ArticleMedical physics2021
Multitask 3D CBCT-to-CT translation and organs-at-risk segmentation using physics-based data augmentation.
Article in Medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy.Frontiers in radiology · 2024Pooled it
- Deep learning methods for enhancing cone-beam CT image quality toward adaptive radiation therapy: A systematic review.Medical physics · 2022Pooled it
- External evaluation of a deep learning-based algorithm for automated lung tumor and esophagus segmentation on CBCT.Biomedical physics & engineering express · 2026Article
- Contrastive adversarial mix-up learning for unsupervised cross-modality image segmentation.Quantitative imaging in medicine and surgery · 2026Article
- Automatic adaptive radiotherapy triggering based on CBCT using deep learning for esophageal cancer underwent volumetric modulated arc therapy.Journal of applied clinical medical physics · 2026Article
- ART workflow based on CBCT and end-to-end clinical dynamic phantom validation.BMC medical imaging · 2026Article
- Geometry-encoded deep learning (GeoDL) framework for real-time 3D dose verification for online adaptive radiotherapy.Machine learning. Health · 2025Article
- Deep Learning for CT Synthesis in Radiotherapy.Bioengineering (Basel, Switzerland) · 2025Review
- Artificial intelligence for treatment delivery: image-guided radiotherapy.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- Monte Carlo-based simulation of virtual 3 and 4-dimensional cone-beam computed tomography from computed tomography images: An end-to-end framework and a deep learning-based speedup strategy.Physics and imaging in radiation oncology · 2024Article
- SinoSynth: A Physics-Based Domain Randomization Approach for Generalizable CBCT Image Enhancement.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Progressive auto-segmentation for cone-beam computed tomography-based online adaptive radiotherapy.Physics and imaging in radiation oncology · 2024Article
- Uncertainty estimation- and attention-based semi-supervised models for automatically delineate clinical target volume in CBCT images of breast cancer.Radiation oncology (London, England) · 2024Article
- Accurate object localization facilitates automatic esophagus segmentation in deep learning.Radiation oncology (London, England) · 2024Article
- Zero-Shot Medical Image Translation via Frequency-Guided Diffusion Models.IEEE transactions on medical imaging · 2024Article
- Inter-observer variability in library plan selection on iterative CBCT and synthetic CT images of cervical cancer patients.Journal of applied clinical medical physics · 2023Article
- Using RegGAN to generate synthetic CT images from CBCT images acquired with different linear accelerators.BMC cancer · 2023Article
- Improving synthetic CT accuracy by combining the benefits of multiple normalized preprocesses.Journal of applied clinical medical physics · 2023Article
- Improving CBCT image quality to the CT level using RegGAN in esophageal cancer adaptive radiotherapy.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2023Article
- Combining physics-based models with deep learning image synthesis and uncertainty in intraoperative cone-beam CT of the brain.Medical physics · 2023Article
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Authors and funding
7 authors.
Funding
Abstract
purposeIn current clinical practice, noisy and artifact-ridden weekly cone beam computed tomography (CBCT) images are only used for patient setup during radiotherapy. Treatment planning is performed once at the beginning of the treatment using high-quality planning CT (pCT) images and manual contours for organs-at-risk (OARs) structures. If the quality of the weekly CBCT images can be improved while simultaneously segmenting OAR structures, this can provide critical information for adapting radiotherapy mid-treatment as well as for deriving biomarkers for treatment response.
methodsUsing a novel physics-based data augmentation strategy, we synthesize a large dataset of perfectly/inherently registered pCT and synthetic-CBCT pairs for locally advanced lung cancer patient cohort, which are then used in a multitask three-dimensional (3D) deep learning framework to simultaneously segment and translate real weekly CBCT images to high-quality pCT-like images.
resultsWe compared the synthetic CT and OAR segmentations generated by the model to real pCT and manual OAR segmentations and showed promising results. The real week 1 (baseline) CBCT images which had an average mean absolute error (MAE) of 162.77 HU compared to pCT images are translated to synthetic CT images that exhibit a drastically improved average MAE of 29.31 HU and average structural similarity of 92% with the pCT images. The average DICE scores of the 3D OARs segmentations are: lungs 0.96, heart 0.88, spinal cord 0.83, and esophagus 0.66.
conclusionsWe demonstrate an approach to translate artifact-ridden CBCT images to high-quality synthetic CT images, while simultaneously generating good quality segmentation masks for different OARs. This approach could allow clinicians to adjust treatment plans using only the routine low-quality CBCT images, potentially improving patient outcomes. Our code, data, and pre-trained models will be made available via our physics-based data augmentation library, Physics-ArX, at https://github.com/nadeemlab/Physics-ArX.
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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.