• Journal of the Korean Society of Radiology
• /
• v.17 no.3
• /
• pp.297-303
• /
• 2023

Pediatric Computed Tomography (CT) examinations can often result in exam failures or the need for frequent retests due to the difficulty of cooperation from young patients. Deep Learning Image Reconstruction (DLIR) methods offer the potential to obtain diagnostically valuable images while reducing the retest rate in CT examinations of pediatric patients with high radiation sensitivity. In this study, we investigated the possibility of applying DLIR to reduce artifacts caused by respiration or motion and obtain clinically useful images in pediatric chest CT examinations. Retrospective analysis was conducted on chest CT examination data of 43 children under the age of 7 from P Hospital in Gyeongsangnam-do. The images reconstructed using Filtered Back Projection (FBP), Adaptive Statistical Iterative Reconstruction (ASIR-50), and the deep learning algorithm TrueFidelity-Middle (TF-M) were compared. Regions of interest (ROI) were drawn on the right ascending aorta (AA) and back muscle (BM) in contrast-enhanced chest images, and noise (standard deviation, SD) was measured using Hounsfield units (HU) in each image. Statistical analysis was performed using SPSS (ver. 22.0), analyzing the mean values of the three measurements with one-way analysis of variance (ANOVA). The results showed that the SD values for AA were FBP=25.65±3.75, ASIR-50=19.08±3.93, and TF-M=17.05±4.45 (F=66.72, p=0.00), while the SD values for BM were FBP=26.64±3.81, ASIR-50=19.19±3.37, and TF-M=19.87±4.25 (F=49.54, p=0.00). Post-hoc tests revealed significant differences among the three groups. DLIR using TF-M demonstrated significantly lower noise values compared to conventional reconstruction methods. Therefore, the application of the deep learning algorithm TrueFidelity-Middle (TF-M) is expected to be clinically valuable in pediatric chest CT examinations by reducing the degradation of image quality caused by respiration or motion.

• The Journal of the Korea Contents Association
• /
• v.15 no.9
• /
• pp.411-417
• /
• 2015

To distinguish between HCC and HH is one of the important test methods in determining the treatment method by determining the treatment method by distinguishing malignant growth and benign tumors in liver CT scan. Currently, the specialist is reading CT images by their subjective judgment. So, the purpose of this study is to treat reading the CT images even more objective way. The test times after injection contrast medium in this study are the before injection phase(Pre.), artery phase(35sec), portal phase(70sec) and delay phase(180sec). The general pattern change of HCC in change of contrast enhancement pattern shows 26.6% matching. And the case of HH shows 16.6% matching. In order to observe the change of HU value between HCC and HH, each average values and standard deviation was confirm and as a result, it shows the lagre difference between artery and portal phase in lesion.(HCC$19.76{\pm}23.52$, HH$60.23{\pm}29.43$). And it shows the 76.6% matching in HCC and 80.0% matching in HH. Thorough this study, to suggest a HU value as objective analysis method and if the anlaysis method was used in clinical will assist in the diagnosis.

• The Journal of Korean Society for Radiation Therapy
• /
• v.18 no.2
• /
• pp.75-80
• /
• 2006

Purpose: In radiation therapy, precise calculation of dose toward malignant tumors or normal tissue would be a critical factor in determining whether the treatment would be successful. The Radiation Treatment Planning (RTP) system is one of most effective methods to make it effective to the correction of dose due to CT number through converting linear attenuation coefficient to density of the inhomogeneous tissue by means of CT based reconstruction. Materials and Methods: In this study, we carried out the measurement of CT number and calculation of mass density by using RTP system and the homemade inhomogeneous tissue Phantom and the values were obtained with reference to water. Moreover, we intended to investigate the effectiveness and accuracy for the correction of inhomogeneous tissue by the CT number through comparing the measured dose (nC) and calculated dose (Percentage Depth Dose, PDD) used CT image during radiation exposure with RTP. Results: The difference in mass density between the calculated tissue equivalent material and the true value was ranged from $0.005g/cm^3\;to\;0.069g/cm^3$. A relative error between PDD of RTP and calculated dose obtained by radiation therapy of machine ranged from -2.8 to +1.06%(effective range within 3%). Conclusion: In conclusion, we confirmed the effectiveness of correction for the inhomogeneous tissues through CT images. These results would be one of good information on the basic outline of Quality Assurance (QA) in RTP system.

