• Korean Journal of Radiology
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• v.22 no.6
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• pp.951-958
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• 2021

Objective: To evaluate the usefulness of virtual monochromatic images (VMIs) obtained using dual-layer dual-energy CT (DL-DECT) for evaluating brain tumors. Materials and Methods: This retrospective study included 32 patients with brain tumors who had undergone non-contrast head CT using DL-DECT. Among them, 15 had glioblastoma (GBM), 7 had malignant lymphoma, 5 had high-grade glioma other than GBM, 3 had low-grade glioma, and 2 had metastatic tumors. Conventional polychromatic images and VMIs (40-200 keV at 10 keV intervals) were generated. We compared CT attenuation, image noise, contrast, and contrast-to-noise ratio (CNR) between tumor and white matter (WM) or grey matter (GM) between VMIs showing the highest CNR (optimized VMI) and conventional CT images using the paired t test. Two radiologists subjectively assessed the contrast, margin, noise, artifact, and diagnostic confidence of optimized VMIs and conventional images on a 4-point scale. Results: The image noise of VMIs at all energy levels tested was significantly lower than that of conventional CT images (p < 0.05). The 40-keV VMIs yielded the best CNR. Furthermore, both contrast and CNR between the tumor and WM were significantly higher in the 40 keV images than in the conventional CT images (p < 0.001); however, the contrast and CNR between tumor and GM were not significantly different (p = 0.47 and p = 0.31, respectively). The subjective scores assigned to contrast, margin, and diagnostic confidence were significantly higher for 40 keV images than for conventional CT images (p < 0.01). Conclusion: In head CT for patients with brain tumors, compared with conventional CT images, 40 keV VMIs from DL-DECT yielded superior tumor contrast and diagnostic confidence, especially for brain tumors located in the WM.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.285-286
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• 1998

Virtual Brain-endoscopy is an effective method to detect lesion in brain. Brain is the most part of the human and is not easy part to operate so that reconstructing in 3D may be very helpful to doctors. In this paper, it is suggested that to increase the reliability, method of matching 3D object with the 2D CT slice. 3D Brain-endoscopy is reconstructed with 35 slices of 2D CT images. There is a plate in 3D brain-endoscopy so as to drag upward or downward to match the relevant 2D CT image. Relevant CT image guides the user to recognize the exact part he or she is investigating. VRML Script is used to make the change in images and PlaneSensor node is used to transmit the y coordinate value with the CT image. The result is test on the PC which has the following spec. 400MHz Clock-speed, 512MB ram, and FireGL 3000 3D accelerator is set up. The VRML file size is 3.83MB. There was no delay in controlling the 3D world and no collision in changing the CT images. This brain-endoscopy can be also put to practical use on medical education through internet.

• Journal of Biomedical Engineering Research
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• v.10 no.3
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• pp.317-322
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• 1989

In this paper, brain CT images are automatically segmented to reconstruct the 3-D scene from consecutive CT sections. Contextual segmentation technique was applied to overcome the partial volume artifact and statistical fluctuation phenomenon of soft tissue images. Images are hierarchically analyzed by region growing and graph editing techniques. Segmented regions are discriptively decided to the final organs by using the semantic informations.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.809-817
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• 2023

Single source and dual source measurements using anthropomorphic phantoms in which the phantoms are lined up in human body equivalents use OSLD (Optically Stimulated Luminescence Dosimeter), so the effective dose is calculated using OSLD. For hospital images, SNR (Signal to Noise Ratio) and CNR (Contrast to Noise Ratio) were measured in MCA (Middle Cerebral Artery) for single source and dual source, and for phantom images, SNR and CNR were measured for brain parenchyma of single source and dual source. For hospital imaging, SNR and CNR were measured in MCA for both single-source and dual-source, and for phantom images, SNR and CNR were measured for brain parenchyma from single-source and dual-source. As a result of comparing the SNR and CNR of the hospital image and the phantom image, there was no statistical difference. Comparing patient doses in hospital images, the effective dose of the dual source was 53.53% less and the effective dose of the dual energy phantom was 57.94% less. The dose can be increased in other areas, but the cerebrovascular area is useful because the dose is small.

