisarnthanarax, Stephen R. Bowen, Sunan Cui, Jie Fu, Clemens Grassberger, Yulun He, Yejin Kim, Matthew J. Nyflot, Sharon Pai

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

Abstract Preview: Purpose: 99mTc-sulfur colloid SPECT imaging enables quantitative assessment of voxel-wise liver function in patients with hepatocellular carcinoma (HCC). Accurately predicting post-radiotherapy (RT) l...

Multi-Scale, Multi-Task Framework with Jacobian Descent for Multi-Plan Dose Prediction in Sequential Boost Radiotherapy

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...

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 ...

Novel AI-Powered Tool to Objectively Evaluate Brain Dose for Multi-Met Stereotactic Radiosurgery Optimization

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...

Optimization of the U-Net Model for the Radiation Dose Prediction in Lung Cancer RT Plans and Its Uncertainty Quantification

Authors: Ibtisam Almajnooni, Victor Cobilean, Milos Manic, Harindra Sandun Mavikumbure, Elisabeth Weiss, Lulin Yuan

Affiliation: Virginia Commonwealth University

Abstract Preview: Purpose: This study aims to optimize the 3D U-Net architecture for dose prediction in lung cancer radiation therapy (RT) plans, particularly in scenarios with limited clinical data, as well as to quan...

Optimization-Based Image Reconstruction Regularized with Inter-Spectral Structural Similarity for Limited-Angle Dual-Energy Cone-Beam CT

Authors: Chih-Wei Chang, Junbo Peng, Richard L.J. Qiu, Justin Roper, Xiangyang Tang, Tonghe Wang, Huiqiao Xie, Xiaofeng Yang, David Yu

Affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Emory Univ, Emory University, Memorial Sloan Kettering Cancer Center, Department of Radiation Oncology and Winship Cancer Institute, Emory University

Abstract Preview: Purpose: Limited-angle dual-energy (DE) cone-beam CT (CBCT) is considered a promising solution to achieve fast and low-dose DE imaging on current CBCT scanners without hardware modification. However, ...

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...

Patient-Specific Ultra-Sparse k-Space Reconstruction Using Motion Decomposition and Sinusoidal Representation Networks for Dynamic Volumetric MRI in Radiotherapy

Authors: Karyn A Goodman, Yang Lei, Tian Liu, Charlotte Elizabeth Read, Jing Wang, Qian Wang, Jiahan Zhang

Affiliation: Icahn School of Medicine at Mount Sinai, Department of Radiation Oncology, Beth Israel Deaconess Medical Center

Abstract Preview: Purpose: Accurate motion management in MRI-guided radiotherapy (MRIgRT) relies on real-time volumetric MRI to track intra-fractional anatomical changes. Dense k-space sampling, while capable of produc...

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...

Predicting Brain V60% in Linac-Based Single-Isocenter-Multiple-Targets (SIMT) Stereotactic Radiosurgery Using Machine Learning

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...

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 and Monitoring Response to Head and Neck Cancer Radiotherapy Using Multi-Modality Imaging and Radiobiological Digital Twin Simulations

Authors: Eric Aliotta, Michalis Aristophanous, Joseph O. Deasy, Bill Diplas, Milan Grkovski, James Han, Vaios Hatzoglou, Jeho Jeong, Nancy Y Lee, Ramesh Paudyal, Nadeem Riaz, Heiko Schoder, Amita Shukla-Dave

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

Abstract Preview: Purpose: To forecast radiotherapy treatment response for head and neck cancer (HNC) using multimodality imaging and personalized radiobiological modeling.
Methods: Multi-modality imaging data from ...

Prospective Organ-Level Dose Estimation in CT Imaging Using Scout-Net: A Comparison with Established Methods

Authors: Maria Jose Medrano, Grant Stevens, Liyan Sun, Justin Ruey Tse, Adam S. Wang, Sen Wang

Affiliation: Department of Radiology, Stanford University, GE HealthCare, Stanford University

Abstract Preview: Purpose: Patient exposure to ionizing radiation is a major concern in CT imaging. Size-specific dose estimation methods can prospectively estimate organ-level radiation doses based on patient sizes an...

