2017
DOI: 10.1016/j.ijrobp.2017.06.550
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A Leaning-Based Method to Improve Cone Beam CT Image Quality for Adaptive Radiation Therapy

Abstract: Purpose/Objective(s): Quantitative Cone Bean CT (CBCT) imaging is on increasing demand for precise image-guided radiation therapy (RT) because it provides a foundation for advanced image-guided techniques, including accurate treatment setup, online tumor delineation, and patient dose calculation. With more precise treatment monitoring from CBCT, dose delivery errors can be significantly reduced in each fraction or compensated for in subsequent fractions using adaptive RT. However, the current CBCT has severe a… Show more

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Cited by 8 publications
(7 citation statements)
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“…Alternating Regression Forest (ARF) is used in this study in training the regression model. Recent studies showed the efficacy of random forest in tackling medical image processing 25,29,32 . Classical random forest trains a bag of binary decision trees each of which is provided with a random subset of training data and trained independently from the others.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Alternating Regression Forest (ARF) is used in this study in training the regression model. Recent studies showed the efficacy of random forest in tackling medical image processing 25,29,32 . Classical random forest trains a bag of binary decision trees each of which is provided with a random subset of training data and trained independently from the others.…”
Section: Methodsmentioning
confidence: 99%
“…Recently, we developed a novel machine-learning based method to further improve CBCT image quality such that they are comparable to that of pCTs for potential application to CBCT-guided adaptive radiotherapy. 1,25,26 By building a set of paired training images of pCT and CBCT and using the pCT as the learning target, the image quality of CBCT was improved significantly through a learning process. Compared with existing methods, our method not only mitigates non-uniform and streaking artifacts, but also restores true HU values on CBCT images such that the CBCT images after correction share the same calibration as pCTs for dose calculation.…”
Section: Introductionmentioning
confidence: 99%
“…We test the parameter μ from 0.1 to 1.0 by 4‐fold cross‐validation, the performance is stable when μ = 0.5. λ is a balancing parameter in computing the information gain, and is designed based on its performance on balancing the information energy of CT target and MR features . In fact the MR features’ length is much bigger than CT target, which means the MR features have more information energy than CT target.…”
Section: Resultsmentioning
confidence: 99%
“…61 We test the parameter l from 0.1 to 1.0 by 4fold cross-validation, the performance is stable when l = 0.5. k is a balancing parameter in computing the information gain, and is designed based on its performance on balancing the information energy of CT target and MR features. 62 In fact the MR features' length is much bigger than CT target, which means the MR features have more information energy than CT target. Thus, k should be very small, and we set it to 0.05. c is also a balancing parameter to balance the information gain and global loss in optimization of splitting procedure of each decision tree.…”
Section: B Parameter Performancementioning
confidence: 99%
“…Quantitative cone-beam CT (CBCT) imaging is on increasing demand for precise image guided radiation therapy since it provides a foundation for advanced image-guidance techniques, including accurate treatment setup, online tumor delineation and patient dose calculation (1, 2). With more precise treatment monitoring from accurate CBCT images, dose delivery errors can be significantly reduced in each fraction and further compensated for in subsequent fractions using adaptive radiation therapy (3-5). However, the current CBCT imaging has severe artifacts and its current clinical application is therefore limited to patient setup based on only bony structures.…”
Section: Introductionmentioning
confidence: 99%