Squid Magnet

7/30/2019 Squid Magnet

http://slidepdf.com/reader/full/squid-magnet 1/15

Perspectives in Magnetic Resonance

SQUID-detected ultra-low field MRIq

Michelle Espy ⇑, Andrei Matlashov, Petr Volegov

Los Alamos National Laboratory, Los Alamos, NM 87545, United States

a r t i c l e i n f o

Article history:

Available online 15 February 2013

Keywords:

Magnetic Resonance Imaging (MRI)

Ultra-low fields (ULFs)

SQUID detection

ULF MRI

a b s t r a c t

MRI remains the premier method for non-invasive imaging of soft-tissue. Since the first demonstration of

ULF MRI the trend has been towards ever higher magnetic fields. This is because the signal, and efficiency

of Faraday detectors, increases with ever higher magnetic fields and corresponding Larmor frequencies.Nevertheless, there are many compelling reasons to continue to explore MRI at much weaker magnetic

fields, the so-called ultra-low field or (ULF) regime. In the past decade many excellent proof-of-concept

demonstrations of ULF MRI have been made. These include combined MRI and magnetoencephalography,

imaging in the presence of metal, unique tissue contrast, and implementation in situations where a high

magnetic field is simply impractical. These demonstrations have routinely used pulsed pre-polarization

(at magnetic fields from $10 to 100 mT) followed by read-out in a much weaker (1–100 lT) magnetic

fields using the ultra-sensitive Superconducting Quantum Interference Device (SQUID) sensor. Even with

pre-polarization and SQUID detection, ULF MRI suffers from many challenges associated with lower mag-

netization (i.e. signal) and inherently long acquisition times compared to conventional >1 T MRI. These

are fundamental limitations imposed by the low measurement and gradient fields used. In this review

article we discuss some of the techniques, potential applications, and inherent challenges of ULF MRI.

Published by Elsevier Inc.

1. Overview

In Magnetic Resonance Imaging (MRI) the use of stronger mag-

netic fields, B p, for sample polarization results in a linear increase

in the equilibrium magnetic moment in a voxel, M eq (and hence

the acquired signal)

M eq ¼N h

2c2I ðI þ 1ÞB p3kBT

ð1Þ

where N is the number of spins in a voxel, c is the gyromagnetic ra-

tio, I the spin number, kB is the Boltzmann constant and T the tem-

perature. The increased signal can be used for faster acquisition

and/or better resolved images. Thus, the vast majority of MRI ma-

chines employ fixed strength, high field (HF) magnets of as high a

field as practically achievable. For this article, we define HF as>1 T. In addition, the performance of Faraday coils used as detectors

in HF MRI increases with magnetic field strength [1]. Thus, trying to

perform conventional MRI at lower magnetic field strengths results

in a penalty in acquired signal that scales as$x20 (wherex0 = cB0 is

the Larmor frequency, c is the gyromagnetic ratio, and B0 is themagnetic field in which the spins precess). Most clinical HF MRI sys-

tems are based on large and highly uniform (ppm) superconducting

magnets and the polarization and measurement field in which spins

precess are the same. Typically B0 = B p = 1.5 or 3 T in these systems,

which results in a proton Larmor frequency of $64–128 MHz.

However, there remain numerous MRI applications where high

field is not an option, for example imaging in the presence of metal

or where a large and expensive magnet might be impractical. In the

early 90s low field MRI based on pulsed pre-polarization to in-

crease signal (followed readout at an even lower magnetic field)

was proposed as a low cost alternative to conventional HF MRI

[2]. A few years later, the ultra-sensitive SQUID (Superconducting

Quantum Interference Device) sensor was shown as a potential ap-

proach to improve detection at the low Larmor frequencies thataccompany the lower magnetic fields [3]. In the early 2000s the

group of John Clarke significantly advanced the concept of

SQUID-based MRI at ultra-low fields (ULFs), with readout magnetic

fields as low as 132 lT and using pulsed pre-polarization [4].

Because of the broad-band nature of SQUID detection, almost all

recent ULF MRI demonstrations have focused on the approach of

SQUID detection in lT readout fields (proton Larmor frequencies

in the Hz to kHz range), with pulsed pre-polarization ($0.01–

0.2 T) to increase signal. Numerous ULF MRI applications using this

approach have been demonstrated by us and others, including

imaging the human brain [5–7]. The use of SQUID detection natu-

rally opens up the possibility of combined magnetoencephalogra-

1090-7807/$ - see front matter Published by Elsevier Inc.http://dx.doi.org/10.1016/j.jmr.2013.02.009

DOI of original article: http://dx.doi.org/10.1016/j.jmr.2012.11.030q A publishers error resulted in this article appearing in the wrong issue. The

article is reprinted here for the reader’s convenience and for the continuity of the

special issue. For citation purposes, please use Journal of Magnetic Resonance, 228,

pp. 1-15.⇑ Corresponding author. Address: LANL, MS-D454, Los Alamos, NM 87545, United

States.

E-mail address: [email protected] (M. Espy).

