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Results for "synthetic pet": 27 found

A Bayesian Model for the Detection of Local Ventilation Changes in Lung Cancer Patients

Authors: Bas W. Raaymakers, Mario Ries, Paris Tzitzimpasis, Cornel Zachiu

Affiliation: Department of Radiotherapy, University Medical Center Utrecht, University Medical Center Utrecht, UMC Utrecht

Abstract Preview: Purpose: Radiation pneumonitis affects approximately 10-30% of lung cancer patients treated with radiation therapy (RT), posing a significant dose-limiting factor. Recently developed CT-ventilation me...

A Modular Approach to Reversible and Stackable Medical Imaging Translation Models: CBCT-Based Synthetic MRI with Multiple U-Nets in Series (MUNETs)

Authors: Eric Chang, Nguyen Phuong Dang, Andrew Lim, Lauren Lukas, Lijun Ma, Yutaka Natsuaki, Zhengzheng Xu, Hualin Zhang

Affiliation: Radiation Oncology, Keck School of Medicine of USC

Abstract Preview: Purpose: Harnessed the power of AI and Deep Learning (DL), Generalized Neural Network models for medical image transformation are trained to predict target images from reference images, often requirin...

AI-Based Registration-Free 3T T2-Weighted MRI Synthesis Using Truefisp MRI Acquired on a 0.35T MR-Linac System

Authors: Hilary P Bagshaw, Mark K Buyyounouski, Cynthia Fu-Yu Chuang, Yu Gao, Dimitre Hristov, Lianli Liu, Lawrie Skinner, Lei Xing

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

Abstract Preview: Purpose:
MR-guided radiation therapy has introduced a significant leap in cancer treatment by allowing adaptive treatment. The low-field MR-guided system predominantly uses the TrueFISP sequence, w...

An Adaptive Radiotherapy Strategy Study Based on Segmented Synthesis and Deformational Registration

Authors: Jie Hu, Zhengdong Jiang, Nan Li, Tie Lv, Yuqing Xia, Shouping Xu, Gaolong Zhang, Wei Zhao, Changyou Zhong

Affiliation: School of Physics, Beihang University, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Radiotherapy Department of Meizhou People’s Hospital (Huangtang Hospital), UT Health San Antonio, National Cancer Center/ National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, Peopleʼs Republic of China, Department of Radiation Oncology

Abstract Preview: Purpose: Patients usually undergo cone-beam computed tomography (CBCT) scans which are used for patient set-up before radiotherapy. However, the low image quality of CBCT hinders its use in adaptive r...

Assessing the Risks of Synthetic MRI Data in Deep Learning: A Study on U-Net Segmentation Accuracy

Authors: Chuangxin Chu, Haotian Huang, Tianhao Li, Jingyu Lu, Zhenyu Yang, Fang-Fang Yin, Tianyu Zeng, Chulong Zhang, Yujia Zheng

Affiliation: The Hong Kong Polytechnic University, Nanyang Technological University, Australian National University, Medical Physics Graduate Program, Duke Kunshan University, North China University of Technology, Duke Kunshan University

Abstract Preview: Purpose: Deep learning segmentation models, such as U-Net, rely on high-quality image-segmentation pairs for accurate predictions. However, the recent increasing use of generative networks for creatin...

Biomechanically Informed Diagnostic-to-Synthetic CT Transformation for Expedited Radiation Therapy Planning

Authors: Liyuan Chen, Steve Jiang, Chenyang Shen

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

Abstract Preview: Purpose: Delays in radiation therapy (RT) initiation caused by conventional CT simulation processes can hinder timely treatment delivery and patient outcomes. This study proposes a Virtual Treatment S...

CT-Free PET Imaging: Synthetic CT Generation for Efficient and Accurate PET-Based 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:
PET is used in radiotherapy workflows for accurate target delineation. However, a separate CT scan is typically required for attenuation correction in PET imaging and for registering PET-d...

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...

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...

