Development of Tissue Equivalent Materials for a Multi-modality (CT&MRI) Phantom in MRI-guided Radiation Treatment

Yunji Seol,Jina Kim,Aeran Kim,Jinho Hwang,Taegeon Oh,Jin-sol Shin,Hong Seok Jang,Byung Ock Choi,Young-nam Kang 한국물리학회 2018 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.73 No.7

This study proposed to develop a phantom material that can represent the various contrasts of both MRI and CT images and is available to use in MRI-guided radiation treatment. Materials used for making a phantom that can be used for both MRI and CT image were agarose (T2 modifier), gadolinium-based contrast agent (T1 modifier), sodium uoride (CT number modifier), and distilled water. They were mixed at various composition ratios and stirred until transparent. For the relationship between the ingredients and values, 48 samples were manufactured at various composition ratios. The relationship was expressed as equations, to be able to get the composition ratios of organs that we wanted to make. MR relaxation times were measured using 1.5 T MRI equipment. CT scans were performed at 120 kVp and extracted CT numbers from images. Based on the fitted equations derived from the relationship between ingredients and values, materials were manufactured using the composition ratio of human organs; brain (white and gray matter), liver, spleen, kidney, and prostate. The all values were within the reference range, but some exceeded the range due to the image noise. A phantom composed of substitutes made from the derived equations added other substances of different density like bone or lung can be used as an inhomogeneity dose calculation phantom for both CT and MRI. Furthermore, it can be applied to MRI-only based RTP systems and MRI-guided radiation treatment QA in the future.

Prediction of Tumor Temperature in Regional Hyperthermia by Using LED Luminance

이재현,Yunji Seol,Taegeon Oh,Na young An,Kyumin Han,Jinho Hwang,Hong Seok Jang,Byung Ock Choi,Young-nam Kang 한국물리학회 2020 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.77 No.6

Hyperthermia is used to destroy tumors by generating heat in the body (40-45 °C). In particular, regional hyperthermia entails intense heating of tumors rather than raising the temperature of the body. In regional hyperthermia, the prediction of the tumor temperature before treatment is essential to ensure treatment efficiency and patient safety. The goal of this study is to predict the temperature of tumors in regional hyperthermia by using a light emitting diode (LED). LED luminance shows a linear relationship with current above a certain voltage. Thus, the temperature may be predicted via LED luminance based on these electrical characteristics, Special Absorption Rate (SAR), and Pennes' Bio-heat Transfer Equation. LED was located in the agar phantom at the same intervals to measure the luminance, and measure temperature at same spot using thermometer and well verified using a commercialized simulation program (Sim4Life). Within the range of electrode size, the difference in luminance between the predicted and the measured temperatures was within 2.5%. In addition, the difference between the predicted temperature and the result of the simulation program was within 1.5%. In this study, the tumor temperature in regional hyperthermia was predicted using LED luminance to ensure treatment accuracy and patient safety. This study showed the possibility of temperature prediction based on LED luminance.

Evaluation of Developed Thermal Distribution Prediction Algorithm Using Mass Density Distribution with CT Image

Jinho Hwang,Yunji Seol,Taegeon Oh,Na young An,Jaehyeon Lee,Chul-Seung Kay,Hong Seok Jang,Byung Ock Choi,Young-nam Kang 한국물리학회 2020 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.76 No.1

Prior to the hyperthermia, the amount of heat energy delivered to the tumors must be confirmed. If it cannot be confirmed before hyperthermia, normal tissues may also be heated, leading to possible necrosis. In a previous study, the thermal distribution was calculated using mass density distribution with CT image. The previous study was not performed various evaluations of accuracy for the developed thermal distribution prediction algorithm. In this study, the developed thermal distribution prediction algorithm was evaluated by comparing the phantom with the measured temperature and a commercial simulation software (Sim4Life) has been used as a reference data for hyperthermia studies. The difference between the measured temperature and the commercial simulation software (Sim4Life) was within 3%, and the difference between the measured temperature and the developed thermal distribution algorithm was also within 2%. The difference between the developed thermal distribution algorithm and the commercial simulation software was also within 3%. The thermal distribution algorithm developed in this study could predict the internal temperature of the patient before hyperthermia and increase the treatment accuracy by preventing necrosis from occurring in normal organs. In addition, it could easily predict the temperatures for hyperthermia without modeling CT images taken for the diagnosis of lesions.

