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Results for "learning reconstruction": 49 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 Deep Learning Method for Direct Vmi Inference Using a Dual-Layer Radiotherapy Kv-CBCT Imager

Authors: Ross I. Berbeco, Vera Birrer, Raphael Bruegger, Pablo Corral Arroyo, Roshanak Etemadpour, Dianne M. Ferguson, Rony Fueglistaller, Thomas C. Harris, Yue-Houng Hu, Matthew W. Jacobson, Mathias Lehmann, Nicholas Lowther, Daniel Morf, Marios Myronakis

Affiliation: Brigham and Women's Hospital, Harvard Medial School, Dana-Farber Cancer Institute, Department of Radiation Oncology, Dana Farber/Brigham and Women's Cancer Center, Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Brigham and Womens Hospital, Dana Farber Cancer Institute, Harvard Medical School, Brigham and Women's Hospital, Varian Imaging Laboratory, Dana-Farber Cancer Institute

Abstract Preview: Purpose: A challenge for dual energy CBCT is that noise and residual errors in material decomposition steps can become amplified when forming low energy, high contrast virtual mono-energetic images (V...

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

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

Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center

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

A 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 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 ...

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...

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...

Adversarial Diffusion-Based Self-Supervised Learning for High-Resolution MR Imaging

Authors: Zachary Buchwald, Chih-Wei Chang, Zach Eidex, Richard L.J. Qiu, Mojtaba Safari, Shansong Wang, Xiaofeng Yang, David Yu

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

Abstract Preview: Purpose: MRI offers excellent soft tissue contrast for diagnosis and treatment but suffers from long acquisition times, causing patient discomfort and motion artifacts. To accelerate MRI, supervised d...

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 ...

Deep Learning Based Filter with Back-Projection Operator for CT Reconstruction

Authors: Justus Adamson, Mu Chen, Ke Lu, Zhenyu Yang, Fang-Fang Yin, Rihui Zhang, Yaogong Zhang, Haipeng Zhao, Haiming Zhu, Yuchun Zhu

Affiliation: Shanghai Dacheng Medical Technology, Duke University, Medical Physics Graduate Program, Duke Kunshan University, Duke Kunshan University, The First People's Hospital of Kunshan

Abstract Preview: Purpose: In filtered back-projection (FBP) reconstruction, conventional filters often reduce noise at the expense of high-frequency details, leading to structural details loss. To address this limitat...

Deep Learning-Based Eye Monitoring and Tracking System for Ocular Proton Therapy in a Regular Gantry Room

Authors: David H. Abramson, Christopher Barker, Jasmine H. Francis, Meng Wei Ho, Yen-Po Lee, Haibo Lin, Hang Qi, Andy Shim, Charles B. Simone, Weihong Sun, Xiaoxuan Xu, Siyu Yang, Francis Yu, Anna Zhai

Affiliation: College of Machine Intelligence, Nankai University, New York Proton Center, Department of Biomedical Engineering, Johns Hopkins University, Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, Memorial Sloan Kettering Cancer Center

Abstract Preview: Purpose: Proton therapy is an effective modality for treating ocular tumors such as uveal melanoma. We developed a novel camera‐based eye‐tracking system to provide real-time noninvasive eye positioni...

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...

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...

Determine Noise Weighting Factor in Photon-Counting CT Via Deep Learning for Personalized Noise Reduction

Authors: Xinhui Duan, Roderick W. McColl, Mi-Ae Park, Liqiang Ren, Gary Xu, Kuan Zhang, Yue Zhang

Affiliation: UT Southwestern Medical Center, Department of Radiology, UT Southwestern Medical Center, Imaging Services, UT Southwestern Medical Center

Abstract Preview: Purpose:
Image-based deep-learning noise-reduction techniques have been developed for photon-counting CT (PCCT) to improve image quality with reduced radiation dose. The denoising strength is typic...

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...

Development of an Orthogonal X-Ray Projections-Guided Cascading Volumetric Reconstruction and Tumor-Tracking Model for Adaptive Radiotherapy

Authors: Penghao Gao, Zejun Jiang, Huazhong Shu, Linlin Wang, Gongsen Zhang, Jian Zhu

Affiliation: Laboratory of Image Science and Technology, Key Laboratory of Computer Network and Information Integration, Southeast University, 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: We propose a cascading framework for time-varying anatomical volumetric reconstruction and tumor-tracking, guided by onboard orthogonal-view X-ray projections.
Methods: We employe multiple...

Dual-Branch Attention-Driven Network for Enhanced Sparse-View CBCT Reconstruction Using Planning CT As Prior Knowledge

Authors: Xiaoyi Dai, Manju Liu, Weiwei Sang, Pulin Sun, Fan Xia, Zhenyu Yang, Fang-Fang Yin, Chulong Zhang, Rihui Zhang

Affiliation: Jiahui International Hospital, Radiation Oncology, Duke Kunshan University, Medical Physics Graduate Program, Duke Kunshan University

Abstract Preview: Purpose:
Current deep learning-based sparse-view CBCT reconstruction methods are prone to hallucinatory artifacts, as they rely on inferred image details that may not correspond to true anatomical ...