• Journal of Biomedical Engineering Research
• /
• v.20 no.2
• /
• pp.173-182
• /
• 1999

Contrast-enhanced CT has an important role in assessing liver lesions, the optimal protocol to get most effective result is not clear. The mein goal when deciding injention protocol is to optimize lesion detectability with rapid scanning when lesion to liver contrast is maximum. For this purpose, we developed a physiological model of the contrast medium enhancement based on the compartment modeling and pharmacokinetics. Blood supply to liver is achieved in two paths. This dual supply characteristic distinguishes the CT enhancement of liver from that of the other organs. The first path is by hepatic artery and to second, by portal vein. However, it is assumed that only gepatic artery can supply blood to hepatocellular carcinoma(HCC) compartment, thus, the difference of contrast enhancement is resulted between normal liver tissue and hepatic tumor. By solving differential equations for each compartment simultaneously using the computer program Matlab, CT contrast-enhancement curves were simulated. The simulated enhancement curves for aortic, hepatic, portal vein, and HCC compartments were compared with the mean enhancement curves from 24 patients exposed to the same protocols as the simulation. These enhancement curves showed a good agreement. Furthermore, we simulated lesion-to-liver curves for various injection protocols, and the effects were analyzed. The variables to be considered in the injection protocol were injection rate, dose, and concentration of contrast material. These data may help to optimize scanning protocols for better diagnosis.

• The Journal of Korean Society for Radiation Therapy
• /
• v.31 no.1
• /
• pp.75-81
• /
• 2019

Purpose: Helmet type bolus for 3D printer is being manufactured because of the disadvantages of Bolus materials when photon beam is used for the treatment of scalp malignancy. However, PLA, which is a used material, has a higher density than a tissue equivalent material and inconveniences occur when the patient wears PLA. In this study, we try to treat malignant scalp tumors by using M3 wax helmet with 3D printer. Methods and materials: For the modeling of the helmet type M3 wax, the head phantom was photographed by CT, which was acquired with a DICOM file. The part for helmet on the scalp was made with Helmet contour. The M3 Wax helmet was made by dissolving paraffin wax, mixing magnesium oxide and calcium carbonate, solidifying it in a PLA 3D helmet, and then eliminated PLA 3D Helmet of the surface. The treatment plan was based on Intensity-Modulated Radiation Therapy (IMRT) of 10 Portals, and the therapeutic dose was 200 cGy, using Analytical Anisotropic Algorithm (AAA) of Eclipse. Then, the dose was verified by using EBT3 film and Mosfet (Metal Oxide Semiconductor Field Effect Transistor: USA), and the IMRT plan was measured 3 times in 3 parts by reproducing the phantom of the head human model under the same condition with the CT simulation room. Results: The Hounsfield unit (HU) of the bolus measured by CT was $52{\pm}37.1$. The dose of TPS was 186.6 cGy, 193.2 cGy and 190.6 cGy at the M3 Wax bolus measurement points of A, B and C, and the dose measured three times at Mostet was $179.66{\pm}2.62cGy$, $184.33{\pm}1.24cGy$ and $195.33{\pm}1.69cGy$. And the error rates were -3.71 %, -4.59 %, and 2.48 %. The dose measured with EBT3 film was $182.00{\pm}1.63cGy$, $193.66{\pm}2.05cGy$ and $196{\pm}2.16cGy$. The error rates were -2.46 %, 0.23 % and 2.83 %. Conclusions: The thickness of the M3 wax bolus was 2 cm, which could help the treatment plan to be established by easily lowering the dose of the brain part. The maximum error rate of the scalp surface dose was measured within 5 % and generally within 3 %, even in the A, B, C measurements of dosimeters of EBT3 film and Mosfet in the treatment dose verification. The making period of M3 wax bolus is shorter, cheaper than that of 3D printer, can be reused and is very useful for the treatment of scalp malignancies as human tissue equivalent material. Therefore, we think that the use of casting type M3 wax bolus, which will complement the making period and cost of high capacity Bolus and Compensator in 3D printer, will increase later.

• Nuclear Medicine and Molecular Imaging
• /
• v.43 no.5
• /
• pp.421-428
• /
• 2009

Purpose: We have evaluated characteristics of adrenal masses incidentally observed in nonenhanced F-18 FDG PET/CT of the oncologic patients and the diagnostic ability of F-18 FDG PET/CT to differentiate malignant from benign adrenal masses. Materials and Methods: Between Mar 2005 and Aug 2008, 75 oncologic patients (46 men, 29 women; mean age, $60.8{\pm}10.2$ years; range, 35-87 years) with 89 adrenal masses incidentally found in PET/CT were enrolled in this study. For quantitative analysis, size (cm), Hounsfield unit (HU), maximum standardized uptake value (SUVmax), SUVratio of all 89 adrenal masses were measured. SUVmax of the adrenal mass divided by SUVliver, which is SUVmax of the segment 8, was defined as SUVratio. The final diagnosis of adrenal masses was based on pathologic confirmation, radiologic evaluation (HU<0 : benign), and clinical decision. Results: Size, HU, SUVmax, and SUVratio were all significantly different between benign and malignant adrenal masses.(P < 0.05) And, SUVratio was the most accurate parameter. A cut-off value of 1.0 for SUVratio provided 90.9% sensitivity and 75.6% specificity. In small adrenal masses (1.5 cm or less), only SUVratio had statistically significant difference between benign and malignant adrenal masses. Similarly a cut-off value of 1.0 for SUVratio provided 80.0% sensitivity and 86.4% specificity. Conclusion: F-18 FDG PET/CT can offer more accurate information with quantitative analysis in differentiating malignant from benign adrenal masses incidentally observed in oncologic patients, compared to nonenhanced CT.