Geometric and dosimetric verification of a recurrent neural network algorithm to compensate for respiratory motion using an articulated robotic couch

Lee Minsik,Cho Min-Seok,Lee Hoyeon,Jeong Chiyoung,Kwak Jungwon,Jung Jinhong,Kim Su Ssan,Yoon Sang Min,Song Si Yeol,Lee Sang-wook,Kim Jong Hoon,Choi Eun Kyung,Cho Seungryong,Cho Byungchul 한국물리학회 2021 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.78 No.1

The purpose of this study is to evaluate the performance of a recurrent neural network (RNN)-based prediction algorithm to compensate for respiratory movement using an articulated robotic couch system. A prototype of a real-time respiratory motion compensation couch was built using an optical 3D motion tracking system and a six-degree-of-freedom-articulated robotic system. To compensate for the system latency from motion detection to re-positioning of the system, RNN and double exponential smoothing (ES2) prediction algorithms were applied. Three aspects of performance were evaluated, simulation and experiments for geometric and dosimetric evaluations, using data from three liver and three lung patients who underwent stereotactic body radiotherapy. Overall, the RNN algorithm showed better geometric and dosimetric results than the other approaches. In simulation tests, RNN showed 82% average improvement ratio, compared with non-predicted results. In the geometric evaluation, RNN only showed average FWHM broadening of 1.5 mm, compared with the static case. In the dosimetric evaluation, RNN showed average gamma passing rates of 97.4 ± 1.0%, 89.0 ± 2.4% under the 3%/3 mm, 2%/2 mm respectively. It may be technically feasible to use the RNN prediction algorithm to compensate for respiratory motion with an articulated robotic couch system. The RNN algorithm could be widely used for motion compensation in patients undergoing radiotherapy.

Development of High-performance Thin-layer Chromatography (HPTLC) Method for Quality Control of Actinidiae Fructus Vermicultus

Kyung Ho Lee,Geonha Park,Sangjae Lee,Yun Gyo Lee,Minsik Choi,Roun Lee,Young Pyo Jang 한국생약학회 2023 Natural Product Sciences Vol.29 No.4

얼굴 및 눈 위치 추적을 통한 IPTV 화면 인터페이스 제어에 관한 연구

이원오(Won Oh Lee),이의철(Eui Chul Lee),박강령(Kang Ryoung Park),이희경(HeeKyung Lee),박민식(Minsik Park),이한규(Han-Kyu Lee),홍진우(Jin Woo Hong) 한국통신학회 2010 韓國通信學會論文誌 Vol.35 No.6b

최근 HCI 분야에서 사용자의 시선 추적을 통해 보다 편리한 입력 장치를 개발하려는 연구가 활발히 진행되고 있다. 하지만 기존의 시선 추적 방법들은 부가적인 사용자 착용형 장비를 필요로 하거나 원거리에서 작동되지 않는 문제 등으로 인해 IPTV 환경에서 적용하기 어려운 실정이다. 이에 본 연구에서는 사용자 착용없이 고정된 하나의 카메라를 이용하여 얼굴을 취득하고, 취득된 얼굴 영역 내에서 눈의 위치를 검출하여 IPTV의 화면 인터페이스를 제어할 수 있는 새로운 방법을 제안한다. 또한, Adaboost 방법으로 얼굴이나 눈이 성공적으로 검출되지 못했을 경우에도, 계층적 KLT (Kanade-Lucas-Tomasi)특징 추적 방법을 통해 구해진 모션 벡터를 이용하여 화면 인터페이스를 제어할 수 있는 방법을 제안한다. 이처럼, 본 논문의 방법은 기존의 방법과는 달리 실제 IPTV의 시청거리인 2m 정도의 원거리에서도 사용가능하며, 카메라 이외에 별도의 장치를 착용할 필요가 없으므로 편의성이 높고 얼굴 움직임의 제약이 없다는 장점이 있다. 실험결과, 입력되는 얼굴 영상을 초당 15프레임의 속도로 실시간 처리함을 확인할 수 있었으며, 기존 입력 장치의 역할을 충분히 대신할 수 있음을 알 수 있었다. Recently, many researches for making more comfortable input device based on gaze detection have been vigorously performed in human computer interaction. However, these previous researches are difficult to be used in IPTV environment because these methods need additional wearing devices or do not work at a distance. To overcome these problems, we propose a new way of controlling IPTV interface by using a detected face and eye positions in single static camera. And although face or eyes are not detected successfully by using Adaboost algorithm, we can control IPTV interface by using motion vectors calculated by pyramidal KLT (Kanade-Lucas-Tomasi) feature tracker. These are two novelties of our research compared to previous works.

