Microtesla MRI of the human brain combined with MEG

One of the challenges in functional brain imaging is integration of complementary imaging modalities, such as magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). MEG, which uses highly sensitive superconducting quantum interference devices (SQUIDs) to directly measure magnetic fields of neuronal currents, cannot be combined with conventional high-field MRI in a single instrument. Indirect matching of MEG and MRI data leads to significant co-registration errors. A recently proposed imaging method - SQUID-based microtesla MRI - can be naturally combined with MEG in the same system to directly provide structural maps for MEG-localized sources. It enables easy and accurate integration of MEG and MRI/fMRI, because microtesla MR images can be precisely matched to structural images provided by high-field MRI and other techniques. Here we report the first images of the human brain by microtesla MRI, together with auditory MEG (functional) data, recorded using the same seven-channel SQUID system during the same imaging session. The images were acquired at 46 microtesla measurement field with pre-polarization at 30 mT. We also estimated transverse relaxation times for different tissues at microtesla fields. Our results demonstrate feasibility and potential of human brain imaging by microtesla MRI. They also show that two new types of imaging equipment - low-cost systems for anatomical MRI of the human brain at microtesla fields, and more advanced instruments for combined functional (MEG) and structural (microtesla MRI) brain imaging - are practical.Comment: 8 pages, 5 figures - accepted by JM

In vivo Observation of Tree Drought Response with Low-Field NMR and Neutron Imaging

Using a simple low-field NMR system, we monitored water content in a livingtree in a greenhouse over two months. By continuously running thesystem, we observed changes in tree water content on a scale of halfan hour. The data showed a diurnal change in water content consistentboth with previous NMR and biological observations. Neutron imaging experiments showthat our NMR signal is primarily due to water being rapidly transported through the plant, and not to other sources of hydrogen, such as water in cytoplasm, or water in cell walls. After accountingfor the role of temperature in the observed NMR signal, we demonstratea change in the diurnal signal behavior due to simulated drought conditionsfor the tree. These results illustrate the utility of our system toperform noninvasive measurements of tree water content outside of a temperature controlled environment

Multi-Channel SQUID System for MEG and Ultra-Low-Field MRI

A seven-channel system capable of performing both magnetoencephalography (MEG) and ultra-low-field magnetic resonance imaging (ULF MRI) is described. The system consists of seven second-order SQUID gradiometers with 37 mm diameter and 60 mm baseline, having magnetic field resolution of 1.2-2.8 fT/rtHz. It also includes four sets of coils for 2-D Fourier imaging with pre-polarization. The system's MEG performance was demonstrated by measurements of auditory evoked response. The system was also used to obtain a multi-channel 2-D image of a whole human hand at the measurement field of 46 microtesla with 3 by 3 mm resolution.Comment: To appear in Proceedings of 2006 Applied Superconductivity Conferenc

Forward model for the superconducting imaging-surface meg system

We have recently completed a novel whole-head MEG system based on the Superconducting Imaging-Surface (SIS) concept originally proposed by van Hulsteyn, et al.[l]. The SIS concept is generally described as a source near a superconducting surface. The source field induces Meissner currents in the superconductor equivalent to a source image 'behind' the surface. A sensor (SQUIDS in our system) placed on the source-side of the SIS will measure the superposed fields from the real and image sources. A second consequence of the Meissner effect is to shield the SQUIDS sensors near the SIS from external or background fields. The shape of the SIS used in our MEG system is a hemisphere with two cut-outs at the nominal ear-locations. A brim is added around the entire periphery with a smooth 0.5 cm radius transition between brim and hemisphere. Benefits of the SIS concept over existing systems include significantly enhanced signal-to-noise as a consequence of the SIS shielding and inherently generating pseudo-first order gradient fields at the sensors. One of the most significant challenges in realizing this system has been to accurately describe how the SIS system impacts the forward physics of any source model. Two approaches have been examined. The first is a hybrid analytical and empirical model using the analytic formalism to describe the hemisphere [1] and a correction matrix derived from empirical measurements to correct for edge effects. This approach proved overly complex and difficult in practice to obtain sufficient empirical data to derive a well-conditioned correction matrix. The second approach, reported here, was to develop a boundary element model (BEM) description of the SIS using the exact as-built geometry. Each element is described by a uniform magnetization arising from a distribution of Meissner currents in the superconductor such that B{perpendicular} = 0 at the surface. B{sub i} at each element is a superposition of the source field and the fields resulting from currents in all other elements. A precision phantom was developed to test the model. Modeled and measured magnetic field distributions agreed with typically less than 1% (< 0.1% in most cases) discrepancy at all SQUID sensors for more than 60 phantom coil positions. The attached figure shows modeled and measured magnetic field distributions for 25 such phantom coils