Journal of Magnetic Resonance 229 (2013) 127–141

Contents lists available at SciVerse ScienceDirect

Journal of Magnetic Resonance

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / j m r

http://dx.doi.org/10.1016/j.jmr.2013.02.009


mailto:[email protected]


http://www.sciencedirect.com/science/journal/10907807

http://www.elsevier.com/locate/jmr

http://www.elsevier.com/locate/jmr

http://www.sciencedirect.com/science/journal/10907807


mailto:[email protected]





phy (MEG) and MRI, which is simply not possible in the HF regime

due to the incompatibility of the SQUID (required for the MEG)

with the large magnetic fields of the MRI. Combining the functional

measurement of MEG and anatomical MRI confers advantages to

either modality [6].

MRI of the human brain at 94 lT [5] with interleaved MEG, ac-

quired by our team at Los Alamos National Laboratory, is shown in

Fig. 1. Pre-polarization was at 30 mT. The ULF images are presentedside-by-side with images acquired at 3 T and highlight some of the

differences (good and bad) between ULF and HF MRI. For example,

spatial resolution is clearly worse, but MEG can be acquired at ULF

(not possible at all with HF). We will discuss these differences in

more detail in the next section.

Despite the drawbacks in terms of lower signal, and the added

complexity of SQUID detection compared to a Faraday coil, ULF

MRI may confer critical advantages for some applications. These

include: (1) Unique MRI contrast arising from the overlap of the Lar-

mor frequency with the msec-sec dynamics of molecular or phys-

iological processes not accessible by fixed field MRI orconventional field cycling methods [8–10]. T 1 dispersion with

[11,12] and without [8,9,13] contrast agents, and resonant contrast

produced by overlapping the Larmor frequency with biomagnetic

Fig. 1. (Left column) 3 T HF MRI and (right column) 94 lT ULF MRI slices. For the ULF MRI, averages of 5 scans are shown. (Bottom right panel) a dipole fit of the N100 m fieldmap to an auditory stimulus. Data are from [5].

128 M. Espy et al. / Journal of Magnetic Resonance 229 (2013) 127–141



processes of interest [14]. (2) Flexible instrumentation compatible

with applications precluded by high field approaches. This includes

imaging through or in the presence of metal [15,16] (e.g. Fig. 2),

compatibility with imaging modalities like MEG [5–7], and the

ability to reduce, remove, or reorient magnetic fields for applica-

tions such as portable or bedside MRI using novel pulse sequences.

Because the magnetic fields are low, susceptibility artifacts are also

greatly reduced. This may be useful for situations where such arti-facts are a problem (e.g. near lungs) but bad for methods like BOLD

fMRI that rely on susceptibility. (3) The potential for lower cost ma-

chines. As liquid helium (LH) supplies and rare earth magnet mate-

rial become more costly and scarce, the expense of field generation

for high field MRI may further increase. SQUID-based systems re-

quire only tens of liters of LH and potentially such systems can em-

ploy recycling or cryocoolers [17].

Of course, there is a reason that the use of higher and higher

magnetic fields is (and likely will continue to be) the dominating

approach to MRI. The advantages of ULF MRI must be balanced

against the challenges arising from loss of signal, difficulties asso-

ciated with pulsed magnets, SQUIDs operating in a dynamic mag-

netic environment, shorter relaxation and bandwidth, etc. In this

paper we aim to present some of these considerations in terms

of instrumentation and pulse sequence development requirements

in the ULF regime. We will discuss recent progress in ULF MRI, and

conclude with what appear to be the promising future directions. It

is our hope to give the reader a reasonable picture of what is (and

might not be) possible for SQUID-detected ultra-low field MRI.

2. Imaging considerations in ULF MRI (why is it so hard?)

Differences between imaging systems and methodologies make

direct comparison between images difficult. But to provide the

general flavor of how MRI scales with field, let us return to Fig. 1.

Table 1 lists some of the parameters between the HF and ULF

images shown in this Figure. It is immediately obvious that the

overall quality of the 3 T image seems ‘‘better’’. Single scan sig-nal-to-noise (SNR) is higher and voxel size is smaller for the 3 T

image. This is not totally surprising given the 100-fold increase

in polarization field (3 T vs. 30 mT). In fact the optimist might be

surprised the ULF MRI is not worse!

But comparing images is not straightforward. Several things be-

tween these images are different. We took fewer steps at ULF, lar-

gely driven by the fact that the acquisition time was 5 times longer

and it would have taken a really long time; each single scan was

$10 min, and the image shown is an average of 5 scans. The longer

acquisition time is driven by bandwidth limitations, compared to

HF MRI. What does this mean? Imagine for a moment that we have

a ball of water that we want to image. To produce a 1D image we

will apply a gradient in our MRI machine. In a HF scanner, 3 T, gra-dients are typically 10À2 T/m. Using the fact that the gyromagnetic

ratio is 42.6 MHz/T for protons, this gradient is about 426 kHz/m,

so if the ball is .10 m in diameter, the frequency spread is 42 kHz

across the sample. From the Nyquist theorem we know that we

will need to sample for

t a ¼1

2Dxð2Þ

where t a is the acquisition time, and Dx is the width of the fre-

quency bin. To take 100 imaging steps we will have Dx = 420 Hz

per step and t a is 1.2 ms. On the other hand in ULF MRI our gradi-

ents are usually $100 times smaller. The frequency spread across

the sample is 420 Hz. For the same number of steps we will have

Dx = 4.2 Hz per step, and thus we have to sample for 120 ms forequivalent sampling. This makes imaging at ULF inherently slower.

