• Journal of the Korean Society of Radiology
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• v.16 no.3
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• pp.319-325
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• 2022

The Study In order to obtain a sharpness Image from Skull PA axial projection (Haas) in a head axial X-ray Examination, this study changed the posture angle using Skull Phantom and evaluated the image subjectively to 5 radiologists who worked in the Department of Imaging at University Hospital. In the prone position, the head was lowered 4 cm from the back of the head, entered 25° toward the head, and the image evaluation score was high with 20 points, such as the back bone, dorsum sellae projected in the large hole, and posterior clinoid process. In addition, the score significance was verified, and the Cronbach Alpha value was evaluated to have good reliability of 0.789. As a result of calculating the signal-to-noise ratio (SNR) by setting the region of interest (ROI) of the image, it was the highest at 5.957 for 25° incident at the back of the head and 6.430 for 30° incident at the back of the head. As a result of the study, in order to obtain a sharp image of the back of the head bone, dorsum sellae, and posterior clinoid process when shooting in the axial direction after the head, it is filmed by tilting 25° toward the head from 4 cm below the back of the head. In order to obtain a sharp image of rock pyramid symmetry, petrous ridge, sagittal suture, and lambdoid suture, it is thought that it will be helpful for clinical use if you shoot it 8cm down from the back of the head and tilt it 30° toward the head.

• Journal of the Korean Society of Radiology
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• v.16 no.3
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• pp.217-223
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• 2022

In this study, images taken using a grid and images taken using Air Gap Technique were evaluated in X-ray chest radiography. Subjective Evaluation The ROC (Receiver Operating Characteristic) evaluation was evaluated by 5 radiologists who had worked for more than 10 years in the radiology department of a university hospital. Objective evaluation SNR (Signal to noise ratio) was evaluated. As a result of the analysis, the Cronbach Alpha value was 0.714, which was significantly higher. In the Air Gap Technique, the distance between the phantom and the subject was set at 20 cm, and the image was taken with a tube voltage of 100 kVp, a tube current and a recording time of 8 mAs. In the ROC (Receiver Operating Characteristic) evaluation, the highest score was obtained with 18 score and an objective evaluation SNR (signal to noise ratio) of 6,149 scored. Also, in the imaging method using a grid, when the distance between the phantom and the constant receptor is 15 cm apart, and the tube voltage is 110 kVp, the tube current and the recording time are taken at 8 mAs, the ROC evaluation score is 19 and the objective evaluation SNR (Signal to noise ratio) is the highest with 6.622 scored. Therefore, if the Air Gap Technique imaging method is used, which overcomes the shortcomings such as delay in reading, increase in patient's exposure dose, and shortening of mechanical lifespan, as well as re-radiography due to the cut-off phenomenon of the grid that appears using the grid, the It is thought that it will be very helpful for chest imaging, including the case of using a portable X-ray imaging device.

• Journal of the Korean Society of Radiology
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• v.16 no.5
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• pp.649-660
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• 2022

In 1959, Satomura used spectral Doppler ultrasound to express the velocity of red blood cells according to time change, and Kato defined a zero-base line that could tell the direction of blood flow, making it possible to know the direction of blood flow. This became the basis for the widely used classifications of Triphasic, Biphasic, and Monophasic. However, the above classification has limitations that confuse users with the meaning and timing of use in a clinical environment. As a result, the American Society for Vascular Medicine (SVM) and the Society for Vascular Ultrasound (SVU) A consensus document on Doppler waveform analysis was declared by the joint committee. This study tried to review this consensus and to suggest nomenclature and modifiers that can be used in the domestic vascular ultrasound clinical field. The joint committee formed by SVM and SVU recommended that the use of the triphasic waveform and the biphasic waveform be used as a multiphasic waveform rather than being used due to the ambiguity of interpretation. In addition, it was agreed to name the hybrid-type waveform, which is a monophasic and high-resistance waveform, which has always been a problem of interpretation in a clinical environment, as an intermediate resistive waveform. In addition, in order to increase the communication efficiency between the interpreter and the sonographer, waveform analysis was classified into a main descriptor and a modifier, and it was recommended to use a single nomenclature by unifying various synonyms. It is expected that this literature review will provide accurate arterial spectral Doppler waveform interpretation and an agreed-upon nomenclature to radiologists performing vascular ultrasound examination in clinical practice, and will be utilized as basic data that can contribute to the improvement of public health.

