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Results for "denoising low": 17 found

An Adaptive Non-Local Means Filtering Method for Denoising CBCT Images Under Low X-Ray Fluence Conditions

Authors: Cem Altunbas, Farhang Bayat

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

Abstract Preview: Purpose:
Low dose imaging and scatter rejection hardware in CBCT reduces X-ray fluence incident on the imager, increasing noise, causing photon starvation artifacts in CBCT images. In this work, we...

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...

Comparative Study between Sparse Primary Sampling Grid Scatter Correction and Low-Count Monte Carlo-Based Scatter Reduction with 3-D Richardson-Lucy Denoising

Authors: Alan Rui Li, Qihui Lyu, Dan Ruan, Ke Sheng

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:
The Sparse Primary Sampling (SPS) grid was shown in a previous computational study to improve image quality by correcting scatter-induced effects and artifacts in Cone-beam Computed Tomogr...

Deep Autoencoder for Ring Artifact Denoising in Photon-Counting CT

Authors: Magdalena Bazalova-Carter, James Day, Xinchen Deng

Affiliation: University of Victoria

Abstract Preview: Purpose:
Ring artifacts in Photon-Counting Computed Tomography (PCCT) images can degrade image quality. this study aims to suppress ring artifacts with a novel autoencoder-based framework that leve...

Denoising Diffusion-Weighted Images of Rectal Cancer Acquired on a 0.35 T Magnetic Resonance Imaging-Guided Linear Accelerator Using Singular Value Decomposition

Authors: Jacqueline M. Andreozzi, Tess Armstrong, Shiva Bhandari, John M Bryant, Jessica M Frakes, David J. Gladstone, Sarah E Hoffe, Kujtim Latifi, Arash Naghavi, Steven Nichols, Ibrahim M. Oraiqat, Russell Palm, Gage H. Redler, Stephen A Rosenberg, Joseph Weygand

Affiliation: Department of Radiation Oncology and Applied Science, Dartmouth Health, Thayer School of Engineering, Dartmouth College, Department of Radiation Oncology, Ohio State University, Moffitt Cancer Center, Department of Radiation Oncology, Moffitt Cancer Center

Abstract Preview: Purpose: The MR-linac provides unique opportunities for integrating advanced imaging into radiotherapy workflows, but the lower sensitivity of systems like the 0.35T model can pose challenges for diff...

Detector Physics-Incorporated Diffusion Denoising Models for Digital Breast Tomosynthesis with Dual-Layer Flat Panel Detectors

Authors: Alexander Bookbinder, Matthew Tivnan, Xiangyi Wu, Wei Zhao

Affiliation: Stony Brook Medicine, Massachusetts General Hospital

Abstract Preview: Purpose: To investigate and benchmark a system-adaptive diffusion-based digital breast tomosynthesis (DBT) denoising model for a direct-indirect dual-layer flat panel detector (DI-DLFPD) with a k-edge...

Development of an Ultrafast MR Technique to Detect Linac Radiation Pulses for Biological Imaging on a Clinical 0.35T MR-Linac

Authors: Pierre Gardair, Johnathan E Leeman, Claire Keun Sun Park, Atchar Sudhyadhom

Affiliation: Brigham and Womenโ€™s Hospital, Dana Farber Cancer Institute, Harvard Medical School, Brigham and Womenโ€™s Hospital and Dana Farber Cancer Institute, Harvard Medical School,, Brigham and Womenโ€™s Hospital and Dana Farber Cancer Institute, Harvard Medical School, Harvard Medical School

Abstract Preview: Purpose: Dose is a poor surrogate for radiobiological damage but no in vivo technology exists to directly measure damage such as DNA strand breaks and free radical generation (FRG). Recent advances in...

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...

Efficient Monte Carlo Proton Dose Calculation Using Denoising Diffusion Probabilistic Models

Authors: Chieh-Ya Chiu, Shen-Hao Li, Hsin-Hon Lin, Shu-Wei Wu

Affiliation: Department of Medical Imaging and Radiological Sciences, Chang Gung University, Proton and Radiation Therapy Center, Chang Gung Memorial Hospital Linkou

Abstract Preview: Purpose: Monte Carlo simulation enables precise calculation of dose distribution in proton therapy through tracing the radiation particles with patient tissues. However, achieving clinical-level preci...

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 Radiation Oncology Imaging with a Novel Variational Model Decomposition, Radon Transformation, and Kohonen Self-Organizing Map Denoising Framework

Authors: Hassan Bagher-Ebadian, Justine M. Cunningham, Anthony J. Doemer, Mohammad M. Ghassemi, Joshua P. Kim, Benjamin Movsas, Kundan S Thind

Affiliation: Michigan State University, Henry Ford Health

Abstract Preview: Purpose: Reduction of noise in medical images critically enables improved accuracy in delineating tumors and organs at risk, leading to more precise treatment planning and safer image-guided radiation...

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...

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...

Rapid Reconstruction of Extremely Accelerated Liver 4D MRI Via Chained Iterative Refinement

Authors: Mary Feng, Yi Lao, Hui Lin, Hengjie Liu, Xin Miao, Michael Ohliger

Affiliation: University of California, Los Angeles, Department of Radiation Oncology, University of California San Francisco, Department of Radiation Oncology, City of Hope National Medical Center, University of California San Francisco, Siemens Medical Solutions USA Inc.

Abstract Preview: Purpose: 4D MRI with high spatiotemporal resolution is vital to characterize the tumor/tumor motion for liver radiotherapy. However, high-quality 4D MRI requires an impractically long scanning time fo...

Real Time Monte Carlo Dose Calculation for Clinical Cyberknife Radiation Therapy Based on Deep Learning Diffusion Model

Authors: Ruiyan Du, He Huang, Mingzhu Li, Ying Li, Hongyu Lin, Wei Liu, Shihuan Qin, Yiming Ren, Hui Xu, Lian Zhang, Xiao Zhang, Zunhao Zhang

Affiliation: Department of Radiation Oncology, Mayo Clinic, 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, Department of Oncology, The First Hospital of Hebei Medical University

Abstract Preview: Purpose: Monte Carlo (MC) dose calculation is the gold standard in clinical CyberKnife radiation therapy (RT), considering its steep dose gradients and high-freedom non-coplanar beam angles, but extre...

The Z Dimension Matters: 3D Noise Power Spectrum of a Clinical Photon Counting Detector CT System

Authors: Frank F. Dong, Megan C. Jacobsen, Ke Li, Xinming Liu, Humberto Monsivais, John Rong

Affiliation: Purdue University, UT MD Anderson Cancer Center

Abstract Preview: Purpose: To investigate the noise characteristics of a clinical photon counting detector CT (PCD-CT) system along axial and through-plane (Z) dimensions using 3D NPS measurements.
Methods: This stu...

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...