• 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.

• ETRI Journal
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• v.33 no.3
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• pp.374-381
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• 2011

A simple and effective compression method is proposed for multiple-scan testing. For a given test set, each test pattern is compressed from the view of slices. An encoding table exploiting seven types of frequently-occurring pattern is used. Compression is then achieved by mapping slice data into codewords. The decompression logic is small and easy to implement. It is also applicable to schemes adopting a single-scan chain. Experimental results show this method can achieve good compression effect.

• Journal of Korea Multimedia Society
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• v.23 no.5
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• pp.625-632
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• 2020

In the diagnosis of lung cancer, the tumor size is measured by the longest diameter of the tumor in the entire slice of the CT. In order to accurately estimate the size of the tumor, it is better to measure the volume, but there are some limitations in calculating the volume in the clinic. In this study, we propose an algorithm to segment lung cancer by applying a custom loss function that combines focal loss and dice loss to a U-Net model that shows high performance in segmentation problems in chest CT images. The combination of values of the various parameters in custom loss function was compared to the results of the model learned. The purposed loss function showed F1 score of 88.77%, precision of 87.31%, recall of 90.30% and average precision of 0.827 at α=0.25, γ=4, β=0.7. The performance of the proposed custom loss function showed good performance in lung cancer segmentation.

• Korean Journal of Digital Imaging in Medicine
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• v.17 no.1
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• pp.13-18
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• 2015

Purpose : Skip the repetitive HRCT axial scan in order to reduce the exposure of patients during chest HRCT scan, Helical Scan Data into a reconstructed image, and exposure of the patient change and visually evaluate the usefulness of the HRCT images. Materials and method : Patients were enrolled in the survey are 50 people who underwent chest CT scans of patients who presented to the hospital from January 2015 to March 2015. 50 people surveyed 22 people men and 28 people women people showed an average distribution of 30 to 80 years age was 48 years. 50 patients to Somatom Sensation 64 ch (Siemens) model with 120 kVp tube voltage to a reference mAs tube current to mAs (Care dose, Siemens) as a whole, including the lungs and the chest CT scan was performed. Scan upon each patient CARE dose 4D (Automatic exposure control, Siemens Medical Solution Erlangen, Germany) was to maintain the proper radiation dose scan every cross-section through a device that automatically adjusts the tube current of. CT scan is the rotation time of the Tube slice collimation, slice width 0.6 mm, pitch factor was made under the terms of 1.4. CT scan obtained after the raw data (raw data) to the upper surface of the axial images and coronal images for each slice thickness 1 mm, 5 mm intervals in the high spatial frequency calculation method (hight spatial resolution algorithm, B60 sharp) was the use of the lung window center -500 HU, windows were reconstructed into images in the interval -1000 HU to see. Result : 1. Measure the total value of DLP 50 patients who proceed to chest CT group A (Helical Scan after scan performed with HRCT) and group B (Helical Scan after the HR image reconstruction to the original data) compared with the group divided, analysis As a result of the age, but show little difference for each age group it had a decreased average dose of about 9%. 2. A Radiation read the results of the two Radiologist and a doctor upper lobe and middle lobe of the lung takes effect the visual evaluation is not a big difference between the two images both, depending on the age of the patient, especially if the blood vessels of the lower lobe (A: 3.4, B: 4.6) and bronchi(A: 3.8, B4.7) image shake caused by breathing in anxiety (blurring lead) to the original data (raw data) showed that the reconstructed image is been more useful in diagnostic terms. Conclusion : Scan was confirmed a continuous, rapid motion video to get Helical scan is much lower lobe lung reduction in visual blurring, Helical scan data to not repeat the examination by obtaining HRCT images reorganization reduced the exposure of the patient.

• Journal of Biomedical Engineering Research
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• v.10 no.1
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• pp.1-10
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• 1989

An automatic lung nodule detection algorithm was applied for digital radiographic images using Bit Slice Processor. In this algorithm, signal enhancing filtering and signal suppressing filtering were performed on the given digital chest image, respectively. Then we grit the dirt- frrence image from these filtered images, and hi-level island images were obtained by applying various threshold values. From the island images, we decided the suspicious nodules using size and circularity test, and marked them to alert radiologists. The performance of the atgorithm was analyzed with respect to the size, contrast and position of digitally synthesized nodules. This method presented 45.8% of true positive ratio for the nodules of lOw in diameter with 12-16 pixel value differnces.

