금나노입자의 방사선 증감 효과를 예측하는 두 가지 전산 모델 비교

학위논문 (석사) -- 서울대학교 대학원 : 융합과학기술대학원 융합과학부(방사선융합의생명전공), 2021. 2. 예성준.Numerous experiments have strongly supported the application of gold nanoparticles (GNPs) as radio-enhanced agents. In the previous study, the local effect model (LEM I) was developed to predict the cell survival for MDA-MB-231 cells exposed to 150 kVp x-rays after 500 µg/ml GNPs treatment. However, measurable microdosimetric quantities could not be obtained, which were correlated with biological effects on cells. Thus, a microdosimetric kinetic model (MKM) was applied for GNP radio-enhancement (GNP-MKM), which uses the microdosimetric quantities such as dose-mean lineal energy. Using the Monte Carlo simulation tool Geant4, the dose-mean lineal energy with secondary radiations from GNPs and the radial dose distributions around a GNP were estimated. The variations in MKM parameters for different photon energies, domain sizes, and GNP concentrations were calculated to compare the survival fractions predicted by both models. As a result of GNP-MKM, the domain size of 500 nm represented pairwise combinations of DSBs making it hard to repair the DNA damage. It contributes to the idea that the DNA repair mechanisms have been modified, which make the biological effect of the intracellular GNPs as radiosensitizers in addition to radioenhancers. With a domain radius of 500 nm and a threshold dose of 20 Gy, the sensitizer enhancement ratio (SER) predicted by GNP–MKM and GNP–LEM was 1.41 and 1.29, respectively. The GNP-MKM predictions much strongly depended on the domain size than GNP-LEM did on the threshold dose. It is able to provide another method to predict survival fraction for the GNP radio-enhancement.방사선 증감제로서의 금나노입자 효과는 수많은 선행연구에서 이미 검증된 바이다. 금나노입자 증감 효과에 의한 암세포의 생존율을 예측하기 위하여 로컬 이펙트 모델(Local Effective Model; LEM)이 도입되었으며, 500 µg/ml 농도의 금나노입자를 섭취시킨 뒤 150 kVp 방사선 조사를 한 MDA-MB-231 유방암 세포의 생존율을 성공적으로 예측하였다. 하지만 세포 안에서 일어나는 생물학적 영향과 관련된 측정 가능한 미시선량계측 인자(microdosimetric quantities)를 얻을 수 없는 한계점이 있었다. 따라서 본 연구에서는 평균 선형에너지 선량(dose-mean lineal energy)과 같은 미시선량계측 인자를 사용하는 마이크로-키네틱 모델(Microdosimetric-Kinetic Model; MKM)을 도입하여 금나노입자의 증감 효과를 예측하고자 하였다. 몬테카를로 시뮬레이션 툴킷 중 하나인 Geant4를 사용하여 금나노입자로부터 방출되는 2차 방사선의 평균 선형에너지 선량과 금나노입자 주변의 방사형 선형 분포를 추정하였다. 또한 기존의 로컬 이펙트 모델과 비교하기 위하여 서로 다른 도메인 크기, 금나노입자의 농도 등에 대해 각 모델에 사용되는 변수가 어떻게 변화하는지 계산하였다. 마이크로-키네틱 모델을 바탕으로 계산된 500 nm의 도메인 크기는 생물학적으로 DNA 손상 복구가 어려운 양가닥 절단(Double Strand Break; DSB)이 주된 대상임을 의미한다. 이것은 금나노입자가 세포 내에서 DNA 복구 메커니즘을 변형시켜 방사선 증감제 뿐만 아니라 감작제로서 역할을 한다는 선행 연구를 뒷받침 한다. 도메인 반지름을 500 nm로, 문턱 선량을 20 Gy로 설정 하였을 때, 마이크로-키네틱 모델과 로컬 이펙트 모델에 의해 예측된 감수증감비(Sensitizer Enhancement Ratio; SER)는 각각 1.41과 1.29이었다. 마이크로-키네틱 모델이 도메인 크기에 대해 강한 의존성을 보이는 반면, 로컬 이펙트 모델은 문턱 선량에 대해 낮은 민감도를 보였다. 본 연구를 통해 마이크로-키네틱 모델이 금나노입자 증감 효과에 의한 생존율 곡선을 예측하는 또 다른 방법론을 제시할 수 있다는 것을 확인하였다.1. Introduction 1 2. Material and Methods 4 2.A. Principle of GNP-LEM 4 2.B. Principle of GNP-MKM 5 2.C. Clonogenic assay and GNP uptakes 8 2.D. Cellular geometry and GNP distributions 9 2.E. MC simulation of D_n 12 2.F. MC simulation of y_D 14 2.G. Comparison of GNP-LEM and GNP-MKM 20 3. Results 21 3.A. Common variables in both models 21 3.B. MC simulation of y_D 23 3.C. Cellular geometry and GNP distributions 27 3.D. Comparison of GNP-MKM and GNP-LEM 31 4. Discussion 36 5. Conclusions 39 REFERENCES 40Maste