• Imaging Science in Dentistry
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• v.52 no.3
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• pp.267-273
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• 2022

Purpose: The aim of this study was to assess the influence of kilovoltage- peak (kVp) and the metal artifact reduction (MAR) tool on the detection of buccal and lingual peri-implant dehiscence in the presence of titanium-zirconia (Ti-Zr) and zirconia (Zr) implants in cone-beam computed tomography (CBCT) images. Materials and Methods: Twenty implant sites were created in the posterior region of human mandibles, including control sites (without dehiscence) and experimental sites (with dehiscence). Individually, a Ti-Zr or Zr implant was placed in each implant site. CBCT scans were performed using a Picasso Trio device, with variation in the kVp setting (70 or 90 kVp) and whether the MAR tool was used. Three oral radiologists scored the detection of dehiscence using a 5-point scale. The area under the receiver operating characteristic (ROC) curve, sensitivity, and specificity were calculated and compared by multi-way analysis of variance (α=0.05). Results: The kVp, cortical plate involved (buccal or lingual cortices), and MAR did not influence any diagnostic values (P>0.05). The material of the implant did not influence the ROC curve values(P>0.05). In contrast, the sensitivity and specificity were statistically significantly influenced by the implant material (P<0.05) with Zr implants showing higher sensitivity values and lower specificity values than Ti-Zr implants. Conclusion: The detection of peri-implant dehiscence was not influenced by kVp, use of the MAR tool, or the cortical plate. Greater sensitivity and lower specificity were shown for the detection of peri-implant dehiscence in the presence of a Zr implant.

• Investigative Magnetic Resonance Imaging
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• v.26 no.1
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• pp.20-31
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• 2022

Purpose: To assess conventional MRI features associated with residual soft-tissue sarcomas following unplanned excision (UPE), and to compare the diagnostic performance of conventional MRI only with that of MRI including diffusion-weighted imaging (DWI) for residual tumors after UPE. Materials and Methods: We included 103 consecutive patients who had received UPE of a soft-tissue sarcoma with wide excision of the tumor bed between December 2013 and December 2019 and who also underwent conventional MRI and DWI in this retrospective study. The presence of focal enhancement, soft-tissue edema, fascial enhancement, fluid collections, and hematoma on MRI including DWI was reviewed by two musculoskeletal radiologists. We used classification and regression tree (CART) analysis to identify the most significant MRI features. We compared the diagnostic performances of conventional MRI and added DWI using the McNemar test. Results: Residual tumors were present in 69 (66.9%) of 103 patients, whereas no tumors were found in 34 (33.1%) patients. CART showed focal enhancement to be the most significant predictor of residual tumors and correctly predicted residual tumors in 81.6% (84/103) and 78.6% (81/103) of patients for Reader 1 and Reader 2, respectively. Compared with conventional MRI only, the addition of DWI for Reader 1 improved specificity (32.8% vs. 56%, 33.3% vs. 63.0%, P < 0.05), decreased sensitivity (96.8% vs. 84.1%, 98.7% vs. 76.7%, P < 0.05), without a difference in diagnostic accuracy (76.7% vs. 74.8%, 72.9% vs. 71.4%) in total and in subgroups. For Reader 2, diagnostic performance was not significantly different between the sets of MRI (P > 0.05). Conclusion: After UPE of a soft-tissue sarcoma, the presence or absence of a focal enhancement was the most significant MRI finding predicting residual tumors. MRI provided good diagnostic accuracy for detecting residual tumors, and the addition of DWI to conventional MRI may increase specificity.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.1009-1015
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• 2023

This study was a one-group pretest-posttest design experimental study that attempted to verify the effects of applying the Havruta learning method on the academic self-efficacy, academic achievement, and communication skills of radiology college students. This study was conducted from May 1 to June 23, 2023, and applied the Havruta learning method for 6 weeks to 38 second-year radiologists taking a radiology technology course at a university in Jeollanam-do. SPSS/WIN 21.0 was used for data analysis, the reliability of the scale was verified, the subject's general characteristics, pre-test values and post-test values for measurement variables were verified with descriptive statistics, and the difference between before and after the Havruta learning method was verified with paired t-test. Research results show that the Havruta learning method improves academic self-efficacy (t=-2.70, p<.001), academic achievement (t=-2.87, p=.006), and communication skills (t=-2.76, p=.008). showed a statistically significant difference. As a result, Havruta learning method was confirmed as an effective learning method that improves academic self-efficacy, academic achievement, and communication skills of radiology college students. In the future, expanded application of the Havruta learning method will be necessary.

