title: Development of advanced signal processing and source imaging methods for superparamagnetic relaxometry.
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.
authors.affiliation: Radiology and Research Services, VA San Diego Healthcare System, San Diego, CA, USA. Department of Radiology, University of California, San Diego, CA, USA.
authors.forename: Ming-Xiong
authors.initials: MX
authors.lastname: Huang
authors.forename: Bill
authors.initials: B
authors.lastname: Anderson
authors.forename: Charles W
authors.initials: CW
authors.lastname: Huang
authors.forename: Gerd J
authors.initials: GJ
authors.lastname: Kunde
authors.forename: Erika C
authors.initials: EC
authors.lastname: Vreeland
authors.forename: Jeffrey W
authors.initials: JW
authors.lastname: Huang
authors.forename: Andrei N
authors.initials: AN
authors.lastname: Matlashov
authors.forename: Todor
authors.initials: T
authors.lastname: Karaulanov
authors.forename: Christopher P
authors.initials: CP
authors.lastname: Nettles
authors.forename: Andrew
authors.initials: A
authors.lastname: Gomez
authors.forename: Kayla
authors.initials: K
authors.lastname: Minser
authors.forename: Caroline
authors.initials: C
authors.lastname: Weldon
authors.forename: Giulio
authors.initials: G
authors.lastname: Paciotti
authors.forename: Michael
authors.initials: M
authors.lastname: Harsh
authors.forename: Roland R
authors.initials: RR
authors.lastname: Lee
authors.forename: Edward R
authors.initials: ER
authors.lastname: Flynn
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D009682:Magnetic Resonance Spectroscopy / Q000295:instrumentation*; D058185:Magnetite Nanoparticles; D057054:Molecular Imaging / Q000379:methods*; D019047:Phantoms, Imaging; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*
chemical_list: D058185:Magnetite Nanoparticles