Realistic tissue dosimetry models using Monte Carlo simulations. Applications for radionuclide therapies

Radionuclide therapy (RNT) is a generic term for treatment modalities that use a radionuclide labeled to a target-specific molecule. This so-called radiopharmaceutical accumulates in the target, where the ionizing radiation damages the cells. At sufficient levels of radiation, the cells cannot repair themselves. The quantity of the energy deposited in a target region is referred to as the absorbed dose [Gy]. Absorbed dose calculations in RNTs are associated with large uncertainties, originating from determination of the activity as well as uncertainties in absorbed dose conversion factors (S factors). S factors are derived for mathematical described source-target combinations (so called phantoms) using Monte Carlo techniques to simulate the particle transport from various radionuclides. The accuracy of the S factor depends on how well the phantom reflects the patient anatomy. The phantoms most used in conventional dosimetry models rely on crude anatomic descriptions; therefore, calculated absorbed doses and radiation-induced biological effects are rarely well correlated. The aim of this thesis was to develop more realistic phantoms to create more accurate dosimetry models. Most preclinical evaluations of new radiopharmaceuticals or treatment strategies are performed on small animals, and the efficacy should be evaluated with the absorbed dose. In practice, dosimetry calculations are not a standard procedure; instead, activity levels below those reported to produce severe side effects are used. Papers I, II, and III present dosimetry models based on Monte Carlo simulations using realistic phantoms of mice and rats that produce reliable S factors, which could be useful in dosimetry studies. In Paper III, we used our rat dosimetry model with data from an activity-escalating study of 90Y- and 177Lu-BR96 monoclonal antibodies. Two novel parameters that can be used to quantify decreases in peripheral blood cells were derived. We showed that the data derived with these parameters correlated well with the absorbed dose in red bone marrow. In Papers IV and V, we propose two small-scale anatomic models for the small intestine and the testis, respectively. The large difference from conventional models is that different tissue structures are incorporated, allowing for the calculation of absorbed doses to the most radiosensitive cells in the tissue while considering heterogeneous uptake therein. Differences in order of magnitude are possible when calculating absorbed doses using these new dosimetry models. These dosimetry models will be important when making correlations with biological effects

Recent trends, technical concepts and components of computer-assisted orthopedic surgery systems: A comprehensive review

Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.Web of Science1923art. no. 519