Weld quality evaluation using a high temperature SQUID array

This paper presents preliminary data for evaluating weld quality using high temperature SQUIDS. The SQUIDS are integrated into an instrument known as the SQUID Array Microscope, or SAMi. The array consists of ll SQUIDs evenly distributed over an 8.25 mm baseline. Welds are detected using SAMi by using an on board coil to induce eddy currents in a conducting sample and measuring the resulting magnetic fields. The concept is that the induced magnetic fields will differ in parts of varying weld quality. The data presented here was collected from three stainless steel parts using SAMi. Each part was either solid, included a good weld, or included a bad weld. The induced magnetic field's magnitude and phase relative to the induction signal were measured. For each sample considered, both the magnitude and phase data were measurably different than the other two samples. These results indicate that it is possible to use SAMi to evaluate weld quality

Multi-sensor system for simultaneous ultra-low-field MRI and MEG

Magnetoencephalography (MEG) and magnetic resonance imaging at ultra-low fields (ULF MRI) are two methods based on the ability of SQUID (superconducting quantum interference device) sensors to detect femtotesla magnetic fields. Combination of these methods will allow simultaneous functional (MEG) and structural (ULF MRI) imaging of the human brain. In this paper, we report the first implementation of a multi-sensor SQUID system designed for both MEG and ULF MRI. We present a multi-channel image of a human hand obtained at 46 microtesla field, as well as results of auditory MEG measurements with the new system.Comment: To appear in Proceedings of 15th International Conference on Biomagnetis

SQUIDs in biomagnetism : A roadmap towards improved healthcare

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 686865.Peer reviewedPublisher PD

Localization precision of the superconducting imaging-surface MEG system

A unique whole-head Magnetoencephalography (MEG) system incorporating a superconducting imaging surface (SIS) has been designed and built at Los Alamos with the goal of dramatically improving source localization accuracy while mitigating limitations of current systems (e.g. low signal-to-noise, cost, bulk). 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. The Los Alamos SIS-MEG system[2] is based on the principal that fields from nearby sources measured by a SQUID sensor array while the SIS 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 SQUIDs from sources outside the helmet while measuring fields from nearby sources within the helmet. We have implemented a finite element model (FEM) description of the SIS using the exact as-built geometry to accurately describe how the SIS impacts the forward physics of source models. The FEM is used to calculate the distribution of Meissner currents in the complicated surface geometry of the SIS such that B{perpendicular} = 0 at the surface. This model of the forward physics is described elsewhere in these proceedings [3]. In this paper, we present the results of localizing well characterized phantom sources using the SIS-MEG system, the SIS forward model, and a simple inverse method

Performance of a novel squid-based superconducting imaging-surface magnetoencephalography system

Performance for a recently completed whole-head magnetoencephalography system using a superconducting imaging-surface (SIS) surrounding an array of 150 SQUID magnetometers is reported. The helmetlike SIS is hemispherical in shape with a brim. Conceptually, the SIS images nearby sources onto the SQUIDs while shielding sensors from distant 'noise' sources. A finite element method (FEM) description using the as-built geometry was developed to describe the SIS effect on source fields by imposing B(surface)=0. Sensors consist of 8mm x 8mm SQUID magnetometers with 0.84nT/F sensitivity and <3fT/vHz noise. A series of phantom experiments to verify system efficacy have been completed. Simple dry-wire phantoms were used to eliminate model dependence from our results. Phantom coils were distributed throughout the volume encompassed by the array with a variety of orientations. Each phantom coil was precisely machined and located to better than 25{micro}m and 10mRad accuracy. Excellent agreement between model-calculated and measured magnetic field distributions of all phantom coil positions and orientations was found. Good agreement was found between modeled and measured shielding of the SQUIDs from sources external to the array showing significant frequency-independent shielding. Phantom localization precision was better than 0.5mm at all locations with a mean of better than 0.3mm