Fig. 2. (Left) ULF MRI of hand at $100lT. The right panel is a photograph of the hand for which the MRI was acquired showing a 4 mm diameter titanium rod that was placedon top of the hand during acquisition of the image. The presence of such metal in typical MRI scanner would have made the acquisition virtually impossible.

Table 1

Image parameters from Fig. 1.

Field strength 3 T 30 mT (94lT)

Voxel size (mm3) 1 Â 1 Â 6 3Â 3 Â 6

N x (readout steps) 128 90

N y,z (phase steps) 128Â 128 51Â 9

t a (ms) $10 56

Gradients $10À2 T/m $10À4 T/m

SNR (voxel) single scan 30 10

M. Espy et al. / Journal of Magnetic Resonance 229 (2013) 127–141 129



Further, because the relaxation time of tissues at ULF is often on the

order of 100–200 ms [9], we might only have time to complete one

acquisition step before we need to re-polarize. In HF MRI many

steps can be taken with a single polarization.

Another consideration is in the relationship between spatial

resolution, D x, and gradients.

D x ¼

2p

2ðcG xt aÞ ð3

Þ

where G x is the readout (frequency encoding) gradient that is ap-

plied during the signal acquisition.

To achieve the same spatial resolution as for HF MRI, at ULF we

either need to turn the gradients up 100 times to match HF MRI, or

acquire (in the case of the readout) or encode (in the case of the

phase encoding direction) for 100 times longer. Often we cannot

simply turn the gradients up to make D x smaller because of distor-

tions caused by concomitant gradients [18,19] and bandwidth

limitations.

Eq. (3) defines spatial resolution based on how we sample our

image. But without enough SNR, a small voxel does not convey

any information. To use Eq. (3) we assume adequate voxel SNR

(usually > 5) [20]. We can write SNR as [21]

SNRv oxel % C Á f ð geomÞ Á B p Á V Á

ffiffiffiffiffiffiffiffit

2S B

r ð4Þ

where C are the physical constants, f is a function of the geometry,

S B is the magnetic noise spectral density, B p is the pre-polarization

field, and V is the voxel volume. We are again reminded that higher

pre-polarization (or lower noise) will increase SNR, and smaller

voxels will decrease it.

This analysis, while by no means rigorous (we remind the read-

er that image comparison is a subtle business), gives the general

flavor of the issue of scaling with magnetic field; there is less sig-

nal, less bandwidth, and differences in tissue relaxation that must

be considered. Many of the practical challenges and considerations

to implementing ULF MRI are discussed in [21].

3. Hardware and pulse sequences

Imaging techniques used in ULF MRI are generally similar to

those used for traditional high field MRI. One key difference is that

the ULF NMR/MRI methods typically utilize different magnetic

fields for polarization, spin evolution, and measurement. In this re-

gard it bears some resemblance to and maintains many of the

advantages for novel contrast of Field-Cycling NMR/MRI, which re-

lies on more traditional permanent and superconducting magnet

technologies [22]. However acquiring a ‘‘useful’’ image with ULF

can be more challenging due to lower signal amplitude and less

bandwidth with which to acquire it, as we saw in the simple exam-

ple of Fig. 1. And of course, dealing with SQUID sensors in the pres-ence of relatively large and time varying magnetic fields is an

added challenge. While the definition of ‘‘useful’’ is application

dependent, ULF MRI will likely always be at a disadvantage com-

pared to HF MRI in terms of spatial resolution and SNR simply be-

cause of these challenges.

An example of the 3D Fourier Imaging pulse sequence used to

produce many of our ULF MRI images (including those shown in

Fig. 1) is shown in Fig. 3. The magnetic field coil hardware used

to implement this pulse sequence is shown schematically in

Fig. 4. B p are the magnetic field coils to generate the sample mag-

netization, in this example along the x-axis. Bm denotes the mea-

surement magnetic field coils, along the z -axis. In HF MRI this is

usually denoted as B0 but here we call it Bm to distinguish it as a

field that may or may not be the same as the polarization field.In HF MRI there is a single fixed field providing both B p and Bm,

typically provided by a large superconducting magnet. In ULF

MRI the field generation is typically produced by simple electro-

magnets and allows for different field orientations and strengths

provided by separate B p and Bm coils. The additional G x,y,z coils

are for gradient encoding in the B z /d x, y, z directions respectively.

Let us now go through the ULF MRI pulse sequence in more detail

to further highlight the similarities and differences between tradi-

tional and ULF MRI.

3.1. Pre-polarization

The first step to any NMR/MRI application, regardless of mag-

netic field strength, is recruitment of the spin population to pro-

duce a measurable magnetization. With ULF MRI, as with many

Field-Cycling methods, sample preparation is done by application

of a pre-polarization field, a method by which we can apply a lar-

ger (typically 10–200 mT) field, B p, for some time t p, to recruit more

spins. Readout is then at lower Larmor frequencies (magnetic

fields) chosen to derive certain benefits (contrast, compatibility,

penetration through metal, etc.).