• Journal of the Korean Society of Radiology
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• v.17 no.7
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• pp.1139-1147
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• 2023

The purpose of this paper is to present and evaluate the performance of a method for controlling the dose for optimal image acquisition while minimizing patient exposure by applying a small-sized Photomultiplier(SiPM) sensor inside a portable detector. Portable detectors have the advantage of being able to quickly access the patient's location for rapid diagnosis, but this mobility comes with the challenge of dose control. This paper presents a method to identify the dose that can have the DQE and optimal image quality of the detector through image evaluation based on IEC62220-1-1, an international standard for X-ray imaging devices, and to identify the optimal dose by matching the ADU of the image and the output of the SiPM Sensor. The Skull AP image was acquired by implementing the detector manufacturer's reference dose. The optimal dose was 342.8 µGy, and the optimal controlled dose was 148.3 µGy, which is 57 % of the manufacturer's reference dose. The Chest AP image was 81.9 µGy and the optimal controlled dose was 27.9 µGy, which is a high dose reduction effect of 66 %. In addition, the two images were analyzed by five radiologists and found to have no clinically significant difference in anatomical delineation.

• Korean Journal of Radiology
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• v.22 no.7
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• pp.1213-1224
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• 2021

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion: CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

• Korean Journal of Radiology
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• v.22 no.3
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• pp.476-488
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• 2021

Objective: We aimed to develop a deep neural network for segmenting lung parenchyma with extensive pathological conditions on non-contrast chest computed tomography (CT) images. Materials and Methods: Thin-section non-contrast chest CT images from 203 patients (115 males, 88 females; age range, 31-89 years) between January 2017 and May 2017 were included in the study, of which 150 cases had extensive lung parenchymal disease involving more than 40% of the parenchymal area. Parenchymal diseases included interstitial lung disease (ILD), emphysema, nontuberculous mycobacterial lung disease, tuberculous destroyed lung, pneumonia, lung cancer, and other diseases. Five experienced radiologists manually drew the margin of the lungs, slice by slice, on CT images. The dataset used to develop the network consisted of 157 cases for training, 20 cases for development, and 26 cases for internal validation. Two-dimensional (2D) U-Net and three-dimensional (3D) U-Net models were used for the task. The network was trained to segment the lung parenchyma as a whole and segment the right and left lung separately. The University Hospitals of Geneva ILD dataset, which contained high-resolution CT images of ILD, was used for external validation. Results: The Dice similarity coefficients for internal validation were 99.6 ± 0.3% (2D U-Net whole lung model), 99.5 ± 0.3% (2D U-Net separate lung model), 99.4 ± 0.5% (3D U-Net whole lung model), and 99.4 ± 0.5% (3D U-Net separate lung model). The Dice similarity coefficients for the external validation dataset were 98.4 ± 1.0% (2D U-Net whole lung model) and 98.4 ± 1.0% (2D U-Net separate lung model). In 31 cases, where the extent of ILD was larger than 75% of the lung parenchymal area, the Dice similarity coefficients were 97.9 ± 1.3% (2D U-Net whole lung model) and 98.0 ± 1.2% (2D U-Net separate lung model). Conclusion: The deep neural network achieved excellent performance in automatically delineating the boundaries of lung parenchyma with extensive pathological conditions on non-contrast chest CT images.

• Korean Journal of Radiology
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• v.22 no.6
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• pp.867-879
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• 2021

Objective: To compare the screening performance of diffusion-weighted (DW) MRI and combined mammography and ultrasound (US) in detecting clinically occult contralateral breast cancer in women with newly diagnosed breast cancer. Materials and Methods: Between January 2017 and July 2018, 1148 women (mean age ± standard deviation, 53.2 ± 10.8 years) with unilateral breast cancer and no clinical abnormalities in the contralateral breast underwent 3T MRI, digital mammography, and radiologist-performed whole-breast US. In this retrospective study, three radiologists independently and blindly reviewed all DW MR images (b = 1000 s/mm² and apparent diffusion coefficient map) of the contralateral breast and assigned a Breast Imaging Reporting and Data System category. For combined mammography and US evaluation, prospectively assessed results were used. Using histopathology or 1-year follow-up as the reference standard, cancer detection rate and the patient percentage with cancers detected among all women recommended for tissue diagnosis (positive predictive value; PPV2) were compared. Results: Of the 30 cases of clinically occult contralateral cancers (13 invasive and 17 ductal carcinoma in situ [DCIS]), DW MRI detected 23 (76.7%) cases (11 invasive and 12 DCIS), whereas combined mammography and US detected 12 (40.0%, five invasive and seven DCIS) cases. All cancers detected by combined mammography and US, except two DCIS cases, were detected by DW MRI. The cancer detection rate of DW MRI (2.0%; 95% confidence interval [CI]: 1.3%, 3.0%) was higher than that of combined mammography and US (1.0%; 95% CI: 0.5%, 1.8%; p = 0.009). DW MRI showed higher PPV2 (42.1%; 95% CI: 26.3%, 59.2%) than combined mammography and US (18.5%; 95% CI: 9.9%, 30.0%; p = 0.001). Conclusion: In women with newly diagnosed breast cancer, DW MRI detected significantly more contralateral breast cancers with fewer biopsy recommendations than combined mammography and US.