A sparse reconstruction algorithm for superparamagnetic relaxometry

超顺磁弛豫测量的稀疏重建算法

基本信息

批准号：
9319535
负责人：
Sara Lynn Loupot Thrower
金额：
$ 2.8万
依托单位：
UNIVERSITY OF TX MD ANDERSON CAN CTR
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-08-01 至 2018-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9319535
关键词：
3-Dimensional ABCG2 gene Algorithms Biological Biomedical Technology Cells Characteristics Clinic Collection Computer Simulation Data Development Devices Disease Early Diagnosis Electromagnetics Environment Future Geometry Goals Imaging Techniques Knowledge Laws Learning Skill Literature Location Magnetism Malignant Neoplasms Measurement Measures Methods Modeling Noise One-Step dentin bonding system Outcome Performance Physics Property Research Research Personnel Resolution Sensitivity and Specificity Signal Transduction Site Source System Techniques Technology Testing Training Translating Translations Uncertainty Work base cancer cell cancer site career clinical application design detector experimental study high risk image guided improved magnetic dipole mouse model nanoparticle new technology novel particle pre-clinical trial preclinical study reconstruction response source localization superconducting quantum interference device treatment response tumor

项目摘要

Project Summary Superparamagnetic relaxometry (SPMR) is a novel nanoparticle imaging technique that utilizes the magnetic properties of biologically targeted superparamagnetic nanoparticles to potentially detect as few as 15,000 cancer cells. Source reconstruction in SPMR requires solving the ill-posed magnetic inverse problem. There is currently a gap in knowledge about how to solve this inverse problem in order to determine which of the many possible solutions represents the true location of bound particles. The long-term goal of this project is to translate SPMR into the clinic as an early detection technique for cancer. The objective for this project is to develop an algorithm that can reconstruct the location of cancer-bound nanoparticles in 3 dimensions without any prior knowledge of the number of sites with bound particles. The hypothesis of the work is that a sparse reconstruction algorithm based on physics models and tuned to the SPMR environment will reliably reconstruct the 3-dimensional distribution of cancer-bound nanoparticles. We plan to test this hypothesis with these specific aims: Specific Aim 1: Develop an experimentally informed forward model. The forward model for the sparse reconstruction algorithm will be based on the application of the Biot-Savart law to the physical conditions of the MRX device. The model will then be adjusted to best simulate data collected from the device. Specific Aim 2: Apply and characterize the performance of the inverse algorithm. A sparse reconstruction algorithm will be implemented to reconstruct the distribution of particles from the signal returned by the detectors. The sensitivity, resolution and accuracy of the algorithm across a range of environmental and user-defined variables will then be characterized and optimized. The expected outcome of these aims is a novel reconstruction algorithm that will significantly improve source localization and quantification in magnetic relaxometry. The development of a robust and well characterized reconstruction method will positively impact the field of SPMR by opening it up to possible applications in image guidance and novel early detection techniques. The knowledge that gained of the minimum detectability of the algorithm and the characterization of its response with respect to environmental noise and optimization parameters will inform the design of future experiments towards preclinical studies. The robust reconstruction algorithm developed by this project will bring this novel technology one step closer to realizing its potential to detect early disease with unparalleled sensitivity and specificity.

项目概要超顺磁弛豫测量（SPMR）是一种新颖的纳米粒子成像技术，它利用生物靶向超顺磁性纳米颗粒的磁性，有可能检测到少至 15,000 个癌细胞。 SPMR 中的源重建需要解决不适定的磁逆问题。目前关于如何解决这个反问题以确定哪一个是知识上的差距许多可能的解决方案代表了束缚粒子的真实位置。本项目的长期目标是将 SPMR 作为癌症早期检测技术转化为临床。该项目的目标是开发一种算法，可以在 3 维中重建癌症结合纳米颗粒的位置事先不知道结合颗粒的位点数量。这项工作的假设是基于物理模型并适应 SPMR 环境的稀疏重建算法将可靠地重建癌症结合纳米颗粒的 3 维分布。我们计划用以下方法来检验这个假设这些具体目标：具体目标 1：开发一个基于实验的前向模型。前锋稀疏重建算法的模型将基于毕奥-萨伐尔定律的应用 MRX 设备的物理条件。然后将调整模型以最好地模拟从以下位置收集的数据设备。具体目标 2：应用并表征逆算法的性能。稀疏的将实施重建算法以根据返回的信号重建粒子的分布通过探测器。该算法在各种环境和环境下的灵敏度、分辨率和准确性然后将对用户定义的变量进行表征和优化。这些目标的预期成果是新颖的重建算法将显着改善磁源定位和量化松弛测量法。开发稳健且特征良好的重建方法将产生积极影响 SPMR 领域，开放其在图像引导和新型早期检测中的可能应用技术。获得算法的最小可检测性和表征的知识它对环境噪声和优化参数的响应将为未来的设计提供信息临床前研究的实验。该项目开发的鲁棒重建算法将使这项新技术更接近于实现其以无与伦比的能力检测早期疾病的潜力敏感性和特异性。