Comparative Analysis of Nine Deep Learning Architectures for Variable Density Grappa 1H Magnetic Resonance Spectroscopy Imaging (MRSI) Reconstruction

Authors: Kimberly Chan, Anke Henning, Mahrshi Jani, Andrew Wright, Xinyu Zhang

Affiliation: Advanced Imaging Research Center (AIRC), UT Southwestern Medical Center

Abstract Preview: Purpose: To evaluate the performance of multiple deep learning architectures for MRSI reconstruction and determine their effectiveness in maintaining high-resolution metabolite mapping while reducing ...

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: ...

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 ...

Evaluating PET-Based Metrics to Identify Patients with Liver Metastases for Biology-Guided Radiotherapy

Authors: Girish Bal, Ke Colin Huang, Christopher F. Njeh, Oluwaseyi Oderinde, Yong Yue

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

Abstract Preview: Purpose: Biology-guided radiotherapy (BgRT) uses real-time positron emission tomography (PET) signals to precisely direct radiotherapy beamlets. The success of BgRT planning and delivery relies on PET...

Fast 3D Scintillation Dosimetry Using Single View Deep Learning Reconstruction

Authors: Louis Archambault, Nicolas Drouin, Alexis Horik, Simon Thibault

Affiliation: Département de Physique, de Génie Physique et D'optique, et Centre de Recherche sur le Cancer, Université Laval, Département de Physique, de Génie Physique et D'optique, et Centre d'optique, photonique et laser, Université Laval

Abstract Preview: Purpose: To develop a novel type of real-time 3D dosimeter for the quality assurance of linear accelerators used in external beam radiotherapy.
Methods: An experimental setup was constructed using ...

Feasibility Study of Deep Learning-Based MRI-to-PET Generation for Rectal Cancer: Overall Survival Prediction and Pathological Complete Response Assessment

Authors: Weigang Hu, Zhenhao Li, Jiazhou Wang, Xiaojie Yin, Zhen Zhang

Affiliation: Fudan University Shanghai Cancer Center

Abstract Preview: Purpose:
This study aims to develop and validate a novel deep learning method to generate synthetic PET images for rectal cancer from MRI data. By incorporating metabolic information from the synth...

Feasibility of Using a Convolutional Neural Network to Predict Physician Evaluation of Synthetic Medical Images

Authors: Sofia Beer, Menal Bhandari, Alec Block, Nader Darwish, Joseph Dingillo, Sebastien A. Gros, Hyejoo Kang, Andrew Keeler, Rajkumar Kettimuthu, Jason Patrick Luce, Ha Nguyen, John C. Roeske, George K. Thiruvathukal, Austin Yunker

Affiliation: Data Science and Learning Division, Argonne National Laboratory, Department of Radiation Oncology, Stritch School of Medicine, Loyola University Chicago, Stritch School of Medicine Loyola University Chicago, Cardinal Bernardin Cancer Center, Loyola University Chicago, Department of Computer Science, Loyola University of Chicago

Abstract Preview: Purpose: Artificial intelligence (AI) generated synthetic medical images are seeing increased use in radiology and radiation oncology. Physician observer studies are an ideal way to evaluate the usabi...

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-Quality Patchnet (HQ-PatchNet) for Synthetic CT Generation in Head & Neck Imaging

Authors: So Hyun Ahn, Chris Beltran, Byongsu Choi, Jeong Heon Kim, Jin Sung Kim, Bo Lu, Justin Chunjoo Park, Bongyong Song, Jun Tan

Affiliation: Mayo Clinic, Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Ewha Medical Research Institute, Ewha Womans University College of Medicine, UC San Diego, Yonsei University College of Medicine

Abstract Preview: Purpose:
Cone-beam computed tomography (CBCT) is widely used in IGRT for patient positioning but suffers from low resolution and poor soft tissue contrast. Synthetic CT (sCT) generated from CBCT ad...

