Image Noise Standard Deviation Research Articles

Evaluation of radiation dose reduction in head CT using the half-dose method

PurposeThe present study introduced the half-dose method (HDM), which halves the radiation dose for conventional head computed tomography (CT), for postoperative hydrocephalus and follow-up for craniosynostosis at a children’s hospital. This study aimed to evaluate the contribution of selective head CT scanning optimization towards the overall reduction of radiation exposure.Materials and methodsWe retrospectively assessed 1227 and 1352 head CT examinations acquired before and after the introduction of the HDM, respectively, in children aged 0–15 years. The radiation exposure was evaluated using the CT dose index volume (CTDI-vol), dose-length product (DLP), rate of HDM introduction, and effect of reducing in-hospital radiation dose before and after the introduction of the HDM. For an objective evaluation of the image quality, head CT scans acquired with HDM and full-dose method (FDM) were randomly selected, and the image noise standard deviation (SD) was measured for each scan. In addition, some HDM images were randomly selected and independently reviewed by two radiologists.ResultsThe HDM was introduced in 27.9% of all head CTs. The mean CTDI-vol of all head CTs was 21.5 ± 6.9 mGy after the introduction, a 14.9% reduction. The mean DLP was 418.4 ± 152.9 mGy.cm after the introduction, a 17.2% reduction. Compared to the FDM images, the noise SD of the HDM ones worsened by almost 0.9; however, none of the images were difficult or impossible to evaluate.ConclusionThe HDM yielded diagnostically acceptable images. In addition, a change in protocol for only two diseases successfully reduced the patients’ overall radiation exposure by approximately 15%. Introducing and optimizing the HDM for frequently performed target diseases will be useful in reducing the exposure dose for the hospital’s patient population.

No-reference image noise estimation based on noise level accumulation

In this paper, a method of no-reference image noise assessment is presented, which utilizes the estimated noise level accumulation (NLA) index value. The affine reconstruction model is applied after segmenting the noisy image into several patches. Boundary blur process is conducted to smooth the segmentation edges. For each image patch the mean value standing for brightness and the standard deviation value indicating the noise standard deviation are computed to give the noise samples estimation. The accurate image noise standard deviation is estimated by integrating NLA index value of several overlapped intervals combined with different visual weights. Experiment results are provided to demonstrate that the proposed method performs well for images with different contents over a large range of noise levels both monotonously and accurately. Comparisons against other conventional approaches are also carried out to exhibit the superior performance of the proposed algorithm.

Fractal-feature distance analysis of contrast-detail phantom image and meaning of pseudo fractal dimension and complexity

The purposes of our studies are to examine whether or not fractal-feature distance deduced from virtual volume method can simulate observer performance indices and to investigate the physical meaning of pseudo fractal dimension and complexity. Contrast-detail (C-D) phantom radiographs were obtained at various mAs values (0.5 - 4.0 mAs) and 140 kVp with a computed radiography system, and the reference image was acquired at 13 mAs. For all C-D images, fractal analysis was conducted using the virtual volume method that was devised with a fractional Brownian motion model. The fractal-feature distances between the considered and reference images were calculated using pseudo fractal dimension and complexity. Further, we have performed the C-D analysis in which ten radiologists participated, and compared the fractal-feature distances with the image quality figures (IQF). To clarify the physical meaning of the pseudo fractal dimension and complexity, contrast-to-noise ratio (CNR) and standard deviation (SD) of images noise were calculated for each mAs and compared with the pseudo fractal dimension and complexity, respectively. A strong linear correlation was found between the fractal-feature distance and IQF. The pseudo fractal dimensions became large as CNR increased. Further, a linear correlation was found between the exponential complexity and image noise SD.