A sparse reconstruction algorithm for superparamagnetic relaxometry

项目摘要 超顺磁弛豫法(SPMR)是一种新型纳米粒子成像技术，它利用 生物靶向超顺磁性纳米颗粒的磁特性 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，在如何解决这个逆问题以确定哪一个逆问题方面存在知识缺口 许多可能的解决方案代表了束缚粒子的真实位置。这个项目的长期目标是 是将SPMR作为癌症的早期检测技术应用于临床。这个项目的目标是 开发一种算法，可以在3维空间重建与癌症结合的纳米颗粒的位置 而不需要事先知道具有结合粒子的位置的数量。这项工作的假设是一个 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三维分布。我们计划用以下方法来检验这一假设 这些具体目标：具体目标1：开发一个经过实验证实的正向模型。前锋 稀疏重建算法的模型将基于Biot-Savart定律对 MRX设备的物理条件。然后对模型进行调整，以最好地模拟从 这个装置。具体目标2：应用和表征逆算法的性能。稀疏的 将实施重建算法，从返回的信号中重建粒子的分布 被探测器发现。该算法在一系列环境和环境中的灵敏度、分辨率和准确性 然后将对用户定义的变量进行特征化和优化。这些目标的预期结果是 一种新的重建算法，将显著改善磁源的定位和量化 松弛测量法。开发一种稳健和具有良好特性的重建方法将产生积极的影响 SPMR在图像制导和新的早期检测中的可能应用 技巧。所获得的算法的最小可检测性的知识和特征 它对环境噪声的响应和参数的优化将为未来的设计提供参考 临床前研究的实验。该项目开发的健壮重建算法将 使这项新技术更接近于实现其检测早期疾病的潜力 敏感性和特异性。

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.