Image Quality Enhancement for Transrectal Ultrasound Imaging of Prostate Brachytherapy Using Deep Learning: A Needle Eraser

Authors: Hilary P Bagshaw, Mark K Buyyounouski, Serdar Charyyev, Xianjin Dai, PhD, Yu Gao, Thomas R. Niedermayr, Lei Xing

Affiliation: Department of Radiation Oncology, Stanford University

Abstract Preview: Purpose: Real-time transrectal ultrasound imaging is the gold standard for needle placement and treatment planning of real-time based-ultrasound-based high dose-rate (HDR) prostate brachytherapy. Cumu...

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...

Optimizing Quality Assurance CT Frequency and Setup Uncertainty in Brain Proton Therapy Patients for Reduced Normal Tissue Dose

Authors: Rachel B. Ger, Heng Li, Anh Tran

Affiliation: Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Johns Hopkins University

Abstract Preview: Purpose: Proton therapy patients undergo quality assurance CT scans (QACTs) during treatment to verify dosimetric accuracy and utilize robustness scenarios for setup and range uncertainties. For intra...

Robust Evaluation of Rotational Uncertainties on Single-Isocenter Multi-Target Stereotactic Radiosurgery Hyperarc Treatment

Authors: Albert Admasu, Kayla Alfonso, Maria de la Luz De Ornelas, Nesrin Dogan, Eric Mellon, Yunze Yang

Affiliation: University of Miami, Department of Radiation Oncology, University of Miami, University of Miami Sylvester Comprehensive Cancer Center

Abstract Preview: Purpose: Rotational uncertainties in single-isocenter multi-target (SIMT) stereotactic radiosurgery (SRS) HyperArc treatments can cause additional target displacement, potentially degrading overall pl...

Skin Lesion Subtype Classification Using Lesion and Border Radiomic Features

Authors: Rituparna Basak, Maede Boroji, Renee F Cattell, Vahid Danesh, Imin Kao, Kartik Mani, Xin Qian, Samuel Ryu, Tiezhi Zhang

Affiliation: Stony Brook Medicine, Stony Brook University, Washington University in St. Louis, Stony Brook University Hospital

Abstract Preview: Purpose: Fundamental qualitative characteristics physicians use to differentiate skin lesion subtypes include asymmetry, border irregularity, and color. Radiomic features have potential to quantify th...

Synthetic CT Generation from a Cycle Diffusion Model Based Framework for Ultrasound-Based Prostate HDR Brachytherapy

Authors: Michael Baine, Charles Enke, Yang Lei, Yu Lei, Ruirui Liu, Su-Min Zhou

Affiliation: Icahn School of Medicine at Mount Sinai, University of Nebraska Medical Center, Department of Radiation Oncology, University of Nebraska Medical Center

Abstract Preview: Purpose: This study presents a framework for generating synthetic CT images using a Cycle Diffusion model, which can be utilized to enhance needle conspicuity in ultrasound-guided prostate HDR brachyt...

Teaching an Old Dog New Tricks: Unlocking Hidden Potential in Existing Frameworks for Versatile Radiotherapy Applications

Authors: Mingli Chen, Xuejun Gu, Hao Jiang, Mahdieh Kazemimoghadam, Weiguo Lu, Qingying Wang, Kangning Zhang

Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Department of Radiation Oncology, Stanford University School of Medicine

Abstract Preview: Purpose:
This work demonstrates how existing software, when creatively adapted, can address a wide range of clinical challenges. By focusing on data exploration and application-specific modificatio...

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...

Validation of Synthetic CT-Based Online Monitoring for Adaptive Proton Therapy

Authors: Ozgur Ates, Chin-Cheng Chen, Chia-Ho Hua, Matthew J. Krasin, Thomas E. Merchant

Affiliation: St. Jude Children's Research Hospital

Abstract Preview: Purpose: To validate the use of synthetic CTs generated from CBCT images for online monitoring, ensuring accurate and reliable daily plan quality assessments in adaptive proton therapy (APT).
Metho...