For the case where we start with no initial magnetization, and

apply this field along some axis (in Fig. 4 it is the x-axis) the voxel

magnetic moment develops as

M xðt Þ ¼ M x;eqð1 À eÀt =T 1 Þ ð5Þ

where M x,eq, is the equilibrium magnetic moment (see Eq. (1)) and

T 1 is the longitudinal relaxation time. We also remind the readerthat we are discussing T 1 in the B p field.

Fig. 3. Fourier imaging sequence for ULF MRI, adapted from [6].

Fig. 4. Schematic of ULF MRI field generation hardware for the pulse sequence in

Fig. 3.




As Eq. (5) shows, the magnetization exponentially approaches

equilibrium. A common ‘‘rule of thumb’’ is to polarize for several

T 1 times to attain the maximum signal (at least 3Â T 1 is common).

While the most obvious implication of ULF MRI is lower SNR due to

small polarizing fields, T 1 (and hence contrast) also changes with

field, typically decreasing with reduced fields. Therefore, an advan-

tage at ULF is that shorter T 1 leads to shorter required polarization

times and the potential for a higher duty cycle. For example, inbrain tissue at 1.5 T, T 1 is $1 s [23] whereas at ULF is it $100 ms

[9]. Another advantage of the ULF approach is that the T 1 contrast

in the image can be selected by the user to correspond to either

that of the polarization field or the measurement field, depending

on the pulse sequence. This can provide additional information

about tissue based on how T 1 changes with field strength (T 1dispersion).

The B p field can be relatively inhomogeneous compared to HF

MRI systems. For a fixed relative homogeneity, the NMR line width

scales linearly with the strength of the measurement magnetic

field [24,25]. Thus, at the low fields of ULF MRI, quite narrow line

widths can be achieved even for a rather inhomogeneous field.

The inherent line width is proportional to the inverse of the

measured relaxation time, 1=T Ã2,

Dxl %2

T Ã2

ð6Þ

For simplicity we can consider the 1-D imaging case, where we

have two objects separated by x, and we apply a gradient G x. There

will be two peaks in the FFT separated by cG xD x. The minimum

ability to resolve them spatially will be

cG xD x ¼ Dxl ð7Þ

Thus the spatial resolution can be expressed as

D x ¼Dxl

cG x¼

2

cG xT Ã2

ð8Þ

If T

Ã

2 is dominated by T 2, the resolution can be improved only byraising the applied gradient. This is the case for gray matter at ULF

(T 1 $ 100 ms [9]). Using Eq. (8) with G x = 10À4 T/m we find

D x $ 5 mm. In the case of water T 2 is long, $3 s, and it is field inho-

mogeneities across the sample, DB, that are the primary cause of

de-phasing. Our typical ULF measurement for water is T Ã2 $ 1 s,

D x $ .5 mm. In this case making Bm more homogeneous will fur-

ther improve resolution. The field inhomogeneity of Bm can be

written as BmðDBBmÞ, where DB

Bmis the relative homogeneity. Thus

Dxl ¼ cDB ¼ cBm

DB

Bm

ð9Þ

and

D x ¼

Bm

G x

DB

Bm

ð10Þ

Typically it is much easier to generate the large B p field when

the requirement for homogeneity is removed.

However, there are considerations to producing (and removing)

B p. And in fact, some of the advantages (reduced homogeneity

requirement, shorter T 1) can be disadvantages if not accounted

for. The first consideration is that it is not trivial to make a pulsed

field at >50 mT. The coil will experience heating, the energy must

be removed, and the proximity of a large amount of conductor near

the SQUIDs can introduce noise. We have shown that the B p coil

should be physically disconnected during measurement (via relay

switch) to reduce the effect of acting like a large antenna.

There have been several approaches to producing a pulsed B p.

We have demonstrated resistive room temperature coolant (Fluor-inert) cooled, and liquid nitrogen (LN) cooled coils [26]. The LN coil

has the benefit of 7Â lower resistance, but requires the complexity

of an additional cryostat. Recently a group in Finland has shown a

self-shielded [27] pulsed superconducting coil [7] for ULF MRI,

integrated directly into the cryostat with the SQUIDs.

When the B p field is removed one must consider that transient

eddy currents will be induced in nearby conductors, which can im-

pose a long dead-time if the magnetic fields from the transients ex-

ceed the dynamic range of the SQUIDs. Further, the choice of materials for B p can be important. For example we have found that

multi-stranded Litz wire performs much better than solid wire in

terms of noise. In reference [7] the superconducting wire appeared

to become magnetized if too high a current (>12 A) were applied,

producing gradients that influenced the image quality, limiting B p

in that work to <24 mT.

Also, how one removes B p is an important consideration. In the

images shown in Fig. 1 we employed a non-adiabatic ramp-down,

dB p=dt ) cB2m such that the magnetization was left aligned with

the original direction of B p, ^ x in Fig. 4 after $10 ms shut down.

We did this for two reasons, it is the simplest possible approach

to elicit an MR signal (no spin flip coil is required), and it (in prin-

ciple) minimizes time between beginning precession and measure-

ment. If Bm is applied orthogonally as shown, precession begins

instantly after shut down.

