Objective To analyze scanned image optimization based on the multifractal soectrum and image fractal algorithm of 64-slice spiral CT in brain of infant. Methods The image data of Toshiba Aquilion 64-slice CT scanning using 10 mAs were imported to image processing toolboxs of Matlab 7.1. The evaluation of muhifractal spectrum and image denosing were performed, and compared with image quality of conventional low-dose CT using 50 mAs. Results The low-contrast scanned image used 10 mAs is the valueless medical image because of serious noise. Image denoise based on the fractal model had superior characteristic of image detail preserving and better contrast-to-noise ratio(CNR). There existed a group difference in the score of image quality between the rude imaging noise and optimized image based on the muhffraetal spectrum algorithm, though the score was still significantly lower than the normal dosage scanned image(F = 38.85, P < 0.01). The group difference was also manifested the image quality of infants can achieve basieaUy the request of clinical diagnosis by suitable model denoising algorithm. Conclusions Image denoising based on the multifraetal spectrum model can be used on the low-dose and low-contrast CT image optimization. It improved the CNR of the pathological region. The radiation dose of CT scanning in infants would be declined significantly by its further application in the future.

Objective To learn the impacts of major diagnosis, other diagnoses, major surgery and other surgeries on the grouping of DRGs, and to optimize the DRGs data grouping quality by analyzing the main influencing factors of DRGs.Methods Based on regrouping results of the 1 940 questionable cases which have been corrected, using SPSS 13.0 to study the impacts of the questions found on the grouping of DRGs.Results 438 Patient records affecting DRGs grouping were regrouped according to major diagnosis, other diagnoses, major surgery and other surgeries.Influences of the above four groups on the grouping vary in general For comparison between two groups, P＞0.007 1 between main surgery and other surgeries;while P＜0.007 1 for comparison between other groups.Conclusion The highest influence of grouping was found in questionable major diagnosis, much higher than such other factors as other diagnoses, major surgery and other surgeries.This conclusion conforms to the steps of basic DRGs grouping logic-sorting the eases according to major diagnosis first of all Correct naming of surgery influences DRGs grouping, but the influence extent of major surgery and other surgeries is close.