TIGRE: a MATLAB-GPU toolbox for CBCT image reconstruction
2023年12月13日 · 本文介绍了基于层析迭代GPU的重建 (TIGRE)工具箱，这是一个用于快速准确重建3D X射线图像的MATLAB/ CUDA 工具箱。 其中一个关键特征是实现了各种各样的迭代算法和FDK，包括SART族中的一系列算法，Krylov子空间族和使用全变分正则化的一系列方法。 此外，该工具箱具有 GPU加速 投影和使用最新技术的反投影，并且它具有模块化设计，便于新算法的实现。 我们概述了创建工具箱时使用的结构和技术，并提供了两个使用示例。 TIGRE工具箱 …
[2102.00590] Deep learning based CT-to-CBCT deformable image ...
2021年2月1日 · The purpose of this study is to develop a deep learning based method that can automatically generate segmentations on cone-beam CT (CBCT) for head and neck online adaptive radiation therapy (ART),...
CBCT-to-CT synthesis using a hybrid U-Net diffusion model based …
17 小时之前 · Cone-beam computed tomography (CBCT) scans are widely used for real time monitoring and patient positioning corrections in image-guided radiation therapy (IGRT), enhancing the precision of ...
X-ray imaging dose from serial cone-beam CT (CBCT) scans raises a clinical concern in most image guided radiation therapy procedures. It is the goal of this paper to develop a fast...
CBCT reconstruction technique. This paper mainly focuses on: feature-based mesh generation, motion-compensated CBCT image reconstruction, updated motion model estimation, and GPU-based parallel acceleration, which are discussed in the following sections. A. …
Feature-targeted deep learning framework for pulmonary …
The CBCT-to-CT translation network deploys feature-guided Channel-Attention-U-Net and CycleGan for generating high-quality synthesized CT (sCT) imaging from CBCT by using the customized feature-to-feature perceptual loss between the sCT and paired CT images.
Motion-Compensated Mega-Voltage Cone Beam CT Using the …
2012年12月10日 · Abstract: This paper presents a novel method for respiratory motion compensated reconstruction for cone beam computed tomography (CBCT). The reconstruction is based on a time sequence of motion vector fields, which is generated by a dynamic geometrical object shape model.
A Biomechanical Modeling Guided CBCT Estimation Technique
2016年11月1日 · The solved deformation vector fields (DVFs) can also be biomechanically unrealistic. To address these problems, we have developed a biomechanical modeling guided CBCT estimation technique (Bio-CBCT-est) by combining 2D-3D deformation with finite element analysis (FEA)-based biomechanical modeling of anatomical structures.
Respiratory motion prediction based on 4D-CT/CBCT using deep …
Purpose: The purpose is to investigate the feasibility of using Convolutional Neural Network (CNN) to register phase-to-phase deformation vector field (DVF) of lung 4D Computed Tomography (CT) / Cone-Beam Computed Tomography (CBCT). Methods: A Convolutional Neural Network (CNN) based deep learning method was built to directly register the ...
Deformation vector fields (DVF)-driven image reconstruction for 4D-CBCT
The scatter signal level in CBCT projections is much higher than pCT, the SSID metric may not lead to optimal DVF. Objective: To improve the DVF estimation accuracy, we develop a new matching metric that is less sensitive to the intensity level difference caused by the scatter signal.