Abstract
PurposeTo evaluate the image quality of low-dose chest digital radiographic images obtained with a new spatial noise reduction algorithm, compared to a conventional de-noising technique.Materials and methodsIn 69 patients, the dose reduction protocol was divided into A, B, and C test groups– 60% (n = 22), 50% (n = 23), and 40% (n = 24) of the baseline dose. In each patient, baseline dose radiographs were obtained with conventional image processing while low-dose images were acquired with new image processing. A set of baseline and low-dose radiographic images per patient was evaluated and scored on a 5-point scale over seven anatomical landmarks (radiolucency of unobscured lung, pulmonary vascularity, trachea, edge of rib, heart border, intervertebral disc space, and pulmonary vessels in the retrocardiac area) and three representative abnormal findings (nodule, consolidation, and interstitial marking) by two thoracic radiologists. A comparison of paired baseline and low-dose images was statistically analyzed using a non-inferiority test based on the paired t-test or the Wilcoxon signed-rank test.ResultsIn A, B, and C test groups, the mean dose reduction rate of the baseline radiation dose was 63.4%, 53.9%, and 47.8%, respectively. In all test groups, the upper limit of the 95% confidence interval was less than the non-inferiority margin of 0.5 every seven anatomical landmarks and three representative abnormal findings, which suggested that the image quality of the low-dose image was not inferior to that of the baseline dose image even if the maximum average dose reduction rate was reduced to 47.8% of the baseline dose.ConclusionIn our study, an image processing technique integrating a new noise reduction algorithm achieved dose reductions of approximately half without compromising image quality for abnormal lung findings and anatomical landmarks seen on chest radiographs. This feature-preserving, noise reduction algorithm adopted in the proposed engine enables a lower radiation dose boundary for the sake of patient’s and radiography technologist’s radiation safety in routine clinical practice, in compliance with regulatory guidelines.
Highlights
According to the European guidelines issued by the Commission of the European Communities (CEC), chest radiography radiation dose criteria are 0.3 mGy based on the entrance surface dose for a standard-sized patient
The upper limit of the 95% confidence interval was less than the non-inferiority margin of 0.5 every seven anatomical landmarks and three representative abnormal findings, which suggested that the image quality of the low-dose image was not inferior to that of the baseline dose image even if the maximum average dose reduction rate was reduced to 47.8% of the baseline dose
The upper limit of the 95% confidence interval was less than the non-inferiority margin of 0.5 every seven anatomical landmarks, and less than 3.5 for the total of the anatomical landmarks, which means that the image quality of the low-dose image was not inferior to that of the baseline dose image even if the maximum average dose reduction rate was reduced to 47.8% of the baseline dose (Fig 3)
Summary
According to the European guidelines issued by the Commission of the European Communities (CEC), chest radiography radiation dose criteria are 0.3 mGy based on the entrance surface dose for a standard-sized patient. In a retrospective study of digital chest radiography, Grewal et al reported that by utilizing additional filtration adding to conventional requirements (at least 2.5 mm Al equivalent), the calculated effective dose (mSv) was reduced by 52% without compromising image quality [4]. Multiscale frequency processing algorithms have been applied to digital chest radiography, improving the low-density structure and achieving better subtle pathological conditions [5,6,7]. Those approaches may not provide optimum noise reduction in the region of locally varying imaging features. We have devised a new spatially-adaptive noise reduction algorithm based on multi-scale noise covariance, including multi-scale frequency processing with a non-local mean method and noise whitening technique
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