Cyclotron Designs for Ion Beam Therapy with Cyclinacs

This thesis presents new superconducting compact (as opposed to separated-sector ) cyclotron designs for injection in CABOTO, a linac developed by the TERA Foundation delivering C6+/H2+ beams up to 400 MeV/u for ion beam therapy. This association of a variable energy linac injected by a fixed energy cyclotron is called cyclinac. Two superconducting cyclotron designs are compared under the same design constraints and methods: a synchrocyclotron and an isochronous cyclotron, both at the highest possible magnetic field and with an output energy of 230 MeV/u. This energy allows to use the cyclotron as a stand-alone accelerator for protontherapy. Once the optimal cyclotron is determined, lower energy cyclotrons can easily be designed. The short pulse length (1.5 µs), fast repetition rate (100-300 Hz) and small beam transmission of the cyclinac (0.2%) require intense pulsed ion sources. To deliver the desired clinical dose rate, the average pulse current of 60 eµA of C6+ at 300 Hz can be produced by three commercial EBIS (EBIS-SC by Dreebit Gmbh) operating at 100 Hz and connected to the beamline in alternating mode. A multicusp ion source is sufficient to produce compatible H2+ beams. The synchrocyclotron design features a central magnetic field of 5 T, an axisymmetric pole and a constant field index of 0.02. The beam is injected axially with a spiral inflector (K = 1.4). A static magnetic perturbation of 0.1 T and 5° width boosts the beam radial gain per turn (with no emittance degradation) by exciting the first radial integer resonance and thus allows beam ejection with moderate beam losses (30%). The RF system operates in first harmonic (Q = 2500). The 180° Dee provides 28 kV peak voltage and the RF is modulated (30-38 MHz) by a rotating capacitor (90-900 pF). The synchrocyclotron's best features are the simple and compact magnet (300 tons) and the low RF power requirements (30 kW power supply). The isochronous cyclotron design features a 3.2 T central magnetic field, four sectors and a pole characterized by elliptical gaps in the hills (3-30 mm) and in the valleys (11-50 cm). Spiraling is minimized (80° total hill axis rotation) and beam ejection is achieved with a single electrostatic deflector placed inside an empty valley. The two RF cavities operate in fourth harmonic at 98 MHz (Q = 7100). The RF system provides peak voltages of 70-120 kV and is powered by a single 100 kW unit. The synchrocyclotron reliability is brought into question by the need of a rotating capacitor and by the complexity of the injection and ejection systems. However, the isochronous cyclotron requires a much more complex magnet. Overall, the isochronous cyclotron is a better solution compared to the synchrocyclotron, because it is as compact but more reliable. To quantitatively determine the industrial and clinical optimum for the CABOTO injection energy, three complementary isochronous cyclotrons of 70, 120 and 170 MeV/u are studied, based on the 230 MeV/u design. The optimal cyclotron energy strongly depends on the clinical aim of the facility. For a dual proton and carbon ion centre, the best compromise between clinical flexibility, accelerator size and power consumption is to accelerate particles up to 150 MeV/u in the cyclotron. In this configuration, the 150 MeV/u isochronous cyclotron has similar weight and spiraling as the most widely used cyclotron for protontherapy (C235 by IBA S.A.), CABOTO is 24 m long and the overall power consumption of the cyclinac is 650 kW. Adding to these characteristics, the property of fast energy variation of the linac makes the cyclinac presented in this thesis a strongly competitive accelerator for dual proton and carbon ion therapy

