ArticleScientific reports2023
Deep learning synthesis of cone-beam computed tomography from zero echo time magnetic resonance imaging.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 8 citations in OpenAlex.
- Deep learning for dentomaxillofacial cone-beam computed tomography image quality enhancement: A pilot study.Imaging science in dentistry · 2025Article
- Sex estimation with convolutional neural networks using the patella magnetic resonance image slices.Forensic science, medicine, and pathology · 2025Article
- Image quality-based dose optimization in pediatric cone-beam computed tomography: A pilot methodological study.Imaging science in dentistry · 2024Article
- Clinical application of high-resolution spiral CT scanning in the diagnosis of auriculotemporal and ossicle.BMC medical imaging · 2024Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cone-beam computed tomography (CBCT) produces high-resolution of hard tissue even in small voxel size, but the process is associated with radiation exposure and poor soft tissue imaging. Thus, we synthesized a CBCT image from the magnetic resonance imaging (MRI), using deep learning and to assess its clinical accuracy. We collected patients who underwent both CBCT and MRI simultaneously in our institution (Seoul). MRI data were registered with CBCT data, and both data were prepared into 512 slices of axial, sagittal, and coronal sections. A deep learning-based synthesis model was trained and the output data were evaluated by comparing the original and synthetic CBCT (syCBCT). According to expert evaluation, syCBCT images showed better performance in terms of artifacts and noise criteria but had poor resolution compared to the original CBCT images. In syCBCT, hard tissue showed better clarity with significantly different MAE and SSIM. This study result would be a basis for replacing CBCT with non-radiation imaging that would be helpful for patients planning to undergo both MRI and CBCT.
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