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The primary purpose of this paper is to provide a novel wavelet-domain method for digital radiography with low dose examination. Approach of this study is an improved wavelet-transform-based method for potentially reducing radiation dose while maintaining clinically acceptable image quality. The wavelet algorithm integrates the advantages of wavelet-coefficient-weighted method and the existing Bayes Shrink thresholding method. In order to confirm the usefulness of the proposed method, the resolving and noise characteristics of the processed computed radiography images were measured. In addition, variations of contrast and noise with respect to radiation dose were also examined. Finally, to verify the effect of clinical examination, visual evaluations were also performed in lower abdominal area using phantom. Our quantitative results demonstrated that our wavelet algorithm could improve resolution characteristics while keeping the noise level within acceptable limits. Visual evaluation result demonstrated that the proposed method was superior to other published methods. Our proposed method recognized effect on decreasing in exposure dose in lower abdominal radiographs. As a conclusion, our proposed method’s performance is better when compared with that of the 3 conventional methods. The proposed method has the potential to improve visibility in radiographs when a lower radiation dose is applied.

The issue of radiation dose exposure to patients from digital radiography is a major public health concern. In particular, it is important to keep radiation dose exposure to a minimum in female patients during their reproductive period, who frequently undergo repeated radiation exposure during the course of diagnostic imaging and treatment follow-up.

It is known that a trade-off exists between noise level and radiation dose. On the one hand, high-dose radiation will lower the noise level, but may expose the patient to excessive radiation. On the other hand, low-dose radiation will lower the signal-to-noise ratio of the image and result in reducing the amount of image information. The balancing of radiation dose and image quality should be performed precisely to ensure that patient doses are kept at a reasonable minimum, while maintaining clinically acceptable image quality. To address this issue, much research, including the development of new detectors and image processing methods [

Conventional radiography is widely used for the pelvis and lumbar spine. However, the radiation dose for pelvic and lumbar x-ray examinations using a radiograph is relatively high in order to obtain acceptable image quality. An effort to reduce the exposure dose can have a positive effect on a patient’s quality of life.

In this study, we propose an improved wavelet-transform-based method for potentially reducing the radiation dose while maintaining clinically acceptable image quality. The proposed method integrates the advantages of our previously proposed wavelet-coefficient-weighted method [

The main steps of the proposed method include a previously reported wavelet coefficient adjustment technique for contrast enhancement [

graphic images starts by decomposition of the original image by use of the discrete wavelet transform, which results in obtaining different detail wavelet coefficients (horizontal, vertical, diagonal). The three detail coefficients are then processed by use of a sigmoid-type transfer curve for adjustment of wavelet coefficient, followed by Bayes Shrink thresholding.

A sigmoid-type transfer curve with a one-to-one mapping function is used for enhancement of image contrast. The mapping function was determined based on the following considerations: a) in the case of detail compo- nents at a specific level, high-value coefficients are weighted because they carry effective information; b) the coefficients at low levels are heavily weighted, because they carry detailed information, such as edge information; and c) the approximation coefficients are not manipulated in order to prevent image distortion.

In the wavelet decomposition of level j, the sigmoid-type transfer curves of wavelet coefficients can be expressed as follows:

where _{0}, b_{0}, and c_{0} are

constants. In practice, Equation (2) is used as the mapping function instead of Equation (1). In Equation (2), the values of the coefficients are expressed in terms of percentage for the ease of computation.

Here,

expressed in terms of percentage. The reason for utilization of percentage is that by doing so the constants a, b, and c could be used independent of image characteristics. The constants a, b, and c are determined depending on the extent of enhancement. In this study, the value of a was computed by use of Equation (3):

where N represents the maximum decomposition level. Consequently, the lower the wavelet decomposition level j, the greater the gradient of the transfer curve becomes. The reason for making the value of a dependent on decomposition levels is that the wavelet coefficients at low-decomposition levels, which mainly contain information about edges, are highly weighted. The constant c was determined by use of Equation (4):

where d is a constant used for determining the inflection point of the sigmoid curve, and b represents a constant used for determining the gradient of the sigmoid curve. The values of b and d used in this study were 20 and 25, respectively [

The wavelet transform, due to its excellent localization property, has become an indispensable image-denoising tool during the past two decades. Wavelet de noising attempts to remove the noise present in an image while preserving the image characteristics. Wavelet thresholding, first proposed by Donoho [

