Abstract B31: A preprocessing filter to improve superparamagnetic relaxometry (SPMR)-based early tumor detection

Abstract

Abstract The early detection of aggressive forms of ovarian cancer before they metastasize is critical for reducing overall mortality from the disease. Superparamagnetic relaxometry (SPMR) is an imaging technique useful for visualizing early-stage tumors with high sensitivity and specificity. It uses superconducting quantum interference devices (SQUIDs) to detect targeted superparamagnetic iron oxide nanoparticles (SPIONs) that visualize tumors ten times smaller than what conventional imaging techniques can. However, the ultra-sensitivity of SQUIDs increases their risk of distortion due to far-field artifacts. Therefore, a preprocessing filter was developed to mitigate far-field, low-frequency disturbances to SQUID signal acquisition. This is based on the hypothesis that correcting SQUID signal acquisition using a magnetometer for far-field detection will increase the accuracy of SPMR for early tumor detection. The hypothesis was tested in three steps. First, it was shown that the magnetometer (MAG) could specifically detect far-field noise and effectively avoid nanoparticle signatures. Second, high- and low-frequency noise was induced to show that far-field artifacts in the MAG signal correlated with distortions in the SQUID channels. Therefore, a processing filter was developed to parse through and parameterize MAG signal extrema to SQUID signal distortions. A series of further optimization steps included anchoring the MAG signal to respective channels, modeling and subtracting the component of structural (environmental) relaxation, and constraining a general subtraction window. Third, success was measured by the image reconstruction accuracy of sources with various nanoparticle concentrations, using the HSPMR dipole-fitting technique. Overall, the MAG-filter increased reconstruction accuracy more effectively with decreasing nanoparticle signal; accuracy increased the most at very low concentrations (~1ug). Therefore, these preliminary data indicate that the MAG-filter increases SPMR sensitivity for low concentrations representative of very small clusters of cells, typical of early disease stages. Future work will further optimize this initial filter to work uniformly and effectively across different nanoparticle concentrations (and tumor sizes) and translate this technology to highly sensitive early and sparse tumor detection. Note: This abstract was not presented at the conference. Citation Format: Mehdi Baqri, Sri Kandala, David Fuentes. A preprocessing filter to improve superparamagnetic relaxometry (SPMR)-based early tumor detection [abstract]. In: Proceedings of the AACR Special Conference on the Evolving Landscape of Cancer Modeling; 2020 Mar 2-5; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2020;80(11 Suppl):Abstract nr B31.