• The Journal of Korean Society for Radiation Therapy
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• v.29 no.1
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• pp.69-76
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• 2017

Purpose: The purpose of this study was to verify dosimetric results and reproducibility of position during craniospinal irradiation (CSI) using tomotherapy (Accuray Incorporated, USA). Also, by comparing with conventional CSI Technique, we confirmed the efficiency of using a Tomotherapy. Materials and Methods: 10 CSI patients who get tomotherapy participate. Patient-specific quality assurances (QA) for each patient are conducted before treatment. When treating, we took Megavoltage Computed Tomography (MVCT) that range of head and neck before treatment, L spine area after treatment. Also we conducted in-vivo dosimetry to check a scalp dose. Finally, we made a 3D conventional radiation therapy(3D-CRT) of those patients to compare dosimetric differences with tomotherapy treatment planning. Results: V107, V95 of brain is 0 %, 97.2 % in tomotherapy, and 0.3 %, 95.1 % in 3D-CRT. In spine, value of V107, V95 is 0.2 %, 18.6 % in tomotherapy and 89.6 %, 69.9 % in 3D-CRT. Except kidney and lung, tomotherapy reduced normal organ doses than 3D-CRT. The maximum positioning error value of X, Y, Z was 10.2 mm, -8.9 mm, -11.9 mm. Through in-vivo dosimetry, the average of scalp dose was 67.8 % of prescription dose. All patient-specific QA were passed by tolerance value. Conclusion: CSI using tomotherapy had a risk of parallel organ such as lung and kidney because of integral dose in low dose area. However, it demonstrated dosimetric superiority at a target and saved normal organ to reduce high dose. Also results of reproducibility were not exceeded margins that estimated treatment planning and invivo dosimetry showed to reduce scalp dose. Therefore, CSI using tomotherapy is considered to efficient method to make up for 3D-CRT.

• The Journal of Korean Society for Radiation Therapy
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• v.31 no.1
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• pp.67-74
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• 2019

Purpose: The purpose is to correct for position errors caused by long treatment times. By correcting the target motion that can occur during lung SBRT using IntraFraction CBCT. Methods and materials: We analyzed retrospectively the IFM data of 14 patients with two treatment arc in the treatment plan for lung cancer with stereotactic radiotherapy. An IntraFraction Motion was applied to the Arccheck phantom to acquire the Gamma index data. Results : IntraFraction Motion during the first treatment arc is in the left-right(LR), superiorinferior(SI), anterior-posterior(AP) directions were $0.16{\pm}0.05cm$, 0.72 cm(max error), $0.2{\pm}0.14cm$, 1.26 cm, $0.24{\pm}0.08cm$, 0.82 cm and rotational directions was $0.84{\pm}0.23^{\circ}$, $2.8^{\circ}$(pitch), $0.72{\pm}0.23^{\circ}$, $2.5^{\circ}$(yaw), $0.7{\pm}0.19^{\circ}$, $2^{\circ}$(roll). IntraFraction Motion during the second treatment arc is in the LR, SI, AP directions were $0.1{\pm}0.04cm$, 0.37 cm, $0.14{\pm}0.17cm$, 2 cm, $0.12{\pm}0.04cm$, 0.5 cm and rotational directions was $0.45{\pm}0.12^{\circ}$, $1.3^{\circ}$, $0.37{\pm}0.1^{\circ}$, $1^{\circ}$, $0.35{\pm}0.1^{\circ}$, $1.2^{\circ}$. Gamma index pass rates were $82.64{\pm}10.51%$, 48.4 %. Conclusions : In this study, we examined the validity of IntraFraction Motion correction in lung SBRT and the efficiency of IntraFraction CBCT. Due to the nature of SBRT treatment, IFM may increase due to the increased treatment time. It is believed that the increase in IFM with the increase in treatment time can be improved with the use of FFF Beam and additional position correction using CBCT during treatment.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1125-1139
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• 2022

