Abstract 888: Volumetric reconstruction of targeted nanoparticles for superparamagnetic relaxometry

Superparamagnetic relaxometry (SPMR) is an emerging technology that uses the unique magnetic properties of superparamagnetic iron oxide nanoparticles (SPIONs) to detect cancer cells. In order to estimate tumor locations from raw MRX data, we developed an L1 reconstruction algorithm under the assumption that early stage disease is sparsely distributed throughout the anatomy. The approach was previously validated in phantom datasets of known signal locations. Advantages of our method are that the solver does not require the user to input prior information regarding the expected number of tumors or their approximate locations. Additionally, the solver reconstructs a volumetric distribution of detected sources within the field of view. To validate the algorithm for use in preclinical settings, SPMR was performed on SKOV3 ovarian tumor bearing mice (n = 3) with the MRX device over time following an intratumoral injection of anti-Her2 antibody-conjugated 25nm SPIONs (Senior Scientific LLC). The SPMR data was reconstructed with our sparse solver and was found to be highly correlated (r = 0.9978) with the results generated by the commercial software that accompanies the MRX instrument (MSA). Additionally, segmentation of the reconstruction revealed a strong signal (2.0·106 pJ/T) in the area of the tumor and almost no signal in areas outside of the tumor (0.077 pJ/T) at four hours after injection. This result was consistent with our prior observations which have revealed that a large fraction of intratumorally-injected nanoparticles remain localized within the tumor for several hours after injection. Furthermore, these results were consistent with SPMR data collected by measuring tissue samples excised 24 hours after injection, of which the tumor had the highest signal. Thus, our sparse reconstruction algorithm was able to return the expected results without prior information regarding the location of nanoparticles. Future work will focus on quantifying the uncertainty in our reconstruction method, as well as characterizing its stability with increasingly complex nanoparticle distributions and detectability limits. In conclusion, this work represents an important advancement of the SPMR technology by allowing for volumetric reconstructions of bound nanoparticles from in vivo data. Citation Format: Sara L. Thrower, Kelsey Mathieu, Wolfgang Stefan, R. Romero Aburto, Zhen Lu, Robert C. Bast, Javad Sovizi, David Fuentes, John D. Hazle. Volumetric reconstruction of targeted nanoparticles for superparamagnetic relaxometry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 888. doi:10.1158/1538-7445.AM2017-888