• Journal of radiological science and technology
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• v.33 no.4
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• pp.355-362
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• 2010

This study determines a range of CT parameter values in Brain CT which are minimizing patient absorption dose without compromising the image quality and optimal exposure condition. We measured dose and image noise using conventional CT parameters in Brain CT. In additon, we evaluated dose, SNR and PSNR of head phantom images while changing kVp and rotation time. In this study, effectiveness of dose that was achieved from dose reproducible experiments in conventional head CT condition is determined by changing kVp and rotation time. Dose and PSNR is related to low dose-high resolution condition. In conclusion, we suggest that using proposed conditions is effective for imaging to compare with conditions proposed by the manufacturer.

• Journal of the Korean Society of Radiology
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• v.14 no.7
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• pp.991-1001
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• 2020

Supervised deep learning technologies for improving the image quality of computed tomography (CT) need a lot of training data. When input images have different characteristics with training images, the technologies cause structural distortion in output images. In this study, an imaging model based on the deep reinforcement learning (DRL) was developed for overcoming the drawbacks of the supervised deep learning technologies and reducing noise in CT images. The DRL model was consisted of shared, value and policy networks, and the networks included convolutional layers, rectified linear unit (ReLU), dilation factors and gate rotation unit (GRU) in order to extract noise features from CT images and improve the performance of the DRL model. Also, the quality of the CT images obtained by using the DRL model was compared to that obtained by using the supervised deep learning model. The results showed that the image accuracy for the DRL model was higher than that for the supervised deep learning model, and the image noise for the DRL model was smaller than that for the supervised deep learning model. Also, the DRL model reduced the noise of the CT images, which had different characteristics with training images. Therefore, the DRL model is able to reduce image noise as well as maintain the structural information of CT images.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.285-295
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• 2023

With the development of technology, efforts to reduce the exposure dose received by patients in CT scans are continuing with the development of new reconstruction techniques. Recently, deep learning reconstruction techniques have been developed to overcome the limitations of repetitive reconstruction techniques. This study aims to evaluate the usefulness of images according to reconstruction techniques in pediatric chest CT images. Patient study conducted a study on 85 pediatric patients who underwent chest CT scan at P-Hospital in Gyeongsangnam-do from January 1, 2021 to December 31, 2022. The phantom used in the Phantom Study is the Pediatrics Whole Body Phantom PBU-70. After the test, the images were reconstructed with FBP, ASIR-V (50%) and DLIR (TF-Medium, High), and the images were evaluated by obtaining SNR and CNR values by setting ROI of the same size. As a result, TF-H of deep learning reconstruction techniques had the lowest noise value compared to ASIR-V (50%) and TF-M in all experiments, and SNR and CNR had the highest values. In pediatric chest CT scans, TF images with deep learning reconstruction techniques were less noisy than ASiR-V images with adaptive statistical iterative reconstruction techniques, CNR and SNR were higher, and the quality of images was improved compared to conventional reconstruction techniques.

• The Korean Journal of Nuclear Medicine Technology
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• v.17 no.2
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• pp.67-71
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• 2013

Purpose: The Change of CT exposure condition have a effect on image quality and patient exposure dose. In this study, we evaluated effect CT image quality and SUV when CT parameters (Pitch, Rotation time) were changed. Materials and Methods: Discovery Ste (GE, USA) was used as a PET/CT scanner. Using GE QA Phantom and AAPM CT Performance Phantom for evaluate Noise of CT image. Images are acquired by using 24 combinations that four stages pitch (0.562, 0.938, 1.375, 1.75:1) and six stages X-ray tube rotation time (0.5s-1.0s). PET images are acquired using 1994 NEMA PET Phantom ($^{18}F-FDG$ 5.3 kBq/mL, 2.5 min/frame). For noise test, noise are evaluated by standard deviation of each image's CT numbers. And then we used expectation noise according to change of DLP (Dose Length Product) to experimental noise ratio for index of effectiveness. For spatial resolution test, we confirmed that it is possible to identify to 1.0 mm size of the holes at the AAPM CT Performance Phantom. Finally we evaluated each 24 image's SUV. Results: Noise efficiency were 1.00, 1.03, 1.01, 0.96 and 1.00, 1.04, 1.02, 0.97 when pitch changes at the QA Phantom and AAPM Phantom. In case of X-ray tube rotation time changes, 0.99, 1.02, 1.00, 1.00, 0.99, 0.99 and 1.01, 1.01, 0.99, 1.01, 1.01, 1.01 at the QA Phantom and AAPM Phantom. We could identify 1.0 mm size of the holes all 24 images. Also, there were no significant change of SUV and all image's average SUV were 1.1. Conclusion: 1.75:1 pitch is the most effective value at the CT image evaluation according to pitch change and It doesn't affect to the spatial resolution and SUV. However, the change of rotation time doesn't affect anything. So, we recommend to use the effective pitch like 1.75:1 and adequate X-ray tube rotation time according to patient size.

• Journal of radiological science and technology
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• v.40 no.4
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• pp.589-594
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• 2017

The objective of this study was to evaluate the enhancement effects of the quantum denoising system (QDS) on brain CT images. This retrospective study was conducted with 45 adults who visited G Radiology located in Gyungbuk for having brain CT tests between Jul 2017 and Oct 2017 after receiving consents. Subjects were divided into a control group (A group; no QDS(-) application during the brain CT test) and a treatment group (B Group; QDS(+) application during the brain CT test). The following conclusions were obtained from the study. The noise values at the Pons part and the Vermis part were significantly (p<0.05) lower in B Group ($Pons=5.41{\pm}1.05HU$; $Vermis=5.28{\pm}0.73HU$) than A Group ($Pons=6.92{\pm}0.98HU$; Vermis=6.72). The SNR values at the Pons part and the Vermis part were significantly (p<0.05) higher in B Group ($Pons=7.28{\pm}2.56$; $Vermis=8.63{\pm}3.04$) than A Group ($Pons=5.21{\pm}1.28$; $Vermis=6.23{\pm}1.49$). In conclusion, the results of this study suggested that the application of QDS to the brain CT test would enhance the signal to noise ratio (SNR) and the contrast to noise ratio (CNR) to provide an image more appropriate for diagnosis.

