ArticleIEEE transactions on medical imaging2019
Augmentation of CBCT Reconstructed From Under-Sampled Projections Using Deep Learning.
Article in IEEE transactions on medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- A Framework for Guiding DDPM-Based Reconstruction of Damaged CT Projections Using Traditional Methods.Journal of imaging informatics in medicine · 2026Article
- Low-dose CBCT image reconstruction: a review.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2026Review
- Cross-Domain TransNet for sparse-view CT reconstruction.Frontiers in nuclear medicine · 2026Article
- Prior-adapted progressive time-resolved CBCT reconstruction using a dynamic reconstruction and motion estimation method.Medical physics · 2025Article
- Reducing radiomics errors in nasopharyngeal cancer via deep learning-based synthetic CT generation from CBCT.BMC medical imaging · 2025Article
- [Advances in low-dose cone-beam computed tomography image reconstruction methods based on deep learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
- Artificial intelligence in four-dimensional imaging for motion management in radiation therapy.Artificial intelligence review · 2025Article
- [A dual-domain cone beam computed tomography sparse-view reconstruction method based on generative projection interpolation].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024Article
- Non-coplanar CBCT image reconstruction using a generative adversarial network for non-coplanar radiotherapy.Journal of applied clinical medical physics · 2024Article
- Enhanced Electroacoustic Tomography with Supervised Learning for Real-time Electroporation Monitoring.Precision radiation oncology · 2024Article
- A review on 4D cone-beam CT (4D-CBCT) in radiation therapy: Technical advances and clinical applications.Medical physics · 2024Review
- Image fusion-based low-dose CBCT enhancement method for visualizing miniscrew insertion in the infrazygomatic crest.BMC medical imaging · 2024Article
- Hybrid-supervised deep learning for domain transfer 3D protoacoustic image reconstruction.Physics in medicine and biology · 2024Article
- A 4D-CBCT correction network based on contrastive learning for dose calculation in lung cancer.Radiation oncology (London, England) · 2024Article
- Surrogate-driven respiratory motion model for projection-resolved motion estimation and motion compensated cone-beam CT reconstruction from unsorted projection data.Physics in medicine and biology · 2024Article
- Deep learning-based projection synthesis for low-dose cone-beam computed tomography imaging in image-guided radiotherapy.Quantitative imaging in medicine and surgery · 2024Article
- Radiation-induced acoustic signal denoising using a supervised deep learning framework for imaging and therapy monitoring.Physics in medicine and biology · 2023Article
- Interpretable AI-assisted clinical decision making (CDM) for dose prescription in radiosurgery of brain metastases.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2023Article
- Simulation of a new respiratory phase sorting method for 4D-imaging using optical surface information towards precision radiotherapy.Computers in biology and medicine · 2023Article
- Application of Artificial Intelligence in the Diagnosis, Treatment, and Prognostic Evaluation of Mediastinal Malignant Tumors.Journal of clinical medicine · 2023Review
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6 authors.
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Abstract
Edges tend to be over-smoothed in total variation (TV) regularized under-sampled images. In this paper, symmetric residual convolutional neural network (SR-CNN), a deep learning based model, was proposed to enhance the sharpness of edges and detailed anatomical structures in under-sampled cone-beam computed tomography (CBCT). For training, CBCT images were reconstructed using TV-based method from limited projections simulated from the ground truth CT, and were fed into SR-CNN, which was trained to learn a restoring pattern from under-sampled images to the ground truth. For testing, under-sampled CBCT was reconstructed using TV regularization and was then augmented by SR-CNN. Performance of SR-CNN was evaluated using phantom and patient images of various disease sites acquired at different institutions both qualitatively and quantitatively using structure similarity (SSIM) and peak signal-to-noise ratio (PSNR). SR-CNN substantially enhanced image details in the TV-based CBCT across all experiments. In the patient study using real projections, SR-CNN augmented CBCT images reconstructed from as low as 120 half-fan projections to image quality comparable to the reference fully-sampled FDK reconstruction using 900 projections. In the tumor localization study, improvements in the tumor localization accuracy were made by the SR-CNN augmented images compared with the conventional FDK and TV-based images. SR-CNN demonstrated robustness against noise levels and projection number reductions and generalization for various disease sites and datasets from different institutions. Overall, the SR-CNN-based image augmentation technique was efficient and effective in considerably enhancing edges and anatomical structures in under-sampled 3D/4D-CBCT, which can be very valuable for image-guided radiotherapy.
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