ArticleMedical physics2019
Paired cycle-GAN-based image correction for quantitative cone-beam computed tomography.
Article in Medical physics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 119 papers, 3 of them syntheses that pooled it.
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Who cites it
119 citing papers in PubMed, 3 syntheses or guidelines pooled it, 251 citations in OpenAlex.
- Exploring a decade of deep learning in dentistry: A comprehensive mapping review.Clinical oral investigations · 2025Pooled 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
- Knowledge-distilled diffusion models for improving cone-beam CT image quality with meta-learning under imbalanced data.Medical physics · 2026Article
- Synthetic CT generation from cone-beam CT using deep learning for head-and-neck adaptive radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Paired CycleGAN-based virtual staining for 3D X-ray histology of bone-implant systems.Journal of synchrotron radiation · 2026Article
- Building a Clinically Relevant and Technically Robust Synthetic Histopathology Dataset for Breast and Gastric Cancer.Journal of medical systems · 2026Article
- Hip joint image quality screening based on the Diffusion Mamba model.Scientific data · 2026Article
- Deep Learning on Misaligned Dual-Energy Chest X-ray Images Using Paired Cycle-Consistent Generative Adversarial Networks.Journal of imaging informatics in medicine · 2026Article
- ART workflow based on CBCT and end-to-end clinical dynamic phantom validation.BMC medical imaging · 2026Article
- Enhancing CBCT-based CT synthesis using planning MRI in adaptive proton therapy for head cancer: A deep learning approach.Journal of applied clinical medical physics · 2025Article
- Deep Learning for CT Synthesis in Radiotherapy.Bioengineering (Basel, Switzerland) · 2025Review
- Cone Beam Computed Tomography Image-Quality Improvement Using "One-Shot" Super-resolution.Journal of imaging informatics in medicine · 2025Article
- Cone-Beam CT to CT Image Translation Using a Transformer-Based Deep Learning Model for Prostate Cancer Adaptive Radiotherapy.Journal of imaging informatics in medicine · 2025Article
- Evaluation of model uncertainty in AI-based synthetic CT generation from CBCT for abdominal adaptive radiotherapy.Medical physics · 2025Article
- Scatter and beam hardening effect corrections in pelvic region cone beam CT images using a convolutional neural network.Radiological physics and technology · 2025Article
- Design of a Novel Conditional Noise Predictor for Image Super-Resolution Reconstruction Based on DDPM.Journal of imaging · 2025Article
- Unsupervised Bayesian generation of synthetic CT from CBCT using patient-specific score-based prior.Medical physics · 2025Article
- CBCT-to-CT synthesis using a hybrid U-Net diffusion model based on transformers and information bottleneck theory.Scientific reports · 2025Article
- Real-time 3D synthetic MRI based on kV imaging for motion monitoring of abdominal radiotherapy in a conventional LINAC.Physics in medicine and biology · 2025Article
59 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
purposeThe incorporation of cone-beam computed tomography (CBCT) has allowed for enhanced image-guided radiation therapy. While CBCT allows for daily 3D imaging, images suffer from severe artifacts, limiting the clinical potential of CBCT. In this work, a deep learning-based method for generating high quality corrected CBCT (CCBCT) images is proposed.
methodsThe proposed method integrates a residual block concept into a cycle-consistent adversarial network (cycle-GAN) framework, called res-cycle GAN, to learn a mapping between CBCT images and paired planning CT images. Compared with a GAN, a cycle-GAN includes an inverse transformation from CBCT to CT images, which constrains the model by forcing calculation of both a CCBCT and a synthetic CBCT. A fully convolution neural network with residual blocks is used in the generator to enable end-to-end CBCT-to-CT transformations. The proposed algorithm was evaluated using 24 sets of patient data in the brain and 20 sets of patient data in the pelvis. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC) indices, and spatial non-uniformity (SNU) were used to quantify the correction accuracy of the proposed algorithm. The proposed method is compared to both a conventional scatter correction and another machine learning-based CBCT correction method.
resultsOverall, the MAE, PSNR, NCC, and SNU were 13.0 HU, 37.5 dB, 0.99, and 0.05 in the brain, 16.1 HU, 30.7 dB, 0.98, and 0.09 in the pelvis for the proposed method, improvements of 45%, 16%, 1%, and 93% in the brain, and 71%, 38%, 2%, and 65% in the pelvis, over the CBCT image. The proposed method showed superior image quality as compared to the scatter correction method, reducing noise and artifact severity. The proposed method produced images with less noise and artifacts than the comparison machine learning-based method.
conclusionsThe authors have developed a novel deep learning-based method to generate high-quality corrected CBCT images. The proposed method increases onboard CBCT image quality, making it comparable to that of the planning CT. With further evaluation and clinical implementation, this method could lead to quantitative adaptive radiation therapy.
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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.