AIMC Topic: Four-Dimensional Computed Tomography

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Functional-guided radiotherapy using knowledge-based planning.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Apr 5, 2018
BACKGROUND AND PURPOSE: There are two significant challenges when implementing functional-guided radiotherapy using 4DCT-ventilation imaging: (1) lack of knowledge of realistic patient specific dosimetric goals for functional lung and (2) ensuring co...
Four-Dimensional Computed Tomography Lung Neoplasms Humans Knowledge Bases Organs at Risk Radiotherapy Planning, Computer-Assisted Respiration Radiotherapy Dosage
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Principal component reconstruction (PCR) for cine CBCT with motion learning from 2D fluoroscopy.

Medical physics Dec 11, 2017
PURPOSE: This work aims to generate cine CT images (i.e., 4D images with high-temporal resolution) based on a novel principal component reconstruction (PCR) technique with motion learning from 2D fluoroscopic training images.
Machine Learning Cone-Beam Computed Tomography Movement Four-Dimensional Computed Tomography Image Processing, Computer-Assisted Fluoroscopy Artifacts
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A method to combine target volume data from 3D and 4D planned thoracic radiotherapy patient cohorts for machine learning applications.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Dec 6, 2017
BACKGROUND AND PURPOSE: The gross tumour volume (GTV) is predictive of clinical outcome and consequently features in many machine-learned models. 4D-planning, however, has prompted substitution of the GTV with the internal gross target volume (iGTV)....
Radiotherapy Planning, Computer-Assisted Humans Tumor Burden Observer Variation Four-Dimensional Computed Tomography Lung Neoplasms Machine Learning Carcinoma, Non-Small-Cell Lung Cohort Studies Models, Statistical
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Multiple Kernel Point Set Registration.

IEEE transactions on medical imaging Dec 22, 2015
The finite Gaussian mixture model with kernel correlation is a flexible tool that has recently received attention for point set registration. While there are many algorithms for point set registration presented in the literature, an important issue a...
Databases, Factual Pulmonary Disease, Chronic Obstructive Animals Machine Learning Algorithms Image Processing, Computer-Assisted Models, Theoretical Radiography, Thoracic Normal Distribution Four-Dimensional Computed Tomography Humans
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Comparison of 3D and 4D Monte Carlo optimization in robotic tracking stereotactic body radiotherapy of lung cancer.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] Sep 20, 2014
PURPOSE: To investigate the adequacy of three-dimensional (3D) Monte Carlo (MC) optimization (3DMCO) and the potential of four-dimensional (4D) dose renormalization (4DMCrenorm) and optimization (4DMCO) for CyberKnife (Accuray Inc., Sunnyvale, CA) ra...
Tumor Burden Monte Carlo Method Humans Radiotherapy, Conformal Lung Neoplasms Imaging, Three-Dimensional Radiotherapy Dosage Four-Dimensional Computed Tomography Robotics Radiotherapy Planning, Computer-Assisted
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Deep learning-based fine-grained assessment of aneurysm wall characteristics using 4D-CT angiography.

PeerJ Jan 1, 2025
PURPOSE: This study proposes a novel deep learning-based approach for aneurysm wall characteristics, including thin-walled (TW) and hyperplastic-remodeling (HR) regions.
Neural Networks, Computer Female Aged Humans Computed Tomography Angiography Intracranial Aneurysm Four-Dimensional Computed Tomography Male Middle Aged Deep Learning
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A back propagation neural network based respiratory motion modelling method.

The international journal of medical robotics + computer assisted surgery : MRCAS Jun 1, 2024
BACKGROUND: This study presents the development of a backpropagation neural network-based respiratory motion modelling method (BP-RMM) for precisely tracking arbitrary points within lung tissue throughout free respiration, encompassing deep inspirati...
Movement Four-Dimensional Computed Tomography Algorithms Image Processing, Computer-Assisted Reproducibility of Results Humans Motion Lung Respiration Neural Networks, Computer Artificial Intelligence
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CvTMorph: Improving Local Feature Extraction in Medical Image Registration for Respiratory Motion Modeling with Convolutional Vision Transformer.

Current medical imaging Jan 1, 2024
BACKGROUND: Accurately modeling respiratory motion in medical images is crucial for various applications, including radiation therapy planning. However, existing registration methods often struggle to extract local features effectively, limiting thei...
Lung Image Processing, Computer-Assisted Respiration Humans Neural Networks, Computer Algorithms Movement Four-Dimensional Computed Tomography
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Musculoskeletal CT Imaging: State-of-the-Art Advancements and Future Directions.

Radiology Aug 1, 2023
CT is one of the most widely used modalities for musculoskeletal imaging. Recent advancements in the field include the introduction of four-dimensional CT, which captures a CT image during motion; cone-beam CT, which uses flat-panel detectors to capt...
Lower Extremity Motion Four-Dimensional Computed Tomography Artificial Intelligence Cone-Beam Computed Tomography Humans
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Deep Learning-Based Internal Target Volume (ITV) Prediction Using Cone-Beam CT Images in Lung Stereotactic Body Radiotherapy.

Technology in cancer research & treatment Jan 1, 2022
This study aims to develop a deep learning (DL)-based (Mask R-CNN) method to predict the internal target volume (ITV) in cone beam computed tomography (CBCT) images for lung stereotactic body radiotherapy (SBRT) patients and to evaluate the predictio...
Lung Neoplasms Radiotherapy Planning, Computer-Assisted Respiration Radiosurgery Four-Dimensional Computed Tomography Lung Deep Learning Cone-Beam Computed Tomography Humans
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