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 3D Volumetric Denoising for Low-Dose CT Images Using Hformer

Authors: Edward Robert Criscuolo, Chenlu Qin, Deshan Yang, Zhendong Zhang

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

Abstract Preview: Purpose:
Low-dose CT (LDCT) imaging minimizes radiation exposure but introduces significant noise, compromising image quality. While deep learning-based denoising models such as HFormer achieve sta...

Enhancing T2-Weighted Brain MRI Resolution across Orientations Using AI-Based Volumetric Reconstruction

Authors: Mengqi Shen, Meghna Trivedi, Tony J.C. Wang, Andy (Yuanguang) Xu, Yading Yuan

Affiliation: Columbia University Medical Center, Dept of Med Hematology & Oncology, Data Science Institute at Columbia University, Columbia University Irving Medical Center, Department of Radiation Oncology, Columbia University Irving Medical Center

Abstract Preview: Purpose: T2-weighted (T2w) images are critical for identifying pathological changes due to their superior contrast in differentiating tissue types. However, they often lack detailed anatomical resolut...

Evaluation of Nodule Volume Accuracy with Deep Learning-Based Reconstructions on Cdznte Photon-Counting and Energy-Integrating CT

Authors: Gisell Ruiz Boiset, Paulo ROBERTO Costa, Luuk J Oostveen, Elsa Bifano Pimenta, Ioannis Sechopoulos, Alessandra Tomal

Affiliation: Radboud University Medical Center, University of São Paulo (USP), Institute of Physics, Universidade Estadual de Campinas. Instituto de Física Gleb Wataghin

Abstract Preview: Purpose: This study aimed to evaluate the precision and accuracy of volume measurements for solid nodules (SNs) and ground-glass opacities (GGOs) in lung images acquired using energy-integrating CT (E...

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 ...

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 3D Brain in Volume (BRAVO) Images Using Attention-Gated Conditional Gan (AGC-GAN)

Authors: Nan Li, Shouping Xu, Gaolong Zhang, Xuerong Zhang

Affiliation: Department of Radiation Oncology, HeBei YiZhou proton center, School of Physics, Beihang University, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College

Abstract Preview: Purpose:
The 3D BRAVO sequence is an advanced magnetic resonance (MR) technique that allows for image reconstruction at any angle. It offers 1 mm gapless scanning and has a high signal-to-noise rat...

Geometric Alignment of MV-CBCT and Dual-Layer Kv-CBCT Projections Using Deep Learning

Authors: Ross I. Berbeco, Vera Birrer, Raphael Bruegger, Pablo Corral Arroyo, Roshanak Etemadpour, Dianne M. Ferguson, Rony Fueglistaller, Thomas C. Harris, Yue-Houng Hu, Matthew W. Jacobson, Mathias Lehmann, Nicholas Lowther, Daniel Morf, Marios Myronakis

Affiliation: Brigham and Women's Hospital, Harvard Medial School, Dana-Farber Cancer Institute, Department of Radiation Oncology, Dana Farber/Brigham and Women's Cancer Center, Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Brigham and Womens Hospital, Dana Farber Cancer Institute, Harvard Medical School, Brigham and Women's Hospital, Varian Imaging Laboratory, Dana-Farber Cancer Institute

Abstract Preview: Purpose: Applications of combined kV-MV CBCT include metal artifact correction and material identification. Difficulties arise, however, when the imagers have misaligned geometric perspectives of the ...

High-Resolution Limited-Angle CBCT Image Reconstruction for Non-Coplanar Radiation Therapy Via Dual-Domain Ordered-Subset Neural Representation with Prior Embedding (DDOS-NeRP)

Authors: Yu Gao, Lei Xing, Siqi Ye

Affiliation: Department of Radiation Oncology, Stanford University

Abstract Preview: Purpose:
Limited-angle CBCT (LA-CBCT) scans are often the only option for non-coplanar radiation therapy to prevent potential mechanical collisions. However, the consecutive angular occlusion of pr...

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 Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems

Authors: Fang-Fang Yin, Lei Zhang, Yaogong Zhang

Affiliation: Medical Physics Graduate Program, Duke Kunshan University

Abstract Preview: Purpose: Rotational symmetry is an inherent property of many tomography systems (e.g., CT, PET, SPECT), arising from the circular arrangement or rotation of detectors. This study revisits the image re...

Investigate Deep-Learned MRI Reconstruction with Data Consistency Mechanism and Task-Informed Loss

Authors: Mark Anastasio, Hua Li, Zhuchen Shao

Affiliation: Washington University School of Medicine, University of Illinois Urbana-Champaign

Abstract Preview: Purpose: Ill-conditioned reconstruction problems in medical imaging, such as those arising from undersampled k-space data in MRI, can result in degraded image quality and clinical task-orientated perf...

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...