• Radiation Oncology Journal
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• v.22 no.1
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• pp.55-63
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• 2004

Purpose : To evaluate the reflection of tumor motion according to the planning CT scan time. Material and Methods : A model of N-shape, which moved aiong the longitudinal axis during the ventilation caused by a mechanical ventilator, was produced. The model was scanned by planning CT, while setting the relative CT scan time (T: CT scan time/ventilatory period) to 0.33, 0.50, 0.67, 0.75, 1.00, 1.337, and 1.537. In addition, three patients with non-small cell lung cancer who received stereotactic radiosurgery In the Department of Radiation Oncology, Asan Medical Center from 03/19/2002 to 05/21/2002 were scanned. Slow (10 Premier, Picker, scan time 2.0 seconds per slice) and fast CT scans (Lightspeed, GE Medical Systems, with a scan time of 0.8 second per slice) were peformed for each patient. The magnitude of reflected movement of the N-shaped model was evaluated by measuring the transverse length, which reflected the movement of the declined bar of the model at each slice. For patients' scans, all CT data sets were registered using a stereotactic body frame scale with the gross tumor volumes delineated in one CT image set. The volume and three-dimensional diameter of the gross tumor volume were measured and analyzed between the slow and fast CT scans. Results : The reflection degree of longitudinal movement of the model increased in proportion to the relative CT scan times below 1.00 7, but remained constant above 1.00 T Assuming the mean value of scanned transverse lengths with CT scan time 1.00 T to be $100\%$, CT scans with scan times of 0.33, 0.50, 0.57, and 0.75 T missed the tumor motion by 30, 27, 20, and $7.0\%$ respectively, Slow (scan time 2.0 sec) and Fast (scan time 0.8 sec) CT scans of three patients with longitudinal movement of 3, 5, and 10 mm measured by fluoroscopy revealed the increases in the diameter along the longitudinal axis Increased by 6.3, 17, and $23\%$ in the slow CT scans. Conculsion : As the relative CT scan time increased, the reflection of the respiratory tumor movement on planning CT also Increased, but remained constant with relative CT scan times above 1.00 T When setting the planning CT scan time above one respiration period (>1.00 T), only the set-up margin is needed to delineate the planning target volume. Therefore, therapeutic ratio can be increased by reducing the radiation dose delivered to normal lung tissue.

• The Korean Journal of Physiology and Pharmacology
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• v.16 no.1
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• pp.59-64
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• 2012

Hypoxic pulmonary vasoconstriction (HPV) is physiologically important response for preventing mismatching between ventilation and perfusion in lungs. The HPV of isolated pulmonary arteries (HPV-PA) usually require a partial pretone by thromboxane agonist (U46619). Because the HPV of ventilated/perfused lungs (HPV-lung) can be triggered without pretone conditioning, we suspected that a putative tissue factor might be responsible for the pretone of HPV. Here we investigated whether HPV can be also observed in precision-cut lung slices (PCLS) from rats. The HPV in PCLS also required partial contraction by U46619. In addition, $K^+$ channel blockers (4AP and TEA) required U46619-pretone to induce significant contraction of PA in PCLS. In contrast, the airways in PCLS showed reversible contraction in response to the $K^+$ channel blockers without pretone conditioning. Also, the airways showed no hypoxic constriction but a relaxation under the partial pretone by U46619. The airways in PCLS showed reliable, concentration-dependent contraction by metacholine ($EC_{50}$, ~210 nM). In summary, the HPV in PCLS is more similar to isolated PA than V/P lungs. The metacholineinduced constriction of bronchioles suggested that the PLCS might be also useful for studying airway physiology in situ.

• Korean Journal of Radiology
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• v.25 no.9
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• pp.833-842
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• 2024

Objective: To assess the effect of a new lung enhancement filter combined with deep learning image reconstruction (DLIR) algorithm on image quality and ground-glass nodule (GGN) sharpness compared to hybrid iterative reconstruction or DLIR alone. Materials and Methods: Five artificial spherical GGNs with various densities (-250, -350, -450, -550, and -630 Hounsfield units) and 10 mm in diameter were placed in a thorax anthropomorphic phantom. Four scans at four different radiation dose levels were performed using a 256-slice CT (Revolution Apex CT, GE Healthcare). Each scan was reconstructed using three different reconstruction algorithms: adaptive statistical iterative reconstruction-V at a level of 50% (AR50), Truefidelity (TF), which is a DLIR method, and TF with a lung enhancement filter (TF + Lu). Thus, 12 sets of reconstructed images were obtained and analyzed. Image noise, signal-to-noise ratio, and contrast-to-noise ratio were compared among the three reconstruction algorithms. Nodule sharpness was compared among the three reconstruction algorithms using the full-width at half-maximum value. Furthermore, subjective image quality analysis was performed. Results: AR50 demonstrated the highest level of noise, which was decreased by using TF + Lu and TF alone (P = 0.001). TF + Lu significantly improved nodule sharpness at all radiation doses compared to TF alone (P = 0.001). The nodule sharpness of TF + Lu was similar to that of AR50. Using TF alone resulted in the lowest nodule sharpness. Conclusion: Adding a lung enhancement filter to DLIR (TF + Lu) significantly improved the nodule sharpness compared to DLIR alone (TF). TF + Lu can be an effective reconstruction technique to enhance image quality and GGN evaluation in ultralow-dose chest CT scans.

