Development of advanced signal processing and source imaging methods for superparamagnetic relaxometry.
Development of advanced signal processing and source imaging methods for superparamagnetic relaxometry.
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开发用于超顺磁弛豫测量的先进信号处理和源成像方法。
DOI：
10.1088/1361-6560/aa553b
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发表时间：
2017
影响因子：
3.5
通讯作者：
Lee,
中科院分区：
文献类型：
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作者：
Huang,Ming-Xiong;Anderson,Bill;Huang,CharlesW;Kunde,GerdJ;Vreeland,ErikaC;Huang,JeffreyW;Matlashov,AndreiN;Karaulanov,Todor;Nettles,ChristopherP;Gomez,Andrew;Minser,Kayla;Weldon,Caroline;Paciotti,Giulio;Harsh,Michael;Lee,
超顺磁弛豫仪（SPMR）是一种高灵敏度的肿瘤细胞体内检测技术，可以提高癌症的早期检测。SPMR采用超顺磁性氧化铁纳米颗粒（SPION）。SPMR用一个短暂的磁化脉冲对准SPION后，利用超导量子干涉装置（SQUID）传感器测量SPION的时间衰减。在开发SQUID硬件和改进SPION性能方面进行了大量的研究。然而，SPMR中对传感器信号的预处理和后处理源建模的研究很少。在本研究中，我们展示了开发的新的预处理工具：(1)去除受伪影污染的试验，(2)评估并确保数据中存在与有界SPION相关的单一衰减过程，(3)自动检测和纠正通量跳变，以及(4)准确拟合不同衰减模型的传感器信号。此外，我们开发了一种基于多起点偶极子成像技术的自动化方法，无需用户初始猜测即可获得多个磁源的位置和震级。实现了正则化过程，以解决与SPMR源变量相关的模糊问题。引入了一种基于简化卡方代价函数的方法来客观地获得描述数据的足够数量的偶极子。新的预处理工具和多启动源成像方法已经成功地使用幻象数据进行了评估。总之，这些工具和多启动源建模方法大大提高了从SPMR信号中检测和定位源的准确性和灵敏度。此外，正则化的多启动方法为类似于SPMR检测灵敏度在1000个单元数量级的低信噪比条件下提供了鲁棒和准确的解决方案。我们相信这些算法将有助于在临床前和临床环境中应用SPMR技术时建立行业标准。
Superparamagnetic relaxometry (SPMR) is a highly sensitive technique for the in vivo detection of tumor cells and may improve early stage detection of cancers. SPMR employs superparamagnetic iron oxide nanoparticles (SPION). After a brief magnetizing pulse is used to align the SPION, SPMR measures the time decay of SPION using super-conducting quantum interference device (SQUID) sensors. Substantial research has been carried out in developing the SQUID hardware and in improving the properties of the SPION. However, little research has been done in the pre-processing of sensor signals and post-processing source modeling in SPMR. In the present study, we illustrate new pre-processing tools that were developed to:(1) remove trials contaminated with artifacts,(2) evaluate and ensure that a single decay process associated with bounded SPION exists in the data,(3) automatically detect and correct flux jumps, and (4) accurately fit the sensor signals with different decay models. Furthermore, we developed an automated approach based on multi-start dipole imaging technique to obtain the locations and magnitudes of multiple magnetic sources, without initial guesses from the users. A regularization process was implemented to solve the ambiguity issue related to the SPMR source variables. A procedure based on reduced chi-square cost-function was introduced to objectively obtain the adequate number of dipoles that describe the data. The new pre-processing tools and multi-start source imaging approach have been successfully evaluated using phantom data. In conclusion, these tools and multi-start source modeling approach substantially enhance the accuracy and sensitivity in detecting and localizing sources from the SPMR signals. Furthermore, multi-start approach with regularization provided robust and accurate solutions for a poor SNR condition similar to the SPMR detection sensitivity in the order of 1000 cells. We believe such algorithms will help establishing the industrial standards for SPMR when applying the technique in pre-clinical and clinical settings.