ArticlePhysics in medicine and biology2017
Reducing scan angle using adaptive prior knowledge for a limited-angle intrafraction verification (LIVE) system for conformal arc radiotherapy.
Article in Physics in medicine and biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 20 citations in OpenAlex.
- A hybrid population-based and patient-specific framework for 2D-3D deformable registration-driven limited-angle cone-beam CT estimation.Medical physics · 2025Article
- A review on 4D cone-beam CT (4D-CBCT) in radiation therapy: Technical advances and clinical applications.Medical physics · 2024Review
- Enhancement of 4-D Cone-Beam Computed Tomography (4D-CBCT) Using a Dual-Encoder Convolutional Neural Network (DeCNN).IEEE transactions on radiation and plasma medical sciences · 2022Article
- Real-time Markerless Tracking of Lung Tumors based on 2-D Fluoroscopy Imaging using Convolutional LSTM.IEEE transactions on radiation and plasma medical sciences · 2022Article
- Prior image-guided cone-beam computed tomography augmentation from under-sampled projections using a convolutional neural network.Quantitative imaging in medicine and surgery · 2021Article
- A generative adversarial network (GAN)-based technique for synthesizing realistic respiratory motion in the extended cardiac-torso (XCAT) phantoms.Physics in medicine and biology · 2021Article
- Enhancing digital tomosynthesis (DTS) for lung radiotherapy guidance using patient-specific deep learning model.Physics in medicine and biology · 2021Article
- Development of realistic multi-contrast textured XCAT (MT-XCAT) phantoms using a dual-discriminator conditional-generative adversarial network (D-CGAN).Physics in medicine and biology · 2020Article
- Augmentation of CBCT Reconstructed From Under-Sampled Projections Using Deep Learning.IEEE transactions on medical imaging · 2019Article
- Daily edge deformation prediction using an unsupervised convolutional neural network model for low dose prior contour based total variation CBCT reconstruction (PCTV-CNN).Biomedical physics & engineering express · 2019Article
- Predicting real-time 3D deformation field maps (DFM) based on volumetric cine MRI (VC-MRI) and artificial neural networks for on-board 4D target tracking: a feasibility study.Physics in medicine and biology · 2019Article
- Low dose cone-beam computed tomography reconstruction via hybrid prior contour based total variation regularization (hybrid-PCTV).Quantitative imaging in medicine and surgery · 2019Article
- Technical Note: Imaging dose resulting from optimized procedures with limited-angle intrafractional verification system during stereotactic body radiation therapy lung treatment.Medical physics · 2019Article
- First clinical retrospective investigation of limited projection CBCT for lung tumor localization in patients receiving SBRT treatment.Physics in medicine and biology · 2019Article
- Low dose CBCT reconstruction via prior contour based total variation (PCTV) regularization: a feasibility study.Physics in medicine and biology · 2018Article
- Image acquisition optimization of a limited-angle intrafraction verification (LIVE) system for lung radiotherapy.Medical physics · 2018Article
- Markerless Four-Dimensional-Cone Beam Computed Tomography Projection-Phase Sorting Using Prior Knowledge and Patient Motion Modeling: A Feasibility Study.Cancer translational medicine · 2017Article
- Accuracy and Feasibility of Synthetic CT for Lung Adaptive Radiotherapy: A Phantom Study.Technology in cancer research & treatmentArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
The purpose of this study is to develop an adaptive prior knowledge guided image estimation technique to reduce the scan angle needed in the limited-angle intrafraction verification (LIVE) system for 4D-CBCT reconstruction. The LIVE system has been previously developed to reconstruct 4D volumetric images on-the-fly during arc treatment for intrafraction target verification and dose calculation. In this study, we developed an adaptive constrained free-form deformation reconstruction technique in LIVE to further reduce the scanning angle needed to reconstruct the 4D-CBCT images for faster intrafraction verification. This technique uses free form deformation with energy minimization to deform prior images to estimate 4D-CBCT based on kV-MV projections acquired in extremely limited angle (orthogonal 3°) during the treatment. Note that the prior images are adaptively updated using the latest CBCT images reconstructed by LIVE during treatment to utilize the continuity of the respiratory motion. The 4D digital extended-cardiac-torso (XCAT) phantom and a CIRS 008A dynamic thoracic phantom were used to evaluate the effectiveness of this technique. The reconstruction accuracy of the technique was evaluated by calculating both the center-of-mass-shift (COMS) and 3D volume-percentage-difference (VPD) of the tumor in reconstructed images and the true on-board images. The performance of the technique was also assessed with varied breathing signals against scanning angle, lesion size, lesion location, projection sampling interval, and scanning direction. In the XCAT study, using orthogonal-view of 3° kV and portal MV projections, this technique achieved an average tumor COMS/VPD of 0.4 ± 0.1 mm/5.5 ± 2.2%, 0.6 ± 0.3 mm/7.2 ± 2.8%, 0.5 ± 0.2 mm/7.1 ± 2.6%, 0.6 ± 0.2 mm/8.3 ± 2.4%, for baseline drift, amplitude variation, phase shift, and patient breathing signal variation, respectively. In the CIRS phantom study, this technique achieved an average tumor COMS/VPD of 0.7 ± 0.1 mm/7.5 ± 1.3% for a 3 cm lesion and 0.6 ± 0.2 mm/11.4 ± 1.5% for a 2 cm lesion in the baseline drift case. The average tumor COMS/VPD were 0.5 ± 0.2 mm/10.8 ± 1.4%, 0.4 ± 0.3 mm/7.3 ± 2.9%, 0.4 ± 0.2 mm/7.4 ± 2.5%, 0.4 ± 0.2 mm/7.3 ± 2.8% for the four real patient breathing signals, respectively. Results demonstrated that the adaptive prior knowledge guided image estimation technique with LIVE system is robust against scanning angle, lesion size, location and scanning direction. It can estimate on-board images accurately with as little as 6 projections in orthogonal-view 3° angle. In conclusion, adaptive prior knowledge guided image reconstruction technique accurately estimates 4D-CBCT images using extremely-limited angle and projections. This technique greatly improves the efficiency and accuracy of LIVE system for ultrafast 4D intrafraction verification of lung SBRT treatments.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.