Modeling of the Magnetic Particle Imaging Signal Due to Magnetic Nanoparticles

磁性纳米粒子产生的磁性粒子成像信号的建模

• 批准号: 9024525
• 负责人: Carlos M Rinaldi-Ramos
• 金额: $ 18万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-03-01 至 2018-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9024525
• 关键词: Accounting; Algorithms; Angiography; Anisotropy; Arteries; Attention; Caliber; Cells; Characteristics; Chronic Kidney Failure; Coagulation Process; Computer Simulation; Contrast Media; Coronary; Coronary artery; Dependence; Detection; Development; Disadvantaged; Environment; Equation; Foundations; Future; Future Generations; Health; Hybrids; Image; Imaging Techniques; Inflammation; Location; Magnetic Resonance Imaging; Magnetic nanoparticles; Magnetism; Maps; Measurement; Mission; Modeling; Monitor; Motion; Organ; Patients; Performance; Property; Relaxation; Research; Resolution; Rotation; Scanning; Shipping; Ships; Signal Transduction; Solid; Spatial Distribution; Suspension substance; Suspensions; Time; Tissues; Tracer; Translations; Viscosity; Work; bioimaging; cancer imaging; contrast imaging; cost effective; design; experience; image processing; improved; innovation; interest; iron oxide; magnetic dipole; magnetic field; nanoparticle; novel; operation; particle; response; simulation; theories

 DESCRIPTION (provided by applicant): Magnetic Particle Imaging (MPI) is a new tomographic imaging technique that maps the spatial distribution of iron oxide magnetic nanoparticles (MNPs) in real time and with spatial resolution that is on par or better than other biomedical imaging techniques. Because iron oxide MNPs are nontoxic, MPI is a safe imaging alternative for Chronic Kidney Disease (CKD) patients and due to its sensitivity it is suitable for angiography, cell tracking, cancer imaging, inflammation imaging, imaging major organs, and imaging of coronary arteries. Recently attention has shifted towards development of MNPs with ideal MPI signal characteristics. Unfortunately, these efforts are hampered by a lack of theories that predict the MPI signal due to MNP tracers, taking into account the finite relaxation dynamics of MNPs in time-varying magnetic fields typical of MPI. Because of this, most prior work on development of MNP MPI tracers has been limited to trial-and-error characterization of synthesized particles, without a theory guiding their rational design. What is needed is a solid theoretical foundation that will allow rational design of future generations of MNP MPI tracers and tuning of MPI magnetic field conditions to yield optimal image contrast and resolution. The proposed research will develop a theoretical foundation relating MNP properties (e.g., core size, hydrodynamic diameter, domain magnetization, magnetic anisotropy, particle-particle interactions, etc.) and MPI magnetic field conditions (strength of bias and excitation field, magnetic field gradient strength, scan rate, etc.) to the MPI signal strength and resolution. The proposed approach is unique and distinct from other work because we will develop stochastic computer simulation models of the response of MNPs to the magnetic fields typical of MPI, taking into account nanoparticle translation, physical rotation, internal dipole rotation, and particle-particle magnetic interactions. These models will enable systematic study of the large parameter space of particle properties and magnetic field conditions typical of MPI. The proposed work is significant because it will provide a much-needed theoretical understanding of the relation- ship between particle properties, MPI magnetic field conditions, and MPI signal strength and resolution. The proposed work is also significant because it will yield rules for the rational design of MNP MPI tracers with optimal signal strength and resolution and could also suggest novel applications of MPI beyond imaging of MNP tracer location and motion. The proposed work is innovative because it will yield this theoretical foundation through development of computer simulation platforms to model the response of MNPs to the magnetic fields generated in MPI through a combination of Brownian dynamics simulations of particle translation and rotation and the Landau-Lifshitz-Gilbert equation describing internal magnetic dipole rotation, an approach that is currently unexplored. The proposed work is also innovative because these computer simulation platforms will be used to explore the dependence of the MPI signal on MNP properties and MPI magnetic field conditions, yielding design rules to guide development of future generations of MPI tracers and MPI applications.

项目成果

Ferrohydrodynamic modeling of magnetic nanoparticle harmonic spectra for magnetic particle imaging.

用于磁性粒子成像的磁性纳米粒子谐波谱的铁流体动力学建模。

• 发表时间: 2015-11-07
• 影响因子: 3.2
• 作者: Dhavalikar, Rohan;Maldonado;Garraud, Nicolas;Rinaldi, Carlos
• 通讯作者: Rinaldi, Carlos

Design and validation of magnetic particle spectrometer for characterization of magnetic nanoparticle relaxation dynamics.

用于表征磁性纳米粒子弛豫动力学的磁性粒子光谱仪的设计和验证。

• 发表时间: 2017-05
• 影响因子: 1.6
• 作者: Garraud, Nicolas;Dhavalikar, Rohan;Maldonado;Arnold, David P;Rinaldi, Carlos
• 通讯作者: Rinaldi, Carlos

Thermal Decomposition Synthesis of Iron Oxide Nanoparticles with Diminished Magnetic Dead Layer by Controlled Addition of Oxygen.

通过控制氧的添加热分解合成具有减少磁死层的氧化铁纳米粒子。

• DOI: 10.1021/acsnano.7b00609
• 发表时间: 2017-02-28
• 期刊: ACS nano
• 影响因子: 17.1
• 作者: Unni M;Uhl AM;Savliwala S;Savitzky BH;Dhavalikar R;Garraud N;Arnold DP;Kourkoutis LF;Andrew JS;Rinaldi C
• 通讯作者: Rinaldi C

Benchtop magnetic particle relaxometer for detection, characterization and analysis of magnetic nanoparticles.

台式磁性粒子松弛计，用于磁性纳米粒子的检测、表征和分析。

• 发表时间: 2018-09-06
• 影响因子: 3.5
• 作者: Garraud, Nicolas;Dhavalikar, Rohan;Unni, Mythreyi;Savliwala, Shehaab;Rinaldi, Carlos;Arnold, David P
• 通讯作者: Arnold, David P

Magnetic Particle Imaging-Guided Heating in Vivo Using Gradient Fields for Arbitrary Localization of Magnetic Hyperthermia Therapy.

磁粒子成像引导体内加热利用梯度场进行磁热疗的任意定位。

• DOI: 10.1021/acsnano.8b00893
• 发表时间: 2018-04-24
• 期刊: ACS nano
• 影响因子: 17.1
• 作者: Tay ZW;Chandrasekharan P;Chiu-Lam A;Hensley DW;Dhavalikar R;Zhou XY;Yu EY;Goodwill PW;Zheng B;Rinaldi C;Conolly SM
• 通讯作者: Conolly SM