IMAGING MAGNETIC SOURCES IN THE PRESENCE OF SUPERCONDUCTING SURFACES: MODEL & EXPERIMENT

The forward physics model describing the effect of a superconducting surface on the magnetic field distribution resulting from specific magnetic sources has numerous applications ranging from basic physics experiments to large superconducting magnets used in energy storage and magnetic resonance imaging. In this paper, we describe the novel application of a superconducting imaging surface (SIS) to enhance the performance of systems designed to directly observe and localize human brain function. Magnetoencephalography (MEG) measures the weak magnetic fields emanating from the brain as a direct consequence of the neuronal currents resulting from brain function[1]. The extraordinarily weak magnetic fields are measured by an array of SQUID (Superconducting QUantum Interference Device) sensors. The position and vector characteristics of these neuronal sources can be estimated from the inverse solution of the field distribution at the surface of the head. In addition, MEG temporal resolution is unsurpassed by any other method currently used for brain imaging. Although MEG source reconstruction is limited by solutions of the electromagnetic inverse problem, constraints used for source localization produce reliable results. A novel MEG system incorporating a SIS has been designed and built at Los Alamos with the goal of dramatically improving source localization accuracy while mitigating limitationsmore » of current systems (e.g. low signal-to-noise, cost, bulk). We incorporate shielding and source field measurement into an integrated design and combine the latest SQUID and data acquisition technology. The Los Alamos MEG system is based on the principal that fields from nearby sources measured by a SQUID sensor array while the SIS simultaneously shields the sensor array from distant noise fields. In general, Meissner currents flow in the surface of superconductors, preventing any significant penetration of magnetic fields. A hemispherical SIS with a brim, or helmet, surrounds the SQUID sensor array largely sheilding the SQUID sensors from sources outside the helmet. We present the general derivation of the forward model used to describe the effect of a SIS on source fields. Experimental data for the SIS-MEG system are compared with computed field distributions for a comprehensive set of sources.« less