In reality the faster one removes B p the larger the transients that

are induced in nearby conductors. In the case of combined MEG/

ULF MRI when measurements are made inside conductive magnet-

ically shielded rooms (MSRs), these transients can become a real

confound as some components can persist for hundreds of msec

[28], and are hard to de-convolve from the MEG [7]. Even in the ab-

sence of an MSR, anything conducting nearby will also support

transients and that will impact the image. In our MEG/MRI images

a relatively long wait time between the MRI and the MEG ($3 s)

was imposed due to these effects; the self-shielded approach is

likely quite important to reduce this effect [27].

One added consideration in the non-adiabatic field removal ap-

proach is the non-uniformity of B p. Not requiring a uniform B p

greatly simplifies the magnet, but signal is lost due to this non-uni-formity; when precession starts the spins are not all in phase. In

addition there are technical problems associated with the require-

ment to dissipate the energy, induced transients (the faster you

ramp down the larger), and phase stability. If instead, we ramp

down adiabatically (dB p=dt ( cB2m) the final magnetization is well

aligned with the low Bm field, which is easy to make uniform. Fur-

ther, phase coherence is typically improved due to lack of tran-

sients. A traditional spin-flip pulse is then required to start

precession. In recent demonstrations we have shown that through

the use of amplifiers, we can develop a switch off profile to both

minimize transients and maximize residual signal. Although an

adiabatic ramp down takes longer (and signal is being lost to T 1relaxation during that time) we calculate that for a 100 ms linear

ramp from 100 mT to 0 field, $85% of the signal for gray/whitematter is retained. A non-linear ramp with less time spent at low

frequencies (where relaxation is shorter) may further improve this.

Another advantage of the adiabatic approach is that the dB/dt is

lower. Thus there is less danger associated with heating or energy

deposited in the subject, a special consideration when imaging

near metal. Large dB/dt effects are capable of inducing significant

electric fields that can result in non-trivial transient currents in tis-

sue [29–31]. In a ULF MRI system B p is typically 10–200 mT and is

removed within 1–10 ms (depending on the approach). The field

changes dB/dt may range from 10–20 T/s. It is worth noting that

the FDA initially limited dB/dt to 20 T/s [32], but now these guide-

lines have been relaxed and dB/dt should avoid discomfort, pain, or

nerve stimulation [33]. Even with relatively high pre-polarization,

the dB/dt in a ULF MRI system is typically lower than that found inHF MRI systems.




One also has to consider that the SQUIDs have to survive the B p

pulse. In our applications we have connected the SQUID pick-up

coils to commercial CE2Blue SQUID sensors via cryogenic switches

[34]. Cryogenic switches are activated during pre-polarization and

become normal with 350X resistance. In our applications we have

not used self-shielded coils, but performed some compensation of

transients (induced in the MSR and instrument) by implementa-

tion of external low-frequency negative feedback [35]. One canalso use the approach of protection the SQUID from the polarizing

field by placing niobium plates above and below the SQUID chip

and/or by using flux dams [36].

Before we leave our discussion of pre-polarization it is also

worth mentioning that others have demonstrated that it may be

possible to use the pre-polarization field shape itself to encode im-

age contrast [37].

3.2. Begin precession (spin reorientation)

Upon completion of the polarization, the MRI pulse sequence

begins. As with any MRI, the measurable signal is derived from

the precession of the magnetization when spins are tipped suchthat there is a component of magnetization transverse to the ap-

plied magnetic field. This process is described by the Bloch

equations:

dM

dt ¼ cM Â B À

1

T 1ðM z À v

0B z Þz þ

1

T 2ðM x^ x þ M y^ y Þ

ð11Þ

where B z is the measurement field (here defined to be along the z -

axis), and T 1 is the value in the measurement field. The first term

describes the precession about any transverse magnetization and

the second the effects of spin relaxation.

In the implementation of the pulse sequence shown in Fig. 3 we

begin precession by removing B p (along the x-axis) non-adiabati-cally leaving the magnetization along this direction, and applying

Bm orthogonally to B p (along the z -axis). This immediately begins

the precession of the magnetization about the z -axis of the mea-

surement field at the start of the encoding period, t g . By repeating

the subsequent pulse sequence for different values of t p, and using

the initial amplitude of the precession signal, one can determine T 1at the value of the polarization field.

Alternately, if the B p ramp down had been adiabatic, spins

would end up aligned with the z -axis and a spin-reorientation (also

known as ‘‘spin tipping’’ or ‘‘spin-flip’’) would be required at the

transition between t p (polarization) and t g (gradient encoding) in

Fig. 3. The ultimate goal of spin-reorientation is to result in some

component of the magnetization orthogonal to the measurement

field. Typically 90° for maximum signal, but any component of the magnetization vector tipped away from the measurement field

axis will begin to precess, and provide a measurable signal. To ini-

tiate the spin-flip, the coil(s) are oriented orthogonal to the axis of

the polarization field. Although not shown, in the ULF MRI config-

uration in Fig. 4 a spin-flip coil would be oriented along the y-axis

orthogonal to both B p ( x-axis) and Bm ( z -axis).

