Abstract
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.
Highlights
Superparamagnetic relaxometry (SPMR) is a technology that measures the time decay of superparamagnetic iron oxide nanoparticles (SPION) (Flynn and Bryant 2005, Koetitz et al 1999)
A procedure based on a reduced chi-square cost-function was introduced to objectively obtain the necessary number of dipoles. To further compliment these approaches, we develop pre-processing tools for: 1) removing bad trials that were contaminated with sensor and/or system noises, 2) validating and ensuring that a single decay process associated with bounded SPION exists in the data, 3) reducing the sensor noise, and 4) accurately fitting the sensor signals with different decay models
The pre-processing tools were developed to: 1) remove trials contaminated with artifacts, 2) evaluate and ensure one decay process exists in the data, 3) automatically detect and fix flux jumps in sensor signal, and 4) accurately fit the sensor signals with different decay models
Summary
Superparamagnetic relaxometry (SPMR) is a technology that measures the time decay of superparamagnetic iron oxide nanoparticles (SPION) (Flynn and Bryant 2005, Koetitz et al 1999). In SPMR measurements, a brief magnetizing pulse is used to align the SPION. An array of superconducting quantum interference device (SQUID) sensors detect the decaying magnetization fields. The decaying field measurements can be used to localize the accumulation of the underlying SPION. For bounded SPION, the decay process is usually long and can last several seconds due to the Neel mechanism (Neel 1955). For unbounded SPION, the decay process is short and dominated by Brownian motion, lasting several ms to a few 10s of ms. Such a substantial difference in the decay time scales allows SPMR to differentiate and localize bounded SPION from the unbounded ones
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