In an unmanned aerial vehicles (UAVs) system, a physical offset can be existed between the global positioning system/inertial measurement unit (GPS/IMU) sensor and the observation sensor such as a hyperspectral sensor, and a lidar sensor. As a result of the physical offset, a misalignment between each image can be occurred along with a flight direction. In particular, in a case of multi-sensor system, an observation sensor has to be replaced regularly to equip another observation sensor, and then, a high cost should be paid to acquire a calibration parameter. In this study, we establish a precise sensor model equation to apply for a multiple sensor in common and propose an independent physical offset estimation method. The proposed method consists of 3 steps. Firstly, we define an appropriate rotation matrix for our system, and an initial sensor model equation for direct-georeferencing. Next, an observation equation for the physical offset estimation is established by extracting a corresponding point between a ground control point and the observed data from a sensor. Finally, the physical offset is estimated based on the observed data, and the precise sensor model equation is established by applying the estimated parameters to the initial sensor model equation. 4 region's datasets(Jeon-ju, Incheon, Alaska, Norway) with a different latitude, longitude were compared to analyze the effects of the calibration parameter. We confirmed that a misalignment between images were adjusted after applying for the physical offset in the sensor model equation. An absolute position accuracy was analyzed in the Incheon dataset, compared to a ground control point. For the hyperspectral image, root mean square error (RMSE) for X, Y direction was calculated for 0.12 m, and for the point cloud, RMSE was calculated for 0.03 m. Furthermore, a relative position accuracy for a specific point between the adjusted point cloud and the hyperspectral images were also analyzed for 0.07 m, so we confirmed that a precise data mapping is available for an observation without a ground control point through the proposed estimation method, and we also confirmed a possibility of multi-sensor fusion. From this study, we expect that a flexible multi-sensor platform system can be operated through the independent parameter estimation method with an economic cost saving.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.6
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• pp.553-562
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• 2017

GPS (Global Positioning System) receiver among various sensors mounted on UAV (Unmanned Aerial Vehicle) helps to perform various functions such as hovering flight and waypoint flight based on GPS signals. GPS receiver can be used in an environment where GPS signals are smoothly received. However, recently, the use of UAV has been diversifying into various fields such as facility monitoring, delivery service and leisure as UAV's application field has been expended. For this reason, GPS signals may be interrupted by UAV's flight in a shadow area where the GPS signal is limited. Multipath can also include various noises in the signal, while flying in dense areas such as high-rise buildings. In this study, we used analytical photogrammetry and auto tracking total station technique for 3D positioning of UAV. The analytical photogrammetry is based on the bundle adjustment using the collinearity equations, which is the geometric principle of the center projection. The auto tracking total station technique is based on the principle of tracking the 360 degree prism target in units of seconds or less. In both techniques, the target used for positioning the UAV is mounted on top of the UAV and there is a geometric separation in the x, y and z directions between the targets. Data were acquired at different speeds of 0.86m/s, 1.5m/s and 2.4m/s to verify the flight speed of the UAV. Accuracy was evaluated by geometric separation of the target. As a result, there was an error from 1mm to 12.9cm in the x and y directions of the UAV flight. In the z direction with relatively small movement, approximately 7cm error occurred regardless of the flight speed.

• Journal of Cadastre & Land InformatiX
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• v.48 no.1
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• pp.139-151
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• 2018

The National Base Map construction consists of the regular correction work of dividing the whole country into two regions and carrying out the modification Plotting by aerial photographs every two years as well as the real time updating work of correcting the major change feature within two weeks by the field survey and the As-Built Drawing. In the case of the Building Layout Drawing of Korea Real estate Administration intelligence System(KRAS) used for real time updating work of the National base map, the coordinate transformation error is included in the positional error when applied to the National Base Map based on the World Geodetic Reference System as the coordinate system based on the Regional Geodetic Reference System. In addition, National Base Map is registered based on the outline(eaves line) of the building in the Digital Topographic Map, and the Cadastral and Architecture are registered based on the building center line. Therefore, the Building Object management standard is inconsistent. In order to investigate the improvement method, the network RTK survey was conducted directly on a location of the Building Layout Drawing of Korea Real estate Administration intelligence System(KRAS) and the problems were analyzed by comparing with the plane plotting position reference in National Base Map. In the case of the general structure with the difference on the Building center line and the eaves line, beside the location information was different also the difference in the ratio of the building object was different between Building center line and the eave. In conclusion, it is necessary to provide the Base data of the double layer of the Building center line and the outline of the building(eaves line) in order to utilize the Building Layout Drawing of Korea Real estate Administration intelligence System(KRAS). In addition, it is necessary to study an organic map update process that can acquire the up-to-dateness and the accuracy at the same time.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.9
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• pp.595-602
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• 2013

