ArticleRadiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology2019
4D liver tumor localization using cone-beam projections and a biomechanical model.
Article in Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 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
- Real-Time Dose-Guided Radiation Therapy.International journal of radiation oncology, biology, physics · 2025Review
- A conditional point cloud diffusion model for deformable liver motion tracking via a single arbitrarily-angled x-ray projection.Physics in medicine and biology · 2025Article
- Real-time liver motion estimation via deep learning-based angle-agnostic X-ray imaging.Medical physics · 2023Article
- Real-time liver tumor localization via combined surface imaging and a single x-ray projection.Physics in medicine and biology · 2023Article
- Dynamic cone-beam CT reconstruction using spatial and temporal implicit neural representation learning (STINR).Physics in medicine and biology · 2023Article
- 2D/3D Non-Rigid Image Registration via Two Orthogonal X-ray Projection Images for Lung Tumor Tracking.Bioengineering (Basel, Switzerland) · 2023Article
- Real-time liver tumor localization via a single x-ray projection using deep graph neural network-assisted biomechanical modeling.Physics in medicine and biology · 2022Article
- Automatic liver tumor localization using deep learning-based liver boundary motion estimation and biomechanical modeling (DL-Bio).Medical physics · 2021Article
- An unsupervised 2D-3D deformable registration network (2D3D-RegNet) for cone-beam CT estimation.Physics in medicine and biology · 2021Article
- Medical physics challenges in clinical MR-guided radiotherapy.Radiation oncology (London, England) · 2020Review
- Enhancing liver tumor localization accuracy by prior-knowledge-guided motion modeling and a biomechanical model.Quantitative imaging in medicine and surgery · 2019Article
- Advanced 4-dimensional cone-beam computed tomography reconstruction by combining motion estimation, motion-compensated reconstruction, biomechanical modeling and deep learning.Visual computing for industry, biomedicine, and art · 2019Article
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Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
Abstract
purposeTo improve the accuracy of liver tumor localization, this study tests a biomechanical modeling-guided liver cone-beam CT (CBCT) estimation (Bio-CBCT-est) technique, which generates new CBCTs by deforming a prior high-quality CT or CBCT image using deformation vector fields (DVFs). The DVFs can be used to propagate tumor contours from the prior image to new CBCTs for automatic 4D tumor localization. METHODS/MATERIALS: To solve the DVFs, the Bio-CBCT-est technique employs an iterative scheme that alternates between intensity-driven 2D-3D deformation and biomechanical modeling-guided DVF regularization and optimization. The 2D-3D deformation step solves DVFs by matching digitally reconstructed radiographs of the 3D deformed prior image to 2D phase-sorted on-board projections according to imaging intensities. This step's accuracy is limited at low-contrast intra-liver regions without sufficient intensity variations. To boost the DVF accuracy in these regions, we use the intensity-driven DVFs solved at higher-contrast liver boundaries to fine-tune the intra-liver DVFs by finite element analysis-based biomechanical modeling. We evaluated Bio-CBCT-est's accuracy with seven liver cancer patient cases. For each patient, we simulated 4D cone-beam projections from 4D-CT images, and used these projections for Bio-CBCT-est based image estimations. After Bio-CBCT-est, the DVF-propagated liver tumor/cyst contours were quantitatively compared with the manual contours on the original 4D-CT 'reference' images, using the DICE similarity index, the center-of-mass-error (COME), the Hausdorff distance (HD) and the voxel-wise cross-correlation (CC) metrics. In addition to simulation, we also performed a preliminary study to qualitatively evaluate the Bio-CBCT-est technique via clinically acquired cone beam projections. A quantitative study using an in-house deformable liver phantom was also performed.
resultsUsing 20 projections for image estimation, the average (±s.d.) DICE index increased from 0.48 ± 0.13 (by 2D-3D deformation) to 0.77 ± 0.08 (by Bio-CBCT-est), the average COME decreased from 7.7 ± 1.5 mm to 2.2 ± 1.2 mm, the average HD decreased from 10.6 ± 2.2 mm to 5.9 ± 2.0 mm, and the average CC increased from -0.004 ± 0.216 to 0.422 ± 0.206. The tumor/cyst trajectory solved by Bio-CBCT-est matched well with that manually obtained from 4D-CT reference images.
conclusionsBio-CBCT-est substantially improves the accuracy of 4D liver tumor localization via cone-beam projections and a biomechanical model.
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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.