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- W2063943701 abstract "Background: Imaging modalities in digital radiology produce large amounts of data for which image quality should be determined in order to validate the diagnostic operation. Purpose: To develop an automatic method for image quality assessment. Material and Methods: A filtering algorithm using a moving square mask was applied to create a map of filtered local intensity and noise values. Image quality scores were calculated from the filtered image data. The procedure was applied to technical and anthropomorphic (radiosurgery verification phantom [RSVP] head) phantom images obtained with varying radiation dose, field of view (FOV), and image content. The method was also applied to a clinical computed tomography (CT) brain image. Results: The image quality score ( IQs) of the phantom images increased from 0.51 to 0.82 as the radiation dose (CTDI vol ) increased from 9.2 to 74.3 mGy. Correlation of the IQs with the pixel noise was R 2 = 0.99. The deviation (1 SD) of IQs was 2.8% when the reconstruction FOV was set between 21 and 25 cm. The correlation of IQs with the pixel noise was R 2 = 0.98 with variable image contents and dose. Automatic tube current modulation applied to the RSVP phantom scan reduced the variation in the calculated image quality score by about 60% compared to the use of a fixed tube current. Conclusion: The image quality score provides an efficient tool for automatic quantification of image quality. The presented method also produces a 2D image quality map, which can be used for further image analysis." @default.
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- W2063943701 date "2008-02-01" @default.
- W2063943701 modified "2023-09-25" @default.
- W2063943701 title "Automatic image quality quantification and mapping with an edge-preserving mask-filtering algorithm" @default.
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- W2063943701 doi "https://doi.org/10.1080/02841850701673409" @default.
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