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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.
This article was published in the following journal.
Name: Journal of radiation research
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To evaluate the use of cone beam computed tomography in treating head and neck to hopefully reduce side effects
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Improvement in the quality of an x-ray image by use of an intensifying screen, tube, or filter and by optimum exposure techniques. Digital processing methods are often employed.
Modality of computed tomography in which the patient is irradiated in a spiral path around the body with a cone or pyramid-shaped beam.
Computed tomography modalities which use a cone or pyramid-shaped beam of radiation.
Improvement of the quality of a picture by various techniques, including computer processing, digital filtering, echocardiographic techniques, light and ultrastructural MICROSCOPY, fluorescence spectrometry and microscopy, scintigraphy, and in vitro image processing at the molecular level.
The attainment or process of attaining a new level of performance or quality.