Procrustean Normal Distribution for Non-Rigid Structure from Motion

Lee, Minsik,Cho, Jungchan,Oh, Songhwai IEEE 2017 IEEE transactions on pattern analysis and machine Vol.39 No.7

<P>A well-defined deformation model can be vital for non-rigid structure from motion (NRSfM). Most existing methods restrict the deformation space by assuming a fixed rank or smooth deformation, which are not exactly true in the real world, and they require the degree of deformation to be predetermined, which is impractical. Meanwhile, the errors in rotation estimation can have severe effects on the performance, i.e., these errors can make a rigid motion be misinterpreted as a deformation. In this paper, we propose an alternative to resolve these issues, motivated by an observation that non-rigid deformations, excluding rigid changes, can be concisely represented in a linear subspace without imposing any strong constraints, such as smoothness or low-rank. This observation is embedded in our new prior distribution, the Procrustean normal distribution (PND), which is a shape distribution exclusively for non-rigid deformations. Because of this unique characteristic of the PND, rigid and non-rigid changes can be strictly separated, which leads to better performance. The proposed algorithm, EM-PND, fits a PND to given 2D observations to solve NRSfM without any user-determined parameters. The experimental results show that EM-PND gives the state-of-the-art performance for the benchmark data sets, confirming the adequacy of the new deformation model.</P>

Performance Evaluation of the Tumor Tracking Method Using Beam on/off Interface for the Treatment of Irregular Breathing

Minsik Lee(이민식) 한국방사선학회 2018 한국방사선학회 논문지 Vol.12 No.3

Dose rate regulated tracking is known to be an efficient method which adaptively delivers tracking treatments when patient breathing is irregular. The Motion Management Interface (MMI, Varian Medical System, CA), which provides beam on/off switching during treatment is available for clinic. Study is to test if delivering the adaptive tumor tracking is feasible for irregular breathing using beam switching with MMI. 55 free breathing RPM traces acquired from lung cancer patients are used. The first day RPM traces of the patients are utilized to design preprogrammed tracking MLC patterns, of which periods are intentionally reduced by 20% in order to catch up the variation of patient breathing irregularity in the treatment day. Eligibility criteria for this technique are the variation of amplitude and period less than 20%. An algorithm which determines beam on/off every 100 ms by considering the preprogrammed (MLC) positions and current breathing positions is developed. Tracking error and delivery efficacy are calculated by simulating the beam-switching adaptive tracking from the RPM traces. Breathing patterns of 38 patients (70%) met the eligibility criteria. Tracking errors of all of the cases who meet the criteria are less than 2 mm (average 1.4 mm) and the average delivery efficacy was 71%. Those of rest of the cases are 1.9 mm and 48%. Adaptive tracking with beam switching is feasible if patient selection is based on the eligibility criteria. 선량률을 조절하면서 종양을 추적하는 방법은 호흡이 불규칙한 환자를 치료할 때 방사선을 적응적으로 전달하는 효율적인 방법으로 알려져 있다. 이 연구에서는 빔 켜기/끄기 스위칭을 제공하는 모션 관리 인터페이스 (MMI, Varian Medical System, CA)를 이용한 불규칙 호흡에 대해 적응성 종양 추적을 시행 할 수 있는지 확인하였다. 폐암 환자로부터 획득한 55개의 호흡 정보를 사용하였다. 첫날 환자의 RPM 흔적을 사용하여 사전 프로그래밍 된 추적 MLC 패턴을 디자인하는데, 치료 기간 중 환자의 호흡 불규칙성의 변화를 따라 잡기 위해 기간을 의도적으로 20% 줄였다. 이 기술의 적정성 기준은 진폭 및 주기의 20 % 미만의 표준편차이다. 사전 프로그래밍 된 MLC 위치와 현재 호흡 위치를 고려하여 100 ms마다 빔 켜기 / 끄기를 결정하는 알고리즘이 개발되었다. 추적 오류 및 전달 효율성은 RPM 추적에서 빔 스위칭 적응형 추적을 시뮬레이션하여 계산되었다. 38 명의 환자(70%)의 호흡 양상이 적합 기준을 충족 시켰습니다. 기준을 충족하는 모든 사례의 추적 오류는 2 mm 미만 (평균 1.4 mm)이며 평균 전달 효능은 71 %였다. 기준을 충족하지 못 한, 나머지 경우의 추적오류와 전달 효율은 1.9 mm와 48%였다. 본 연구를 통해, 환자 선택이 적격 기준을 기반으로 하는 경우 빔 스위칭을 통한 적응형 추적 치료가 가능한 것을 확인하였다.