Quantitative Accuracy in Iodine Measurements and CT Numbers Using Standard and Ultra-High Resolution Photon-Counting CT in Coronary CTA

Authors: Afrouz Ataei, Victor Moy, Mark P. Supanich

Affiliation: Rush University

Abstract Preview: Purpose: To evaluate iodine quantification and CT number accuracy in coronary CT angiography (CTA) using standard and ultra-high resolution (UHR) modes of photon-counting CT (PCCT) across different do...

Rapid CBCT Imaging with Ultra-Sparse X-Ray Projections for Head & Neck Cancer Radiotherapy

Authors: Hania A. Al-Hallaq, Chih-Wei Chang, Anees H. Dhabaan, Yuan Gao, Shaoyan Pan, Junbo Peng, Richard L.J. Qiu, Keyur Shah, Sibo Tian, Zhen Tian, Xiaofeng Yang, David Yu, Jun Zhou

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

Abstract Preview: Purpose: Traditional cone-beam computed tomography (CBCT) often requires multiple angular projections, increasing radiation exposure and extending scanning times, which may lead to heightened patient ...

Structure-Based Diffusion Model for CT Synthesis from MR Images for Radiotherapy Treatment Planning

Authors: Samuel Kadoury, Redha Touati

Affiliation: Polytechnique Montréal

Abstract Preview: Purpose:
Generating synthetic CT images from MR acquisitions for radiotherapy planning allows to integrate soft tissue contrast alongside density information stemming from CT, thus improving tumor ...

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...

The Development of a Novel Biomechanical Model for Accurate Contour Deformation during Online Adaptative Metastatic Bone Cancer Radiotherapy Planning.

Authors: Jeremy S. Bredfeldt, Benito De Celis Alonso, Braian Adair Maldonado Luna, Kevin M. Moerman, Gerardo Uriel Perez Rojas, René Eduardo Rodríguez-Pérez, Kamal Singhrao

Affiliation: Department of Radiation Oncology, Brigham and Women's Hospital, Harvard Medical School, Department of Radiation Oncology, Brigham and Women's Hospital, Dana Farber Cancer Institute, Harvard Medical School, Department of Mechanical Engineering, University of Galway, Faculty of Physics and Mathematics, Benemérita Universidad Autónoma de Puebla

Abstract Preview: Purpose: Online adaptive radiotherapy replanning for single-isocenter bone cancer metastasis treatment reduces on-table treatment time and patient discomfort compared to the multi-isocenter standard-o...

Transformer-Based Proton Dose Prediction with and without Diffusion Process

Authors: Jing Qian, Brandon Reber, David M. Routman, Satomi Shiraishi

Affiliation: Mayo Clinic

Abstract Preview: Purpose: The dose distribution in proton radiotherapy (PRT) is characterized by sharp gradients, posing a challenge for machine learning-based dose prediction. While denoising with diffusion processes...

Ukan Architecture for Voxel-Level Dose Prediction in Radiotherapy

Authors: Lu Jiang, Ke Sheng

Affiliation: Department of Radiation Oncology, University of California at San Francisco, Department of Radiation Oncology, University of California, San Francisco

Abstract Preview: Purpose:
Conventional radiotherapy treatment planning is guided by a set of generic objectives that are unspecific to patient anatomy. Treatment planning thus heavily relies on the planner’s experi...

Uncertainties on Synthetic-CT Generation from CBCT: Another Layer of Complexity in Abdominal Adaptive Radiotherapy

Authors: Laura I. Cervino, Wendy B. Harris, Paulo Quintero, Hao Zhang

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

Abstract Preview: Purpose: To evaluate the impact of the prediction uncertainty from CBCT-based synthetic CT (sCT) generation in abdominal adaptive radiotherapy.

Methods: CT and CBCT images from 65 abdominal pat...

Universal MR-to-Synthetic CT: A Streamlined Framework for MR-Only Radiotherapy Planning

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...

Using Multiple Sequences MRI for Synthesizing CT Based on a Deep Learning Approach

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...