Synergies between Numerical Methods for Kinetic Equations and Neural Networks

The overarching theme of this work is the efficient computation of large-scale systems. Here we deal with two types of mathematical challenges, which are quite different at first glance but offer similar opportunities and challenges upon closer examination. Physical descriptions of phenomena and their mathematical modeling are performed on diverse scales, ranging from nano-scale interactions of single atoms to the macroscopic dynamics of the earth\u27s atmosphere. We consider such systems of interacting particles and explore methods to simulate them efficiently and accurately, with a focus on the kinetic and macroscopic description of interacting particle systems. Macroscopic governing equations describe the time evolution of a system in time and space, whereas the more fine-grained kinetic description additionally takes the particle velocity into account. The study of discretizing kinetic equations that depend on space, time, and velocity variables is a challenge due to the need to preserve physical solution bounds, e.g. positivity, avoiding spurious artifacts and computational efficiency. In the pursuit of overcoming the challenge of computability in both kinetic and multi-scale modeling, a wide variety of approximative methods have been established in the realm of reduced order and surrogate modeling, and model compression. For kinetic models, this may manifest in hybrid numerical solvers, that switch between macroscopic and mesoscopic simulation, asymptotic preserving schemes, that bridge the gap between both physical resolution levels, or surrogate models that operate on a kinetic level but replace computationally heavy operations of the simulation by fast approximations. Thus, for the simulation of kinetic and multi-scale systems with a high spatial resolution and long temporal horizon, the quote by Paul Dirac is as relevant as it was almost a century ago. The first goal of the dissertation is therefore the development of acceleration strategies for kinetic discretization methods, that preserve the structure of their governing equations. Particularly, we investigate the use of convex neural networks, to accelerate the minimal entropy closure method. Further, we develop a neural network-based hybrid solver for multi-scale systems, where kinetic and macroscopic methods are chosen based on local flow conditions. Furthermore, we deal with the compression and efficient computation of neural networks. In the meantime, neural networks are successfully used in different forms in countless scientific works and technical systems, with well-known applications in image recognition, and computer-aided language translation, but also as surrogate models for numerical mathematics. Although the first neural networks were already presented in the 1950s, the scientific discipline has enjoyed increasing popularity mainly during the last 15 years, since only now sufficient computing capacity is available. Remarkably, the increasing availability of computing resources is accompanied by a hunger for larger models, fueled by the common conception of machine learning practitioners and researchers that more trainable parameters equal higher performance and better generalization capabilities. The increase in model size exceeds the growth of available computing resources by orders of magnitude. Since $2012$, the computational resources used in the largest neural network models doubled every $3.4$ months\footnote{\url{https://openai.com/blog/ai-and-compute/}}, opposed to Moore\u27s Law that proposes a $2$-year doubling period in available computing power. To some extent, Dirac\u27s statement also applies to the recent computational challenges in the machine-learning community. The desire to evaluate and train on resource-limited devices sparked interest in model compression, where neural networks are sparsified or factorized, typically after training. The second goal of this dissertation is thus a low-rank method, originating from numerical methods for kinetic equations, to compress neural networks already during training by low-rank factorization. This dissertation thus considers synergies between kinetic models, neural networks, and numerical methods in both disciplines to develop time-, memory- and energy-efficient computational methods for both research areas

Brain and Human Body Modeling

This open access book describes modern applications of computational human modeling with specific emphasis in the areas of neurology and neuroelectromagnetics, depression and cancer treatments, radio-frequency studies and wireless communications. Special consideration is also given to the use of human modeling to the computational assessment of relevant regulatory and safety requirements. Readers working on applications that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest developments in computational modelling and human phantom development to assess a given technology’s safety and efficacy in a timely manner. Describes construction and application of computational human models including anatomically detailed and subject specific models; Explains new practices in computational human modeling for neuroelectromagnetics, electromagnetic safety, and exposure evaluations; Includes a survey of modern applications for which computational human models are critical; Describes cellular-level interactions between the human body and electromagnetic fields

Brain and Human Body Modeling

This open access book describes modern applications of computational human modeling with specific emphasis in the areas of neurology and neuroelectromagnetics, depression and cancer treatments, radio-frequency studies and wireless communications. Special consideration is also given to the use of human modeling to the computational assessment of relevant regulatory and safety requirements. Readers working on applications that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest developments in computational modelling and human phantom development to assess a given technology’s safety and efficacy in a timely manner. Describes construction and application of computational human models including anatomically detailed and subject specific models; Explains new practices in computational human modeling for neuroelectromagnetics, electromagnetic safety, and exposure evaluations; Includes a survey of modern applications for which computational human models are critical; Describes cellular-level interactions between the human body and electromagnetic fields

Comparison of parameter fitting on the model of irradiation effects to bystander cells between nelder-mead simplex and particle swarm optimization

Study on the biological effects of irradiation has become important nowadays. Mathematical modeling is one of the interests among researchers due to its ability to explain the dynamics process of the irradiation. Some physical parameters cannot be evaluated from the empirical data. Therefore, the aim of this work is to estimate parameters of the model of irradiation effects on bystander cells using optimization approaches. We employ two algorithms: Nelder-Mead Simplex (NMS) (which is the local optimizer) and Particle Swarm (which is the global optimizer). We compare the efficiency of two optimization algorithms in optimizing the parameter values of the model. 50 sets of parameters have been estimated and all sets are able to match the model simulation and the experimental data with the least Sum-Squared Error (SSE). The graph of model simulation using a set of the estimated parameters from both optimization algorithms shows a good fit with the experimental data. The overall results indicate that NSM is better than Particle Swarm (PS) optimization in the aspect of time computing, while there is no significant difference in the score of SSE and converging iteration to the least SSE