ArticleProceedings of SPIE--the International Society for Optical Engineering2018
Improving Image Quality of Cone-Beam CT Using Alternating Regression Forest.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning methods for enhancing cone-beam CT image quality toward adaptive radiation therapy: A systematic review.Medical physics · 2022Pooled it
- Improving performance of medical image alignment through super-resolution.Biomedical engineering letters · 2023Article
- A Deep Unsupervised Learning Model for Artifact Correction of Pelvis Cone-Beam CT.Frontiers in oncology · 2021Article
- Machine learning in quantitative PET: A review of attenuation correction and low-count image reconstruction methods.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2020Review
- Paired cycle-GAN-based image correction for quantitative cone-beam computed tomography.Medical physics · 2019Article
- Learning-based CBCT correction using alternating random forest based on auto-context model.Medical physics · 2019Article
- Dosimetric study on learning-based cone-beam CT correction in adaptive radiation therapy.Medical dosimetry : official journal of the American Association of Medical DosimetristsArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
We propose a CBCT image quality improvement method based on anatomic signature and auto-context alternating regression forest. Patient-specific anatomical features are extracted from the aligned training images and served as signatures for each voxel. The most relevant and informative features are identified to train regression forest. The well-trained regression forest is used to correct the CBCT of a new patient. This proposed algorithm was evaluated using 10 patients' data with CBCT and CT images. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and normalized cross correlation (NCC) indexes were used to quantify the correction accuracy of the proposed algorithm. The mean MAE, PSNR and NCC between corrected CBCT and ground truth CT were 16.66HU, 37.28dB and 0.98, which demonstrated the CBCT correction accuracy of the proposed learning-based method. We have developed a learning-based method and demonstrated that this method could significantly improve CBCT image quality. The proposed method has great potential in improving CBCT image quality to a level close to planning CT, therefore, allowing its quantitative use in CBCT-guided adaptive radiotherapy.
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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.