Incremental <tex> $N$</tex>-Mode SVD for Large-Scale Multilinear Generative Models

Minsik Lee,Chong-Ho Choi IEEE 2014 IEEE TRANSACTIONS ON IMAGE PROCESSING - Vol.23 No.10

<P>Tensor decomposition is frequently used in image processing and machine learning for its ability to express higher order characteristics of data. Among tensor decomposition methods, N-mode singular value decomposition (SVD) is widely used owing to its simplicity. However, the data dimension often becomes too large to perform N-mode SVD directly due to memory limitation. An incremental method to N-mode SVD can be used to resolve this issue, but existing approaches only provide a result, which is just enough to solve discriminative problems, not the full factorization result. In this paper, we present a complete derivation of the incremental N-mode SVD, which can be applied to generative models, accompanied by a technique that can reduce the computational cost by reordering calculations. The proposed incremental N-mode SVD can also be used effectively to update the current result of N-mode SVD when new training data is received. The proposed method provides a very good approximation of N-mode SVD for the experimental data, and requires much less computation in updating a multilinear model.</P>

Terfenadone is a strong inhibitor of CYP2J2 present in the human liver and intestinal microsomes

Lee, Eunyoung,Kim, Ju-Hyun,Shon, Jong Cheol,Wu, Zhexue,Kim, Hyun Ji,Gim, Minsik,Lee, Taeho,Liu, Kwang-Hyeon Elsevier 2018 Drug metabolism and pharmacokinetics Vol.33 No.3

Preliminary study of new treatment modality for normal tissue dose reduction using low strength magnetic field:Magnetic Modulated Radiation Therapy (MMRT)

Minsik Lee,Jun-Bong Shin,BongHyo Lee,Jungwon Kwak,Young Seob Shin,Nuri Hyun Jung 대한방사선방어학회 2022 대한방사선방어학회 학술발표회 논문요약집 Vol.2022 No.11

Feasibility Study of Polymer Gel Dosimetry Using a 3D Printed Phantom for Liver Cancer Radiotherapy

Minsik Lee,Seonyeong Noh,KyoungJun Yoon,Sang-Wook Lee,Sang Min Yoon,Jinhong Jung,Chiyoung Jeong,Jungwon Kwak 한국물리학회 2020 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.76 No.6

In this study, a new three-dimensional (3D) volumetric dosimetry method utilizing gel dosimetry with a patient-specific (PS) 3D-printed phantom was developed. A PS 3D-printed phantom that closely mimics the actual tumor and surrounding tissues was demonstrated using a 3D printer (3Dison Plus, Lokit, Korea). MAGAT normoxic polymer gel was filled in the tumor-shaped cavity of the 3D-printed phantom to represent the real tumor. Seven identical gel samples for dosimetric calibration were irradiated at different doses and scanned using magnetic resonance imaging. A chlorinated polyethylene filament was utilized as the 3D printing material. The two-dimensional gamma passing rates using the film were 96.1% and 84.3% for the 3%/3 mm and the 2%/1 mm dose accuracy/distance-to agreement criteria, respectively. The radiation dose-R2 (relaxation rate) calibration, with a coefficient of determination (R2) of 0.997, was applied for the absolute dose calibration. The overall gross tumor volume shape and the dose distribution of the gel measurement agreed reasonably well with the plan results. The 3D gamma passing rate was 91.2% for the 3%/3 mm criteria and decreased to 82.3% for the 2%/1 mm criterion. Our results suggest that polymer gel dosimetry with the 3D-printed phantom allows direct validation of the 3D dose distribution.

Simulation and Experimental Studies of Real-Time Motion Compensation Using an Articulated Robotic Manipulator System

Lee, Minsik,Cho, Min-Seok,Lee, Hoyeon,Chung, Hyekyun,Cho, Byungchul Korean Society of Medical Physics 2017 의학물리 Vol.28 No.4

The purpose of this study is to install a system that compensated for the respiration motion using an articulated robotic manipulator couch which enables a wide range of motions that a Stewart platform cannot provide and to evaluate the performance of various prediction algorithms including proposed algorithm. For that purpose, we built a miniature couch tracking system comprising an articulated robotic manipulator, 3D optical tracking system, a phantom that mimicked respiratory motion, and control software. We performed simulations and experiments using respiratory data of 12 patients to investigate the feasibility of the system and various prediction algorithms, namely linear extrapolation (LE) and double exponential smoothing (ES2) with averaging methods. We confirmed that prediction algorithms worked well during simulation and experiment, with the ES2-averaging algorithm showing the best results. The simulation study showed 43% average and 49% maximum improvement ratios with the ES2-averaging algorithm, and the experimental study with the $QUASAR^{TM}$ phantom showed 51% average and 56% maximum improvement ratios with this algorithm. Our results suggest that the articulated robotic manipulator couch system with the ES2-averaging prediction algorithm can be widely used in the field of radiation therapy, providing a highly efficient and utilizable technology that can enhance the therapeutic effect and improve safety through a noninvasive approach.