Various threshold selection methods have been proposed, such as Visu Shrink [

In the Bayes Shrink scheme, the threshold is determined for each sub and by assuming a generalized Gaussian distribution (GGD) [_{B}, is given as

where σ^{2} is the noise variance and σ_{X} the signal standard deviation. Suppose that the signal and the noise are independent of each other. The estimated variance of the observed image,

The estimated variance of signal

A robust estimator of the noise variance is obtained by

where M is the median value of the absolute wavelet coefficients in the first decomposition level. A detailed explanation of the Bayes Shrink method is given in [

Images that were used for measurement of physical characteristics were acquired with use of a multipurpose phantom [

(a) (b) (c)

XG-1, Fuji Photo Film, Tokyo, Japan) and an imaging plate (ST-V_{N}, Fuji Photo Film, Tokyo, Japan) were used in this study. A pixel size of 0.1 mm and a quantization level of 10 bits were employed for data acquisition. The system parameter settings for the latitude (L) and sensitivity (S) were fixed at 3 and 200, respectively. Images were taken with a radiation quality of RQA-5 (HVL = 7.1 mm Al, 21 mm Al additional filtration) by using a tungsten target x-ray tube (Hitachi, Tokyo, Japan). The focal spot size of the x-ray tube was 0.6 mm. The source- to-image receptor distance was 190 cm. The amount of exposure was 4.63 × 10^{−7} C/kg (50 mAs). Twenty phantom images were obtained and used for measuring the presampled modulation transfer function (MTF), noise power spectrum (NPS), and gray level contrast (GLC).

Four different radiation levels were used for investigating the effect of the physical characteristics on the radiation dose. The 4 radiation level ratios with respect to the reference level, 4.63 × 10^{−7} C/kg, were 50/100, 64/ 100, 80/100, and 100/100.

A visual evaluation of wavelet-processed images of a human body phantom was performed to confirm the effectiveness of the proposed method in reducing radiation dose. An anterior-posterior (AP) projection of the hip joint and a lateral view of the lumbar spine on the human body phantom were exposed to various dose levels. These two images were also taken at four different radiation level ratios, 50/100, 64/100, 80/100, and 100/100, instead of the reference level that is commonly used in clinical radiology practice. In this study, the hip joint phantom was exposed at 70 kVp and 32 mAs, and the lumbar phantom at 82 kV and 64 mAs.

The presampled MTFs were measured with an angled-edge method [^{2}. The direction of the edge was oriented with a small angle (2˚ - 3˚). The edge spread function (ESF) in the direction perpendicular to the edge was then obtained. To reduce the noise in the edge profile, 20 representations of the sampled ESFs were generated from the region of interest (ROI). Then the ESFs were differentiated to obtain the line spread functions (LSFs), and the presampled MTFs were deduced by applying Fourier transformation to the LSFs [

NPS measurements were made by exposure of the imaging plate to a uniform beam of radiation. For determination of the NPS, a two-dimensional 2nd-order polynomial was fitted and subtracted to remove background trends. For the calculation, the central portion of each uniform image obtained was divided into 4 non-overlap- ping regions, 256 × 256 in size. A total of 80 regions were used. The NPS was calculated by applying the fast Fourier transform to each of the ROIs and then averaging the resulting spectrum estimates [

A commercially available Burger phantom (Kyoto Kagaku, Kyoto, Japan) was employed for measurement of GLC characteristics. In this study, the GLC was used to describe the relative contrast of an image, defined by

where L_{acrylic}, L_{BG}, and L_{D} represent the mean pixel value of an 8.0 mm diameter circle of an acrylic disk 8.0 mm in thickness, the mean pixel value of the background, and the gray level of the CR, respectively. The GLC ranged from 0 to 1.0. Image contrasts with different gray levels could be compared because the GLC was normalized by (L_{D} ? 1). Low GLC corresponded to low contrast, while high GLC corresponded to high contrast. For clarifying the effect of the radiation dose on the GLC, dose ratios ranging between 100/100 and 50/100, instead of the standard dose, were measured.