• Journal of the Korean Society of Food Culture
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• v.9 no.5
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• pp.499-507
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• 1995

The purpose of this study was to survey the various kinds of cooked beef products focusing on Sukyuk (boiled beef) and Pyunyuk (boiled beef slice) recorded on the historical literatures written from 1670 to 1945. Sukyuk and Pyunyuk were recorded 45 times in the references and could be classified into 11 groups based on major ingredients such as fresh meat, tough meat, rotten meat, tail, head, lung, cup of breast, testicles, pancreas, spleen and tung. Twenty two cooking methods were described on the records. Sukyuk and Pyunyuk based on fresh meat were described the most frequently. Pyunyuk based on head was described late in 18th centuries, but its recipe could not be found in any records and that of internal organs and tung were presented late in 18th centuries and early in 19th centuries, respectively. The major ingredients of Sukyuk and Pyunyuk were lean meat, tail, head, lung, cup of breast, testicles, pancreas, spleen and tongue Mulberry seed, fragment of roof tile and other sub-ingredients were used for softening or deodorizing the off flavor of the products.

• The Journal of Korean Society for Radiation Therapy
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• v.27 no.2
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• pp.131-143
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• 2015

Purpose : By comparing a CT fusion plan based on MRI with a plan based on only MRI without CT, we intended to study usefulness of a plan based on only MRI. And furthermore, we intended to realize a realtime MR-IGRT by MRI image without CT scan during the course of simulation, treatment planning, and radiation treatment. Materials and Methods : BBB CT (Brilliance Big Bore CT, 16slice, Philips), Viewray MRIdian system (Viewray, USA) were used for CT & MR simulation and Treatment plan of 11 patients (1 Head and Neck, 5 Breast, 1 Lung, 3 Liver, 1 Prostate). When scanning for treatment, Free Breathing was enacted for Head&Neck, Breast, Prostate and Inhalation Breathing Holding for Lung and Liver. Considering the difference of size between CT and Viewray, the patient's position and devices were in the same condition. Using Viewray MRIdian system, two treatment plans were established. The one was CT fusion treatment plan based on MR image. Another was MR treatment plan including electron density that [ICRU 46] recommend for Lung, Air and Bone. For Head&Neck, Breast and Prostate, IMRT was established and for Lung and Liver, Gating treatment plan was established. PTV's Homogeneity Index(HI) and Conformity Index(CI) were use to estimate the treatment plan. And DVH and dose difference of each PTV and OAR were compared to estimate the treatment plan. Results : Between the two treatment plan, each difference of PTV's HI value is 0.089% (Head&Neck), 0.26% (Breast), 0.67% (Lung), 0.2% (Liver), 0.4% (Prostate) and in case of CI, 0.043% (Head&Neck), 0.84% (Breast), 0.68% (Lung), 0.46% (Liver), 0.3% (Prostate). As showed above, it is on Head&Neck that HI and CI's difference value is smallest. Each difference of average dose on PTV is 0.07 Gy (Head&Neck), 0.29 Gy (Breast), 0.18 Gy (Lung), 0.3 Gy (Liver), 0.18 Gy (Prostate). And by percentage, it is 0.06% (Head&Neck), 0.7% (Breast), 0.29% (Lung), 0.69% (Liver), 0.44% (Prostate). Likewise, All is under 1%. In Head&Neck, average dose difference of each OAR is 0.01~0.12 Gy, 0.04~0.06 Gy in Breast, 0.01~0.21 Gy in Lung, 0.06~0.27 Gy in Liver and 0.02~0.23 Gy in Prostate. Conclusion : PTV's HI, CI dose difference on the Treatment plan using MR image is under 1% and OAR's dose difference is maximum 0.89 Gy as heterogeneous tissue increases when comparing with that fused CT image. Besides, It characterizes excellent contrast in soft tissue. So, radiation therapy using only MR image without CT scan is useful in the part like Head&Neck, partial breast and prostate cancer which has a little difference of heterogeneity.