Using deep learning to predict radiation pneumonitis in patients treated with stereotactic body radiotherapy (SBRT) for pulmonary nodules: preliminary results

Choi Kyu Hye,Seol Yunji,Kang Young-nam,Lee Young Kyu,Ahn Sang Hee,Song Jin Ho,Choi Byung-Ock,Kim Yeon-Sil,Jang Hong Seok 한국물리학회 2022 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.81 No.5

This study aimed to develop a predictive model using clinical, dosimetric, and radiomic features for radiation-induced pneumonitis (RP) after lung stereotactic body radiation therapy (SBRT). We retrospectively analyzed the clinical data of 153 patients who underwent SBRT for lung nodules between 2010 and 2019. A total of 3,350 radiomic computed tomography (CT) features of radiotherapy simulation (shape, intensity, texture, and log flters) were extracted. Among them, 30 factors were selected through Pearson’s correlation analysis and subjected to analysis. A proposed lung toxicity prediction model was developed using a deep neural network algorithm. The programming language used was Python. The data were divided into a training set (70% of data) and a test/validation set (30% of data). We adjusted the original data by oversampling to correct the uneven sample distribution to balance the data set. The Talos library was used in this study for hyperparameter determination and the model with the highest accuracy was selected. The Talos library provided the RP prediction model with the highest accuracy of 94.8%. The area under the curve of the receiver operating characteristics curve was 0.90, which was relatively fair. It showed relatively high accuracy in the RP prediction model based on the clinical, dosimetric, and radiomic factors of patients who received SBRT for lung nodules. A further study using more cases from other medical centers is being planned for external validation.

Analysis of plan parameters affecting the delivery quality assurance passing rate of the Tomo direct method in Radixact X9

Kim Jina,Oh Taegeon,Seol Yunji,Lee Jaehyeon,Hong Ju-Young,Kim Sung Ho,Jang Hong Seok,Choi Byung Ock,Kang Young-nam 한국물리학회 2021 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.78 No.1

Delivery quality assurance (DQA) is the process of establishing a treatment plan and verifying that the pre-treatment dose is delivered without any problems through a simulation. If the result does not meet certain criteria, the dose delivered to the patient is judged to be inaccurate, and the measurement or the treatment plan should be reconsidered. Therefore, it is important to analyze the plan parameters that influence the DQA outcome. The plan parameters have been studied for LINAC-based intensity-modulated radiation therapy and the Tomo helical method but not for the Tomo direct (TD) method. In this study, we perform this investigation using the TD method. A Radixcat X9 machine was used to collect data. Furthermore, in the TD method, we set the passing rate for each gamma analysis criterion as 2%/2 mm and 3%/3 mm. Next, the plan parameters influencing the DQA passing rate were confirmed by Pearson’s correlation analysis and regression analysis. Based on the gamma analysis, the mean passing rates of 2%/2 mm and 3%/3 mm were 97.9299 and 99.8472, respectively. The plan parameters influencing the DQA passing rate during the Pearson correlation analysis were IECZ (2%/2 mm: p = 0.008), duration (2%/2 mm: p = 0.002), and planning target volume (2%/2 mm: p = 0.030 and 3%/3 mm: p = 0.034). Based on these results, a regression analysis was performed. As a result of the regression analysis, duration (2%/2 mm: p = 0.047) was identified as the most significant plan parameter for the DQA passing rate. However, statistically for the gamma analysis criterion of 3%/3 mm, there were no significant plan parameters. As the beta of the duration has a negative relationship with the passing rate, reducing the duration improves the passing rate. When confirming the passing rate for each group in a duration of 60 s, it was confirmed that the passing rate was high if it was set to less than 240 s. So, we recommend setting the duration to 240 s or less.

Development of an Algorithm for Predicting the Thermal Distribution by using CT Image and the Specific Absorption Rate

Jinho Hwang,Aeran Kim,Jina Kim,Yunji Seol,Taegeon Oh,Jin-sol Shin,Hong Seok Jang,Yeon-Sil Kim,Byung Ock Choi,Young-nam Kang 한국물리학회 2018 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.73 No.10

During hyperthermia therapy, cancer cells are heated to a temperature in the range of 40 45 C for a defined time period to damage these cells while keeping healthy tissues at safe temperatures. Prior to hyperthermia therapy, the amount of heat energy transferred to the cancer cells must be predicted. Among various non-invasive methods, the thermal prediction method using the specific absorption rate (SAR) is the most widely used method. The existing methods predict the thermal distribution by using a single constant for the mass density in one organ through assignment. However, because the SAR and the bio heat equation (BHE) vary with the mass density, the mass density of each organ must be accurately considered. In this study, the mass density distribution was calculated using the relationship between the Hounsfield unit and the mass density of tissues in preceding research. The SAR distribution was found using a quasi-static approximation to Maxwell's equation and was used to calculate the potential distribution and the energy distributions for capacitive RF heating. The thermal distribution during exposure to RF waves was determined by solving the BHE with consideration given to the considering contributions of heat conduction and external heating. Compared with reference data for the mass density, our results was within 1%. When the reconstructed temperature distribution was compared to the measured temperature distribution, the difference was within 3%. In this study, the density distribution and the thermal distribution were reconstructed for the agar phantom. Based on these data, we developed an algorithm that could be applied to patients.