• Journal of the Korean Society of Radiology
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• v.9 no.6
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• pp.369-374
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• 2015

In this study we proposed a texture feature analysis algorithm that distinguishes between a normal image and a diseased image using CT images of some brain hemorrhage patients, and generates both Eigen images and test images which can be applied to the proposed computer aided diagnosis system in order to perform a quantitative analysis for 6 parameters. And through the analysis, we derived and evaluated the recognition rate of CT images of brain hemorrhage. As the results of examining over 40 example CT images of brain hemorrhage, the recognition rates representing a specific texture feature-value are as follows: some appeared to be as high as 100% including average gray level, average contrast, smoothness, and Skewness while others showed a little low disease recognition rate: 95% for uniformity and 87.5% for entropy. Consequently, based on this research result, if a software that enables a computer aided diagnosis system for medical images is developed, it will lead to the availability for the automatic detection of a diseased spot in CT images of brain hemorrhage and quantitative analysis. And they can be used as computer aided diagnosis data, resulting in the increased accuracy and the shortened time in the stage of final reading.

• Korean Journal of Artificial Intelligence
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• v.12 no.1
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• pp.1-6
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• 2024

Recently, various techniques are being applied through the development of medical AI, and research has been conducted on the application of super-resolution AI models. In this study, evaluate the results of the application of the super-resolution AI model to brain CT as the basic data for future research. Acquiring CT images of the brain, algorithm for brain and bone windowing setting, and the resolution was downscaled to 5 types resolution image based on the original resolution image, and then upscaled to resolution to create an LR image and used for network input with the original imaging. The SRCNN model was applied to each of these images and analyzed using PSNR, SSIM, Loss. As a result of quantitative index analysis, the results were the best at 256×256, the brain and bone window setting PSNR were the same at 33.72, 35.2, and SSIM at 0.98 respectively, and the loss was 0.0004 and 0.0003, respectively, showing relatively excellent performance in the bone window setting CT image. The possibility of future studies aimed image quality and exposure dose is confirmed, and additional studies that need to be verified are also presented, which can be used as basic data for the above studies.

• Journal of Electrical Engineering and Technology
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• v.10 no.5
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• pp.2197-2204
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• 2015

In recent years, medical image diagnosis has growing significant momentous in the medicinal field. Brain and lung image of patient are distorted with salt and pepper noise is caused by moving the head and chest during scanning process of patients. Reconstruction of these images is a most significant field of diagnostic evaluation and is produced clearly through techniques such as linear or non-linear filtering. However, restored images are produced with smaller amount of noise reduction in the presence of huge magnitude of salt and pepper noises. To eliminate the high density of salt and pepper noises from the reproduction of images, a new efficient fuzzy based median filtering algorithm with a moderate elapsed time is proposed in this paper. Reproduction image results show enhanced performance for the proposed algorithm over other available noise reduction filtering techniques in terms of peak signal -to -noise ratio (PSNR), mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), image enhancement factor (IMF) and structural similarity (SSIM) value when tested on different medical images like magnetic resonance imaging (MRI) and computer tomography (CT) scan brain image and CT scan lung image. The introduced algorithm is switching filter that recognize the noise pixels and then corrects them by using median filter with fuzzy two-sided π- membership function for extracting the local information.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.150-158
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• 2021