Mask Guided Diffusion Model for Metal Artifacts Reduction

Authors: Shusen Jing, Qihui Lyu, Dan Ruan, Ke Sheng, Qifan Xu

Affiliation: Department of Radiation Oncology, University of California, Los Angeles, University of California San Francisco, Department of Radiation Oncology, University of California, San Francisco

Abstract Preview: Purpose: Metallic implants can significantly distort sinograms, leading to severe artifacts in computed tomography (CT) reconstructions. Reconstructing CT images containing metal is fundamentally an i...

Memory-Efficient Deep Learning for Volumetric Cone-Beam CT Image Reconstruction

Authors: Ziqi Gao, Lei Xing, Siqi Ye, S. Kevin Zhou

Affiliation: Department of Radiation Oncology, Stanford University, University of Science and Technology of China (USTC)

Abstract Preview: Purpose: To address the challenge of high memory usage in volumetric cone-beam CT (CBCT) imaging, we propose a method that combines joint reconstruction and super-resolution for sparsely sampled CBCT ...

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...

Neural Implicit K-Space for Accelerated Patient-Specific Non-Cartesian MRI Reconstruction

Authors: Daniel O Connor, Mary Feng, Hui Lin, Hengjie Liu, Xin Miao, Michael Ohliger, Jess E. Scholey, Ke Sheng, DI Xu, Wensha Yang, Yang Yang

Affiliation: UCSF, University of California, Los Angeles, Department of Radiation Oncology, University of California San Francisco, Department of Radiation Oncology, University of California, San Francisco, Department of Radiation Oncology, University of California at San Francisco, University of San Francisco, Department of Radiology, University of California, San Francisco, University of California San Francisco, Siemens Medical Solutions USA Inc.

Abstract Preview: Purpose: The scanning time for a fully sampled MRI is lengthy. Compressed sensing (CS) has been developed to minimize image artifacts in accelerated scans, but the required iterative reconstruction is...

Nnae: Automating Anomaly Detection and Quality Assurance in Medical Image Segmentation

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

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

Abstract Preview: Purpose:
Deep learning-based automatic medical image segmentation is increasingly employed in clinical practice, significantly reducing manual workload. However, verifying segmentation results rema...

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, ...

Posterior-Mean Diffusion Model for Realistic PET Image Reconstruction

Authors: Osama R. Mawlawi, Yiran Sun

Affiliation: RICE University, UT MD Anderson Cancer Center

Abstract Preview: Purpose: Conventional PET reconstruction methods often produce noisy images with artifacts due to data/model mismatches and inconsistencies. Recently, deep learning-based conditional denoising diffusi...

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...

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 ...

Robustness of Deep Learning-Based Motion Compensated 4D-CBCT Reconstruction to out-of-Distribution Data

Authors: Geoffrey D. Hugo, Eric Laugeman, Thomas R. Mazur, Pamela Samson, Kim A. Selting, Zhehao Zhang

Affiliation: University of Illinois, Washington University in St. Louis School of Medicine, WashU Medicine

Abstract Preview: Purpose: To investigate the robustness of a deep learning (DL)-based 4D-CBCT motion-compensated (MoCo) reconstruction method to out-of-distribution data.
Methods: Our developed 4D-CBCT reconstructi...

Scan Efficiency and Imaging Dose Analysis of Next-Generation Nonstop Gated CBCT for Respiratory Gating Lung Radiotherapy

Authors: Sean L. Berry, Weixing Cai, Laura I. Cervino, Yusuf Emre Erdi, Yabo Fu, Yiming Gao, Daphna Gelblum, Wendy B. Harris, Xiuxiu He, Tianfang Li, Xiang Li, Seng Boh Gary Lim, Jean M. Moran, 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: Gated CBCT (gCBCT) is commonly employed for respiratory gating lung cancer patients to ensure precise patient setup. However, the scan is time-consuming on C-arm linear accelerators (LINAC) d...

Simultaneous Motion Estimation and Image Reconstruction with Spatiotemporal Implicit Neural Representation Initial (STINR-SMEIR) for Gas Bubble Motion Artifact Reduction in on-Board CBCT Imaging

Authors: Hua-Chieh Shao, Shanshan Tang, Jing Wang, Kai Wang, You Zhang

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

Abstract Preview: Purpose: Artifacts caused by gas bubble movement in the gastrointestinal tract can severely degrade the image quality of on-board abdominal cone-beam computed tomography (CBCT), impacting its utility ...

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

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

Affiliation: Mercy Hospital Springfield

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

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...

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 ...

“See” through Surface: Transforming Surface Imaging into a Real-Time Three-Dimensional Imaging Solution for Intra-Treatment Image Guidance

Authors: Steve B. Jiang, Ruiqi Li, Hua-Chieh Shao, Kenneth Westover, You Zhang, Tingliang Zhuang

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

Abstract Preview: Purpose:
Respiratory motion is a long-standing challenge for lung SBRT, particularly for centrally-located lung tumors where increased toxicity demands more precise motion management during treatme...