In order to validate the superiority and effectiveness of the proposed method, the proposed method and 3 conventional methods, namely, the Wiener filter (WF) [

Method | Parameters |
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Wiener Filter (WF) Bayes Shrink (Bay) Sigmoid (Sig) Proposed (Pro) | kernel Size = 5 × 5 Daubechies, Order = 4, Level = 4 Daubechies, Order = 4, Level = 4 Daubechies, Order = 4, Level = 4 |

WF used in this study was a pixelwise adaptive WF given as

where f(i, j) and g(i, j) are the pixel values of pixel (i, j) in the input image and the output image, respectively. μ and σ^{2} are the local mean and local variance, respectively, in the N-by-M local neighborhood of pixel (i, j) in the input image. M × N is the kernel size of the local region (a 5 × 5 matrix was used in this study as shown in ^{2} is the noise variance, which is the average of all the local variances in this study. Where the local image variance was large, the WF performed somewhat smoothly. Where the local image variance was small, the WF performed even more smoothly.

The proposed method and the above-described three methods were applied to the original images for performance comparison.

A visual evaluation was conducted by five experienced radiological technologists. The images were displayed on a liquid-crystal display (1280 × 1024 matrix, LCD-1980SXi, Nippon Electric Company, Tokyo, Japan). The parameters of window level, window width, and display image size on the image display apparatus were fixed. Each observer reviewed the images independently. The reading time was not limited. The five radiological technologists independently evaluated the total depiction of each phantom image for diagnostic acceptability by using a 5-point grading scale (1, no depiction; 2, faint; 3, acceptable; 4, good; 5, excellent). Statistical analyses were performed with the Friedman test. When a statistically significant difference was found (p < 0.01) in the five images (the original and the four image-processed images) at each dose ratio, pairwise comparisons were performed with Scheffe’s method. Comparisons were made by use of five possible combinations, namely, WF-processed image, Bayes Shrink-processed image, sigmoid-processed image, proposed filter-processed image, and the original image.

^{−1} and 4 mm^{−1} for the original image and the four processed images. Although the trend of the values measured from the proposed-method-processed image is similar to that measured from the original image in dose ratios ranging from 80/100 to 50/100, the NPSs for the proposed method showed improvement in noise level at the dose ratio of 50/100 at the spatial frequency of 4 mm^{−1}.

than those of the other methods tested at all radiation dose ratios. In the case of the lumbar spine, except for the radiation dose ratio of 50/100, the mean grade for the proposed method was higher than those of the other methods tested.

In the case of lumbar radiographs, the results obtained from the proposed method were comparable to those of the original image up to a 64/100 radiation dose ratio. However, the proposed method tended to show unsatisfactory results for a 50/100 radiation dose ratio.

The proposed method provides the benefits of improved resolution and noise suppression. The experimental results demonstrated the method’s effectiveness in dose reduction without degradation in image quality at a lower dose as compared to the standard dose. In the MTF and NPS measurements (

In the GLC measurements (

that contrast was independent of x-ray dose and the proposed method did not contribute to contrast enhancement in the GLC experiment. This result was expected because a thin acrylic phantom was used in the experiment and CR has the characteristic of a linear relationship between pixel value and x-ray dose. However, the contrast of signal edge in a low-dose image of a thick scatterer, such as the human body, will decrease, because low-dose radiation brings about an increase in blur and noise. In this study, the proposed method contributed to the contrast enhancement of signal edge. Our previous study [

In

As shown in

This study does have a limitation. The superiority of the proposed method was not demonstrated for the lateral lumbar spine when the radiation dose ratio was at 64/100 as compared to the standard dose. This issue may be overcome by optimizing parameters, such as b and d in Equation (4). To address this issue, further improvement of the proposed method is still needed.

In this paper, we proposed an improved wavelet-transform-based method for potentially reducing radiation dose while maintaining clinically acceptable image quality. The effectiveness of the proposed method was demonstrated quantitatively and qualitatively. The experimental results showed that the proposed method could improve resolution while keeping noise level within acceptable limits. Furthermore, the results validated the effectiveness of our proposed method in the reduction of radiation dose. Our visual evaluation showed that an approximately 40% - 50% reduction in the exposure dose might be achieved with the proposed method in AP views of hip joint radiographs and lateral views of lumbar spine radiographs. The proposed method has the potential to improve visibility in radiographs when a lower radiation dose is applied.

The authors also would like to thank the observers for their participation in visual evaluation.