Intracranial hemorrhage (ICH) refers to acute bleeding inside the intracranial vault. Not only does this devastating disease record a very high mortality rate, but it can also cause serious chronic impairment of sensory, motor, and cognitive functions. Therefore, a prompt and professional diagnosis of the disease is highly critical. Noninvasive brain imaging data are essential for clinicians to efficiently diagnose the locus of brain lesion, volume of bleeding, and subsequent cortical damage, and to take clinical interventions. In particular, computed tomography (CT) images are used most often for the diagnosis of ICH. In order to diagnose ICH through CT images, not only medical specialists with a sufficient number of diagnosis experiences are required, but even when this condition is met, there are many cases where bleeding cannot be successfully detected due to factors such as low signal ratio and artifacts of the image itself. In addition, discrepancies between interpretations or even misinterpretations might exist causing critical clinical consequences. To resolve these clinical problems, we developed a diagnostic model predicting intracranial bleeding and its subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and epidural) by applying deep learning algorithms to CT images. We also constructed a visualization tool highlighting important regions in a CT image for predicting ICH. Specifically, 1) 27,758 CT brain images from RSNA were pre-processed to minimize the computational load. 2) Three different CNN-based models (ResNet, EfficientNet-B2, and EfficientNet-B7) were trained based on a training image data set. 3) Diagnosis performance of each of the three models was evaluated based on an independent test image data set: As a result of the model comparison, EfficientNet-B7's performance (classification accuracy = 91%) was a way greater than the other models. 4) Finally, based on the result of EfficientNet-B7, we visualized the lesions of internal bleeding using the Grad-CAM. Our research suggests that artificial intelligence-based diagnostic systems can help diagnose and treat brain diseases resolving various problems in clinical situations.

• Journal of Biomedical Engineering Research
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• v.39 no.3
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• pp.116-123
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• 2018

This study is a model experimental study using a phantom to propose an optimized brain CT scan protocol that can reduce the radiation dose of a patient and remain quality of image. We investigate the CT scan parameters of brain CT in clinical medical institutions and to measure the important parameters that determine the quality of CT images. We used 52 multislice spiral CT (SOMATOM Definition AS+, Siemens Healthcare, Germany). The scan parameters were tube voltage (kVp), tube current (mAs), scan time, slice thickness, pitch, and scan field of view (SFOV) directly related to the patient's exposure dose. The CT dose indicators were CTDIvol and DLP. The CT images were obtained while increasing the imaging conditions constantly from the phantom limit value (Q1) to the maximum value (Q4) for AAPM CT performance evaluation. And statistics analyzed with Pearson's correlation coefficients. The result of tube voltage that the increase in tube voltage proportionally increases the variation range of the CT number. And similar results were obtained in the qualitative evaluation of the CT image compared to the tube voltage of 120 kVp, which was applied clinically at 100 kVp. Also, the scan conditions were appropriate in the tube current range of 250 mAs to 350 mAs when the tube voltage was 100 kVp. Therefore, by applying the proposed brain CT scanning parameters can be reduced the radiation dose of the patient while maintaining quality of image.

• The Korean Journal of Nuclear Medicine Technology
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• v.19 no.2
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• pp.68-73
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• 2015

Purpose The purpose of this study was to evaluate image quality change by truncated region in field of view (FOV) of attenuation correction computed tomography (AC-CT) in brain PET/CT. Materials and Methods Biograph Truepoint 40 with TrueV (Siemens) was used as a scanner. $^{68}Ge$ phantom scan was performed with and without applying brain holder using brain PET/CT protocol. PET attenuation correction factor (ACF) was evaluated according to existence of pallet in FOV of AC-CT. FBP, OSEM-3D and PSF methods were applied for PET reconstruction. Parameters of iteration 4, subsets 21 and gaussian 2 mm filter were applied for iterative reconstruction methods. Window level 2900, width 6000 and level 4, 200, width 1000 were set for visual evaluation of PET AC images. Vertical profiles of 5 slices and 20 slices summation images applied gaussian 5 mm filter were produced for evaluating integral uniformity. Results Patient pallet was not covered in FOV of AC-CT when without applying brain holder because of small size of FOV. It resulted in defect of ACF sinogram by truncated region in ACF evaluation. When without applying brain holder, defect was appeared in lower part of transverse image on condition of window level 4200, width 1000 in PET AC image evaluation. With and without applying brain holder, integral uniformities of 5 slices and 20 slices summation images were 7.2%, 6.7% and 11.7%, 6.7%. Conclusion Truncated region by small FOV results in count defect in occipital lobe of brain in clinical or research studies. It is necessary to understand effect of truncated region and apply appropriate accessory for brain PET/CT.