The spin reorientation can be provided by either resonant or

non-resonant methods. In the resonant case, a magnetic field time

varying at the Larmor (resonant) frequency is applied in a fixed ori-

entation orthogonal to the measurement field. In the non-resonant

case a field that varies slowly in time and orientation, and does not

contain Larmor frequency content, is applied. It is worth emphasiz-

ing that the second (non-resonant) approach to spin-reorientation

is totally unique to the ULF approach. Both approaches are de-scribed below:

(1) Resonant spin-tipping : After the removal of B p a time varying

field B1 is applied orthogonally to Bm at the Larmor fre-

quency for a desired period of time to reorient the magneti-

zation. This is the typical method for spin reorientation in HF

MRI.

(2) Non-resonant spin-tipping : The non-resonant spin-reorienta-

tion can be implemented several ways. Here we present two

examples: (1) the original orientation of the measurementfield Bm is changed to some new direction adiabatically

(the field changes slowly enough that the magnetization

can follow). The orientation of Bm is then non-adiabatically

restored to its original orientation, leaving the magnetiza-

tion ‘‘tipped’’. (2) B p and Bm are orthogonal, as shown in

Fig. 4 and the non-adiabatic switch off of B p is followed by

application of orthogonal Bm. Spins remain at 90° and simply

begin precession.

3.3. Encoding and acquisition

In order to realize the spatial information in the image, one

must apply and vary the gradients along multiple axes that spa-

tially encode the NMR signal, and collect the data before the signal

has dephased (limited by T 2). These periods are shown as t g (time

during application of gradient fields) and t a,(acquisition time)

respectively, in Fig. 3. Finally, a 2D or 3D FFT must be performed

on the data to extract the image. As in HF MRI, the use of echo

pulses is also routinely employed to help remove the effects of field

inhomogeneity.

To begin the discussion, let’s revisit the various types of echoes:

(1) In high field MRI the spin echo is routinely used, with a 180°

spin-tip to produce re-focusing. This is shown schematically in

Fig. 5a. The spins are shown precessing in the x–y plane about a

magnetic field oriented into the page. The spins have de-phased

due to gradients. After the 180 degree reorientation (imagine flip-

ping the paper over) the faster spins are behind the slower, and

will catch up, producing the echo. This sort of echo is also referred

to as a ‘‘pancake’’ echo because it can also be visualized as flippingthe orientation of the spins in analogy to flipping a pancake. (2) The

gradient echo is also routinely used in HF MRI where we assume

we have a large homogeneous field and then a smaller gradient.

The reversal of the gradient can change the precession speed, thus

causing the echo, as shown in Fig. 5b. (3) The final echo is unique to

ULF MRI and is known as the ‘‘field echo’’. This echo is possible if

one can reverse the orientation of the measurement field as well.

In this case the precession direction is changed. An analogy is to

runners on a racetrack who suddenly reverse direction in the mid-

dle of the race (with faster ones now being behind and the echo

happening when they catch up). For this reason the field echo is

also referred to as the ‘‘racetrack’’ echo. This echo is shown in

Fig. 5c.

In HF MRI the field echo is not an option due to the inability tore-orient the main magnetic field. But a field echo can be quite

valuable. For example, a gradient echo alone cannot remove inho-

mogeneity associated with the main magnetic field, but a field

echo can.

In terms of encoding and acquisition, ULF MRI can use methods

very similar to traditional high field MRI, e.g. using both frequency

and phase of the voxel to encode an image, as shown in Fig. 3.

However, one could also use projection reconstruction encoding

by rotating the encoding gradients between excitations. Another

technique that is uniquely enabled by the low field strengths used

in ULF MRI is the possibility of rotating the main magnetic field (in-

stead of the sample or gradients). This could be useful if there were

multiple sensors, in a variety of orientations around the sample

and one were trying to optimize signal in all of them. We can, inprinciple, leverage any pulse sequence from HF MRI if we have




the hardware and signal time available. On the other hand – maybe

we can invent new approaches such as reorienting Bm.

We remind the reader of the lower gradients and reduced band-width we discussed earlier (i.e. Eqs. (2) and (3)). At ULF there is a

requirement that encoding and acquisition times be longer than in

traditional MRI. In addition to slowing us down, because relaxation

times may get shorter, and we are running out of signal and longer

acquisition times might not be practical. The Fourier imaging se-

quence shown in Fig.3, is simplebut inefficient. We arelosing signal

during a relatively long encoding time, and we only get one acquisi-

tion per polarization due to long acquisitions. The use of this se-

quence brings up another point, projection imaging requires Bm

and that all three gradients are on during readout and this intro-

duces noise. In contrast, in Fourier imaging only Bm and G x are on

and since they do not change in value they can be run on batteries

or very low-noise current supplies. These are the types of practical

challenges one must face.

