ArticleJournal of radiation research2018
Image quality improvement in cone-beam CT using the super-resolution technique.
Article in Journal of radiation research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Cone Beam Computed Tomography Image-Quality Improvement Using "One-Shot" Super-resolution.Journal of imaging informatics in medicine · 2025Article
- Efficiency and Applicability of Virtual Surgical Planning in Maxillofacial and Mandibular Bone Reconstruction: A Narrative Review.Clinics and practice · 2025Review
- NRG Oncology and Particle Therapy Co-Operative Group Patterns of Practice Survey and Consensus Recommendations on Pencil-Beam Scanning Proton Stereotactic Body Radiation Therapy and Hypofractionated Radiation Therapy for Thoracic Malignancies.International journal of radiation oncology, biology, physics · 2024Review
- Measuring the binary thickness of buccal bone of anterior maxilla in low-resolution cone-beam computed tomography via a bilinear convolutional neural network.Quantitative imaging in medicine and surgery · 2023Article
- Reference-free learning-based similarity metric for motion compensation in cone-beam CT.Physics in medicine and biology · 2022Article
- A low-cost pathological image digitalization method based on 5 times magnification scanning.Quantitative imaging in medicine and surgery · 2022Article
- Physiologically variable ventilation reduces regional lung inflammation in a pediatric model of acute respiratory distress syndrome.Respiratory research · 2020Article
- Dose estimation of ultra-low-dose chest CT to different sized adult patients.European radiology · 2019Article
- Dose Reduction and Low-Contrast Detectability Using Iterative CBCT Reconstruction Algorithm for Radiotherapy.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study was conducted to improve cone-beam computed tomography (CBCT) image quality using the super-resolution technique, a method of inferring a high-resolution image from a low-resolution image. This technique is used with two matrices, so-called dictionaries, constructed respectively from high-resolution and low-resolution image bases. For this study, a CBCT image, as a low-resolution image, is represented as a linear combination of atoms, the image bases in the low-resolution dictionary. The corresponding super-resolution image was inferred by multiplying the coefficients and the high-resolution dictionary atoms extracted from planning CT images. To evaluate the proposed method, we computed the root mean square error (RMSE) and structural similarity (SSIM). The resulting RMSE and SSIM between the super-resolution images and the planning CT images were, respectively, as much as 0.81 and 1.29 times better than those obtained without using the super-resolution technique. We used super-resolution technique to improve the CBCT image quality.
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