WiFi fingerprinting is well known as an effective localization technique used for indoor environments. However, this technique requires a large amount of pre-built fingerprint maps over the entire space. Moreover, due to environmental changes, these maps have to be newly built or updated periodically by experts. As a way to avoid this problem, crowd-sourced fingerprint mapping attracts many interests from researchers. This approach supports many volunteer users to share their WiFi fingerprints collected at a specific environment. Therefore, crowd-sourced fingerprinting can automatically update fingerprint maps up-to-date. In most previous systems, however, individual users were asked to enter their positions manually to build their local fingerprint maps. Moreover, the systems do not have any principled mechanism to keep fingerprint maps clean by detecting and filtering out erroneous fingerprints collected from multiple users. In this paper, we present the design of a crowd-sourced fingerprint mapping and localization(CMAL) system. The proposed system can not only automatically build and/or update WiFi fingerprint maps from fingerprint collections provided by multiple smartphone users, but also simultaneously track their positions using the up-to-date maps. The CMAL system consists of multiple clients to work on individual smartphones to collect fingerprints and a central server to maintain a database of fingerprint maps. Each client contains a particle filter-based WiFi SLAM engine, tracking the smartphone user's position and building each local fingerprint map. The server of our system adopts a Gaussian interpolation-based error filtering algorithm to maintain the integrity of fingerprint maps. Through various experiments, we show the high performance of our system.

• Journal of radiological science and technology
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• v.34 no.4
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• pp.277-285
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• 2011

To determine the quality control of UGIS, we acquired 105 patients sampling image at 21 general screening centers. The results of image quality evaluation table containing two countries's UGIS showed that the mean of image qualified education table of our country was 73.3 and the standard error was 4.49; In addition, 19 organizations of 21 general screening centers were given appropriate judgement. The average of image qualified education table of Japan was 58 and the standard error was 4.45. Only 8 organizations were given appropriate judgement. Although we made the image quality evaluation tables with same images, there were many differences in the result of two tables. We figured out the problem about the description of whole stomach and photograph skills. Furthermore, we analysed the situation of the UGIS at each general screening center with the acquired images. The biggest problem of the UGIS of our country was that the procedures were performed without clear medical methods. Methods of UGIS were different at every 21 general screening centers, and most of them did not take exam of anterior surface of stomach of the UGIS. In addition, some general screening centers did not include mucosal relief method or esophagography which is required to include in the image qualified education table of our country. Because polisography is used in the same body position, the problem occured about indiscreet exposure dose of patients. Therefore we have to make an effort to get X-ray images which have enough diagnosis information by the quality control of UGIS.

• Radiation Oncology Journal
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• v.26 no.4
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• pp.263-270
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• 2008