4. Recent progress

4.1. Combined MEG and ULF MRI

Electroencephalography, EEG [38], and MEG [39] are at present

the only noninvasive imaging techniques that can passively and

non-invasively measure the consequences of neural activity on

the time scale at which neurons communicate (sub-millisecond

to tens of milliseconds). Unlike other techniques such as functional

MRI (fMRI) and positron emission tomography (PET), MEG/EEG are

‘‘direct’’ measurements of neural activity as the signals arise from

the electrical activity of the neurons themselves as opposed to

blood flow or increased metabolism indirectly associated with

neural activity. While the debate is still active, MEG is often re-

garded as having less ambiguous spatial localization [39,40] than

EEG, and is a powerful and well-regarded method for noninvasive

studies of neural activity in the human brain, and a powerful diag-

Fig. 5. Examples of various echo types. Precession is about the z -axis (into the page). (a) Spin-echo based on 180° RF pulse. The echo is produced when the faster spins ‘‘catch

up’’ to the slower. (b) The gradient echo is produced by reversing the direction of the gradients to exchange which spins are the faster and slower. (c) The field echo is only

possible in ULF MRI. In this case all the fields (measurement and gradient) are reversed, changing the direction of precession.




nostic tool for diseases such as epilepsy [41]. MEG localization of

small discrete cortical sources typically requires co-registration

with anatomical images acquired by MRI to localize function rela-

tive to anatomical features and landmarks. MEG instrumentation

remains almost exclusively based on the SQUID because of the re-

quired sensitivity to the minute magnetic fields produced by neu-

ral activity, nT to fT. There have been promising demonstrations of

MEG using atomic magnetometers [42], but at the time of thiswriting all clinical MEG systems remain based on the SQUID. Such

sensitive detectors are incompatible with conventional MRI which

is acquired at magnetic fields typically 1.5 T and higher.

Because the anatomical MRI and functional MEG data are ac-

quired in two separate systems, there is added cost as well as a

‘‘co-registration’’ process for the data sets which can introduce

source localization errors ranging from 2–10 mm [43,44], depend-

ing on approach. While this approach is routinely used, there is

some desire in the neuroscience community for improved ap-

proaches to co-register MEG/MRI data and enhance the potential

of MEG.

As soon as it appeared that SQUID-based ULF MRI was possible,

the ability to combine MEG and MRI in a single imaging session

was a logical next step. The first proof-of-principle results in

recording simultaneous MEG and NMR signals were achieved in

2004 [45,46]. In this work, the somatosensory evoked magnetic re-

sponse from the human brain was recorded truly simultaneously

with the free induction decay (FID) signal at 268 Hz. During such

experiments it became clear that simultaneous recording of such

signals is very difficult to perform because of the large low fre-

quency noise arising from the external magnetic field needed for

the NMR precession. This noise was caused by micro-vibrations

of the SQUID gradiometer in the NMR field which seriously dis-

torted the MEG signal. An example of these data is shown in

Fig. 6. In later experiments MEG and MRI signals were recorded

sequentially and the external field and gradients were zeroed dur-

ing the MEG recording.

The first ever ultra-low field (ULF) MRI of the human brain was

published in 2008 [6]. The image resolution was 3 Â 3 Â 6 mm3

and a single scan took $15 min. Six scans were averaged to im-

prove the signal-to-noise ratio (SNR) of the images up to about

30. The system had 7 channels. Auditory evoked magnetic field sig-

nals were recorded immediately after the ULF MRI session was fin-

ished. However the subject moved slightly for better coverage of

the MEG signals, which prevented co-registration of the recorded

MEG signals to the anatomical image. These results demonstrated

that a SQUID-based system could be used for both ULF MRI of the

human brain and MEG. The data are shown in Fig. 7.

Co-registration of auditory evoked magnetic field mapping and

ULF MRI was performed in 2010 [5] using the same 7-channel sys-

tem and an interleaved protocol. These data are shown in Fig. 1.

This time the MEG map was accurately superimposed with the

MR Images, with co-registration error of the different coordinate

systems within 1 mm accuracy. However, due to transients fromswitching the fields for the MRI protocol a wait period of several

seconds had to be introduced before each MEG measurement.

While the data thus far have indicated ‘‘proof-of-concept’’ for a

combined MEG and MRI device, a clinical MEG instrument must

have more than a few sensors. This is because the spatial sensitiv-

ity of the SQUIDs is used to help in MEG source localization: thus,

the more SQUIDs the better. Most clinical MEG systems have

SQUID arrays numbering from 200 to 306 sensors. It is critical that

the number of MEG channels needs to increase from what has been

demonstrated thus far in combined devices. Further, multiple

SQUID sensors can be used for image acceleration [47,48], which

is likely critical to improving the acquisition speed (by using the

spatial sensitivity of the array to reduce the required number of

imaging steps) and quality of the ULF MRI.

At the time of this writing we are aware of two groups actively

pursuing the concept of MEG and ULF MRI in a single device. These

include our group at Los Alamos National Laboratory (LANL) and

the European MEGMRI project. While both groups have ap-

proached the problem from an MEG-starting point (logical since

MEG is a much more mature technology), a combined MEG and

ULF MRI system is not just an upgraded conventional MEG ma-

chine. Adding ULF MRI capability implies the addition of coils for

generation of fields and gradients. However, in addition to the

MRI coils there are completely new requirements on the SQUID-

based sensors. One of the most difficult requirements is that the

SQUIDs should work immediately after being exposed to a pre-

polarizing field of up to 0.2 T. As mentioned above, when MEG is

involved a MSR is required. This adds the potential confound of

large transient magnetic fields from eddy currents even after thepolarization field is removed.