• Journal of the Korean Society of Radiology
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• v.14 no.7
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• pp.981-989
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• 2020

In this study, a self-made customized phantom was used to quantitatively measure the change in CT number and noise according to the change of pitch. In order to acquire an image using the phantom, the inside of the phantom was filled with sterile distilled water. Inside the glass tube, a solution obtained by diluting the ratio of normal saline and contrast medium to 100%(NS), 400:1, 200:1, 100:1, 50:1, respectively, was placed and imaged. At this time, the pitch was divided into steps of 0, 0.35, 0.7, 1.05, and 1.4 for each dilution ratio of the solution and imaged, respectively. One-way ANOVA analysis were performed to verify whether the mean of the CT number and noise values measured in all ROIs by dilution ratio showed a significant difference according to the change in pitch. As a result of the experiment, there was no statistically significant difference in the change of the CT number according to the change in the pitch for each dilution ratio, but the noise value tended to increase with the increase of the pitch, and showed a statistically significant difference. In the spiral image acquisition of CT, noise can be changed to a significant level depending on the pitch. Therefore, it will be necessary to set the quality evaluation items and criteria for CT images using the spiral image acquisition method.

• Journal of the Korean Society of Radiology
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• v.17 no.7
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• pp.1025-1031
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• 2023

We aimed to evaluate the radiation dose and image quality by changing the Scout view voltage in low-dose chest CT (LDCT) and applying scan parameters such as AEC (auto exposure control) and ASIR (adaptive statistical iterative reconstruction) to find the optimal protocol. Scout view voltage was varied at 80, 100, 120, 140 kV and after measuring the dose 5 times using the existing low-dose chest CT protocol, the appropriate kV was selected for the study using the Dose report provided by the equipment. After taking a basic LDCT shot at 120 kV, 30 mAs, ASIR 50% was applied to this condition. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were assessed by measuring Background noise (B/N). For dose comparison, CTDI_vol and DLP provided by the equipment were compared and analyzed using the formulas. The results indicated that the protocol of scout 140 + LDCT + ASIR 50 + AEC reduced radiation exposure and improved image quality compared to traditional LDCT, providing an optimal protocol. As demonstrated in the experiment, LDCT screenings for asymptomatic normal individuals are crucial, as they involve concerns over excessive radiation exposure per examination. Therefore, applying appropriate parameters is important, and it is expected to contribute positively to the public health in future LDCT based health screenings.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.47-53
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• 2023

Devices such as X-Ray, CT, MRI, scanners, etc. can generate S&P noise from several sources during the image acquisition process. Since S&P noise appearing in the image degrades the image quality, it is essential to use noise reduction technology in the image processing process. Various methods have already been proposed in research on S&P noise removal, but all of them have a problem of generating residual noise in an environment with high noise density. Therefore, this paper proposes a filtering algorithm based on a three-dimensional plane equation by setting the grayscale value of the image as a new axis. The proposed algorithm subdivides the local mask to design the three closest non-noisy pixels as effective pixels, and applies cosine similarity to a region with a plurality of pixels. In addition, even when the input pixel cannot form a plane, it is classified as an exception pixel to achieve excellent restoration without residual noise.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.819-825
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• 2023

In this study, among chest CT examination conditions, the tube voltage was changed to 100 and 80 kVp and the reconstruction algorithm was changed to FBP, ASIR-V, and DLIR to compare and analyze changes in examination dose and image quality. As a result, when applying ASIR-V and DLIR at a tube voltage of 100 kVp, which is lower than the existing tube voltage, the dose is lowered while achieving image quality most similar to that when applying 120 kVp and FBP. especially, DLIR reconstructed images had excellent SNR and CNR at all tube voltages. In addition, the SSIM index was analyzed to be closest to 1, showing the highest similarity to the original image. Therefore, when performing repeated chest CT examinations, the application of DLIR can reduce the examination dose by about 29.7%, which is expected to help solve some of the biggest problems with CT examinations, namely radiation exposure due to the examination.

• Journal of the Korean Society of Radiology
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• v.17 no.1
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• pp.141-148
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• 2023

The algebraic reconstruction technique is one of the reconstruction methods in CT and shows good image quality against noise-dominant conditions. The number of iteration is one of the key factors determining the execution time for the algebraic reconstruction technique. However, there are some rules for determining the number of iterations that result in more than a few hundred iterations. Thus, the rules are difficult to apply in practice. In this study, we proposed a method to determine the number of iterations for practical applications. The reconstructed image quality shows slow convergence as the number of iterations increases. Image quality 𝜖 < 0.001 was used to determine the optimal number of iteration. The Shepp-Logan head phantom was used to obtain noise-free projection and projections with noise for 360, 720, and 1440 views were obtained using Geant4 Monte Carlo simulation that has the same geometry dimension as a clinic CT system. Images reconstructed by around 10 iterations within the stop condition showed good quality. The method for determining the iteration number is an efficient way of replacing the best image-quality-based method, which brings over a few hundred iterations.