• Journal of Biomedical Engineering Research
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• v.19 no.3
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• pp.305-312
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• 1998

A small PACS based on PC is designed for CR. To receive the digital image from CR, a DICOM Interface Unit (DIU) is designed that complied with the medical image standard, DICOM V3.0. The CR images acquired through the DIU are stored in a file-server; the patient information of the images is stored in a database. To improve the performance of PC and to use it easily, multiresolution images are constructed by wavelet transform and displayed progressively. Wavelet compression method is newly adopted to store the images hierarchically to storage units. In this compression method, the image is decomposed into subclasses of image by wavelet transform, and then the subclasses of the image are vector quantized using a multiresolution codebook. The storage units for CR images were divided into the short-term storage in file-server and the harddisk in viewing station. Image processing tools supported by general PACS is implemented based on PC.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.167-174
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• 2021

The purpose of this study is to classify TIFF images, PNG images, and JPEG images using deep learning, and to compare the accuracy by verifying the classification performance. The TIFF, PNG, and JPEG images converted from chest X-ray DICOM images were applied to five deep neural network models performed in image recognition and classification to compare classification performance. The data consisted of a total of 4,000 X-ray images, which were converted from DICOM images into 16-bit TIFF images and 8-bit PNG and JPEG images. The learning models are CNN models - VGG16, ResNet50, InceptionV3, DenseNet121, and EfficientNetB0. The accuracy of the five convolutional neural network models of TIFF images is 99.86%, 99.86%, 99.99%, 100%, and 99.89%. The accuracy of PNG images is 99.88%, 100%, 99.97%, 99.87%, and 100%. The accuracy of JPEG images is 100%, 100%, 99.96%, 99.89%, and 100%. Validation of classification performance using test data showed 100% in accuracy, precision, recall and F1 score. Our classification results show that when DICOM images are converted to TIFF, PNG, and JPEG images and learned through preprocessing, the learning works well in all formats. In medical imaging research using deep learning, the classification performance is not affected by converting DICOM images into any format.

• The Journal of Korean Society for Radiation Therapy
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• v.27 no.1
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• pp.61-71
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• 2015

Purpose : 3D Printers are used to create three-dimensional models based on blueprints. Based on this characteristic, it is feasible to develop a bolus that can minimize the air gap between skin and bolus in radiotherapy. This study aims to compare and analyze air gap and target dose at the branded 1 cm bolus with the developed customized bolus using 3D printers. Materials and Methods : RANDO phantom with a protruded tumor was used to procure images using CT simulator. CT DICOM file was transferred into the STL file, equivalent to 3D printers. Using this, customized bolus molding box (maintaining the 1 cm width) was created by processing 3D printers, and paraffin was melted to develop the customized bolus. The air gap of customized bolus and the branded 1 cm bolus was checked, and the differences in air gap was used to compare $D_{max}$, $D_{min}$, $D_{mean}$, $D_{95%}$ and $V_{95%}$ in treatment plan through Eclipse. Results : Customized bolus production period took about 3 days. The total volume of air gap was average $3.9cm^3$ at the customized bolus. And it was average $29.6cm^3$ at the branded 1 cm bolus. The customized bolus developed by the 3D printer was more useful in minimizing the air gap than the branded 1 cm bolus. In the 6 MV photon, at the customized bolus, $D_{max}$, $D_{min}$, $D_{mean}$, $D_{95%}$, $V_{95%}$ of GTV were 102.8%, 88.1%, 99.1%, 95.0%, 94.4% and the $D_{max}$, $D_{min}$, $D_{mean}$, $D_{95%}$, $V_{95%}$ of branded 1cm bolus were 101.4%, 92.0%, 98.2%, 95.2%, 95.7%, respectively. In the proton, at the customized bolus, $D_{max}$, $D_{min}$, $D_{mean}$, $D_{95%}$, $V_{95%}$ of GTV were 104.1%, 84.0%, 101.2%, 95.1%, 99.8% and the $D_{max}$, $D_{min}$, $D_{mean}$, $D_{95%}$, $V_{95%}$ of branded 1cm bolus were 104.8%, 87.9%, 101.5%, 94.9%, 99.9%, respectively. Thus, in treatment plan, there was no significant difference between the customized bolus and 1 cm bolus. However, the normal tissue nearby the GTV showed relatively lower radiation dose. Conclusion : The customized bolus developed by 3D printers was effective in minimizing the air gap, especially when it is used against the treatment area with irregular surface. However, the air gap between branded bolus and skin was not enough to cause a change in target dose. On the other hand, in the chest wall could confirm that dose decrease for small the air gap. Customized bolus production period took about 3 days and the development cost was quite expensive. Therefore, the commercialization of customized bolus developed by 3D printers requires low-cost 3D printer materials, adequate for the use of bolus.

• The Journal of Korean Society for Radiation Therapy
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• v.25 no.1
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• pp.77-85
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• 2013

Purpose: The accurate movement of gantry rotation, collimator and correct application of dose rate are very important to approach the successful performance of Volumetric Modulated Arc Therapy (VMAT), because it is tightly interlocked with a complex treatment plan. The interruption and restart of dose delivery, however, are able to occur on treatment by various factors of a treatment machine and treatment plan. If unexpected problems of a treat machine or a patient interrupt the VMAT, the movement of treatment machine for delivering the remaining dose will be restarted at the start point. In this investigation, We would like to know the effect of interruptions and restart regarding dose delivery at VMAT. Materials and Methods: Treatment plans of 10 patients who had been treated at our center were used to measure and compare the dose distribution of each VMAT after converting to a form of digital image and communications in Medicine (DICOM) with treatment planning system (Eclipse V 10.0, Varian, USA). We selected the 6 MV photon energy of Trilogy (Varian, USA) and used OmniPro I'mRT system (V 1.7b, IBA dosimetry, Germany) to analyze the data that were acquired through this measurement with two types of interruptions four times for each case. The door interlock and the beam-off were used to stop and then to restart the dose delivery of VMAT. The gamma index in OmniPro I'mRT system and T-test in Microsoft Excel 2007 were used to evaluate the result of this investigation. Results: The deviations of average gamma index in cases with door interlock, beam-off and without interruption on VMAT are 0.141, 0.128 and 0.1. The standard deviations of acquired gamma values are 0.099, 0.091, 0.071 and The maximum gamma value in each case is 0.413, 0.379, 0.286, respectively. This analysis has a 95-percent confidence level and the P-value of T-test is under 0.05. Gamma pass rate (3%, 3 mm) is acceptable in all of measurements. Conclusion: As a result, We could make sure that the interruption of this investgation are not enough to seriously affect dose delivery of VMAT by analyzing the measured data. But this investigation did not reflect all cases about interruptions and errors regarding the movement of a gantry rotation, collimator and patient So, We should continuously maintain a treatment machine and program to deliver the accurate dose when we perform the VMAT for the many kinds of cancer patients.