فهرست مطالب h sutanto

Some organs in the body are sensitive to radiation such as eyes, breast, and gonads. Protection of sensitive organs against radiation is necessary. Recently, many sensitive organ shields have been developed from different materials.

The aim of this study is to evaluate the dose reduction and image quality from implementation of Silicone Rubber-Lead (SR-Pb) as an alternative gonad shield in digital radiography (DR).

In this experimental study, the SR-Pb gonad shields with various thicknesses of 2, 4, 6, 8, and 10 mm were synthesized. This study used the Pb percentage of 5 wt%. An anthropomorphic phantom was used in abdomen plain examinations. The results obtained from the use of the SR-Pb was compared with standard gonad shield, i.e. lead apron. To measure the dose reduction, the Piranha detector was used. The image quality assessment was evaluated with the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR).

This study showed the dose reduction was significant for all SR-Pb thicknesses, and incrementally increased with the increase of the SR-Pb thickness. The minimum and maximum of dose reduction were 22.8% for 2 mm and 66.9% for 10 mm SR-Pb, respectively.

Compared to the reference image without gonad shield, the SNR and CNR do not significantly change. Hence, the SR-Pb is probably to be used as an alternative gonad shield.

Noise reduction is a method for reducing CT dose; however, it can reduce image quality. This study aims to propose a selective mean filter (SMF) and evaluate its effectiveness for noise suppression in CT images. This experimental study proposed and implemented the new noise reduction algorithm. The proposed algorithm is based on a mean filter (MF), but the calculation of the mean pixel value using the neighboring pixels in a kernel selectively applied a threshold value based on the noise of the image. The SMF method was evaluated using images of phantoms. The dose reduction was estimated by comparing the image noise acquired with a lower dose after implementing the SMF method and the noise in the original image acquired with a higher dose. For comparison, the images were also filtered with an adaptive mean filter (AMF) and a bilateral filter (BF). The spatial resolution of the image filtered with the SMF was similar to the original images and the images filtered with the BF. While using the AMF, spatial resolution was significantly corrupted. The noise reduction achieved using the SMF was up to 75%, while it was up to 50% using the BF. SMF significantly reduces the noise and preserves the spatial resolution of the image. The noise reduction was more pronounced with BF, and less pronounced with AMF.

The HU linearity is an essential parameter in a quantitative imaging and the treatment planning systems of radiotherapy.

This study aims to evaluate the linearity of Hounsfield unit (HU) in applying the adaptive iterative dose reduction (AIDR) on CT scanner and its comparison to the filtered back-projection (FBP).

In this experimental phantom study, a TOS-phantom was scanned using a Toshiba Alexion 6 CT scanner. The images were reconstructed using the FBP and AIDR. Measurements of HU and noise values were performed on images of the “HU linearity” module of the TOS-phantom. The module had five embedded objects, i.e., air, polypropylene, nylon, acrylic, and Delrin. On each object, a circle area of 4.32 cm2 was drawn and used to measure HU and noise values. The R2 of the relation between mass densities vs. HU values was used to measure HU linearities at four different tube voltages. The Mann-Whitney U test was used to compare unpaired data and p-value < 0.05 was considered statistically significant.

The AIDR method produced a significant smaller image noise than the FBP method (p-value < 0.05). There were no significant differences in HU values of images reconstructed using FBP and AIDR methods (p-value > 0.05). The HU values acquired by the methods showed the same linearity marked by coinciding linear lines with the same R2 value (> 0.999).

AIDR methods produce the HU linearity as FBP methods with a smaller image noise level.