Purpose: This study aimed to quantitatively measure the movement of tumors in real-time and evaluate the treatment accuracy, during the treatment of a liver tumor patient, who underwent radiosurgery with a Synchrony Respiratory motion tracking system of a robot CyberKnife. Materials and Methods: The study subjects included 24 liver tumor patients who underwent CyberKnife treatment, which included 64 times of treatment with the Synchrony Respiratory motion tracking system ($Synchrony^{TM}$). The treatment involved inserting 4 to 6 acupuncture needles into the vicinity of the liver tumor in all the patients using ultrasonography as a guide. A treatment plan was set up using the CT images for treatment planning uses. The position of the acupuncture needle was identified for every treatment time by Digitally Reconstructed Radiography (DRR) prepared at the time of treatment planning and X-ray images photographed in real-time. Subsequent results were stored through a Motion Tracking System (MTS) using the Mtsmain.log treatment file. In this way, movement of the tumor was measured. Besides, the accuracy of radiosurgery using CyberKnife was evaluated by the correlation errors between the real-time positions of the acupuncture needles and the predicted coordinates. Results: The maximum and the average translational movement of the liver tumor were measured 23.5 mm and $13.9{\pm}5.5\;mm$, respectively from the superior to the inferior direction, 3.9 mm and $1.9{\pm}0.9mm$, respectively from left to right, and 8.3 mm and $4.9{\pm}1.9\;mm$, respectively from the anterior to the posterior direction. The maximum and the average rotational movement of the liver tumor were measured to be $3.3^{\circ}$ and $2.6{\pm}1.3^{\circ}$, respectively for X (Left-Right) axis rotation, $4.8^{\circ}$ and $2.3{\pm}1.0^{\circ}$, respectively for Y (Crania-Caudal) axis rotation, $3.9^{\circ}$ and $2.8{\pm}1.1^{\circ}$, respectively for Z (Anterior-Posterior) axis rotation. In addition, the average correlation error, which represents the treatment's accuracy was $1.1{\pm}0.7\;mm$. Conclusion: In this study real-time movement of a liver tumor during the radiosurgery could be verified quantitatively and the accuracy of the radiosurgery with the Synchrony Respiratory motion tracking system of robot could be evaluated. On this basis, the decision of treatment volume in radiosurgery or conventional radiotherapy and useful information on the movement of liver tumor are supposed to be provided.

• Progress in Medical Physics
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• v.26 no.2
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• pp.87-92
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• 2015

The gated RapidArc may produce a dosimetric error due to the stop-and-go motion of heavy gantry which can misalign the gantry restart position and reduce the accuracy of important factors in RapidArc delivery such as MLC movement and gantry speed. In this study, the effect of stop-and-go motion in gated RapidArc was analyzed with varying gating window time, which determines the total number of stop-and-go motions. Total 10 RapidArc plans for treatment of liver cancer were prepared. The RPM gating system and the moving phantom were used to set up the accurate gating window time. Two different delivery quality assurance (DQA) plans were created for each RapidArc plan. One is the portal dosimetry plan and the other is MapCHECK2 plan. The respiratory cycle was set to 4 sec and DQA plans were delivered with three different gating conditions: no gating, 1-sec gating window, and 2-sec gating window. The error between calculated dose and measured dose was evaluated based on the pass rate calculated using the gamma evaluation method with 3%/3 mm criteria. The average pass rates in the portal dosimetry plans were $98.72{\pm}0.82%$, $94.91{\pm}1.64%$, and $98.23{\pm}0.97%$ for no gating, 1-sec gating, and 2-sec gating, respectively. The average pass rates in MapCHECK2 plans were $97.80{\pm}0.91%$, $95.38{\pm}1.31%$, and $97.50{\pm}0.96%$ for no gating, 1-sec gating, and 2-sec gating, respectively. We verified that the dosimetric accuracy of gated RapidArc increases as gating window time increases and efforts should be made to increase gating window time during the RapidArc treatment process.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.221-232
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• 2021

This study analyzed co-registration results according to the geometric processing level of reference image, which are Level 1R and Level 1G provided from KOMPSAT-3 and KOMPSAT-3A images. We performed co-registration using each Level 1R and Level 1G image as a reference image, and Level 1R image as a sensed image. For constructing the experimental dataset, seven Level 1R and 1G images of KOMPSAT-3 and KOMPSAT-3A acquired from Daejeon, South Korea, were used. To coarsely align the geometric position of the two images, SURF (Speeded-Up Robust Feature) and PC (Phase Correlation) methods were combined and then repeatedly applied to the overlapping region of the images. Then, we extracted tie-points using the SURF method from coarsely aligned images and performed fine co-registration through affine transformation and piecewise Linear transformation, respectively, constructed with the tie-points. As a result of the experiment, when Level 1G image was used as a reference image, a relatively large number of tie-points were extracted than Level 1R image. Also, in the case where the reference image is Level 1G image, the root mean square error of co-registration was 5 pixels less than the case of Level 1R image on average. We have shown from the experimental results that the co-registration performance can be affected by the geometric processing level related to the initial geometric relationship between the two images. Moreover, we confirmed that the better geometric quality of the reference image achieved the more stable co-registration performance.