The LANL and MEGMRI systems are somewhat similar; both

leverage commercial MEG cryostats, MSRs, and basic measurement

and gradient coil hardware. Photographs of both systems are pre-

sented in Fig. 8.

At Los Alamos we have adopted the approach of separately opti-

mized magnetometers for imaging and MEG, and an external nitro-

gen B p coil. Our colleagues in Finland use a combination of

magnetometer and planar gradiometers in the array for both

MEG and MRI, and a self-shielded superconducting coil. A detailed

description of our preliminary system design is found in [20]. A

brief description of their system is found in [49] and a more com-

plete one expected in [7].

Although neither system is completed at the time of this writ-ing, preliminary data from the Finnish system is presented in Fig. 9.

Thus far the brain images presented are not ‘‘clinically relevant’’

in terms of ability to resolve key features of anatomy (e.g. cerebel-

lum) or acquisition time. However, it is likely that ULF MRI com-

bined with MEG will only get better as the technology

progresses. Further, one should not lose sight of the fact that the

objective of ULF MRI is not to compete with HF MRI in terms of im-

age quality (which it will not), but to provide a capability that HF

MRI cannot, such as combined MEG. One area of progress that is

essential to achieving better ULF MRI is pre-polarization. Higher

pre-polarization and lower SQUID noise are the only ways to

achieve better SNR, and spatial resolution. Getting the SQUIDs to

survive in the proximity of dynamic fields inside the MSR is the

key challenge to higher pre-polarization. Also, long imaging timesneed to be addressed. Dense array systems are likely the answer.

Fig. 6. Comparison of the somatosensory response for MEG only (blue) and MEG

and simultaneous NMR (red). High frequency oscillations in the MEG and NMR data

are from microphonics in the SQUID moving in the 268.5 Hz measurement field.The data are from [45,46].




This approach is synergistic with MEG, as large arrays are required

there as well. While the combination of anatomical and functional

imaging via ULF MRI and MEG could present a significant advance

in our understanding of the brain, there are other unique ap-

proaches that may be enabled by ULF, which we touch on briefly

here.

4.2. MEG and flow-based fMRI at ULF

Functional MRI relies on a connection between cerebral blood

flow, metabolism, and oxygenation with neuronal activity [50] to

provide$1 mm spatial resolution of function. Typically the tempo-

ral resolution is 3–4 s (1 s for event related tasks, although tempo-

ral resolution of as good as 150 ms has been achieved for some trial

types). The hemodynamic signal lags the neural activity, taking up

to 7 s to peak. There is evidence that fMRI reflects information

about neuronal firing rates or oscillatory power in the same region

under normal conditions [51]. However, the detailed relationship

between the fMRI signal and the neuronal response is not well

understood. Thus, while providing excellent localization, fMRI pro-

vides more modest insight into neural system dynamics. Applica-tions with clinical consequences such as diagnostics and surgical

planning need paradigms in which the neural correlates of the

fMRI signal have been validated using combined electrophysiolog-

ical and imaging approaches [52].

Thus far, information is primarily combined from separate

instruments, because of the instrumentation limitations discussed

above. But there needs to be the strongest correlation possible be-

tween the sources defined by the different methodologies. Manycognitive paradigms are most effective during the first presenta-

tion: learning, recognition, studies involving deception or confed-

erate studies, and many designs where judgments must be made.

Clinical symptoms are often not reproducible, for example epilep-

tic activity is seizure dependent and schizophrenic observation can

be hallucination-dependent.

The most commonly used form of fMRI is based on different

magnetic susceptibilities of oxy- and de-oxy-hemoglobin. These

levels change as a function of brain activity giving rise to Blood

Oxygen Level Dependent (BOLD) fMRI. BOLD fMRI is widely em-

ployed because of its high sensitivity and easy implementation.

However the BOLD effect increases with magnetic field, typically

requiring large magnetic fields (>1 T). Thus, the BOLD approach

to fMRI will probably not work at ULF. However, fMRI is also rou-

tinely performed at high fields using arterial spin labeling (ASL)

Fig. 7. The first ULF MRI of the human brain at 46 lT compared to HF, 1.5 T. Data are from [6].




techniques such as flow-sensitive alternating inversion recovery

(FAIR) [53–55]. ASL methods differentiate the net magnetization

of endogenous arterial water flowing proximally to the region of

interest from the net magnetization of tissue. fMRI based on ASL

is possible at ULF, as the effect is not field dependent. We have re-

cently demonstrated flow-based imaging in a water phantom at

ULF using FAIR [56]. Because the electro-physiological brain re-

sponse is faster than the hemodynamic, it may be possible to inter-

leave the acquisition such that the MEG, and fMRI are acquired for

the brain response to the same event. Interleaved data will provide

for direct comparison of electrophysiological and hemodynamic

brain activity obtained during a single collection. Single collection(EEG)/fMRI at high fields is being widely adopted and may address

many of the scientific questions. Combined MEG/fMRI, however, is

only possible at ULF and the addition of MEG information may pro-

vide further insight.

4.3. Direct neural current imaging at ULF

The search for a modality capable of direct measurement and

imaging of neural activity in the human brain embarked on an

exciting new path at the close of the 20th century when research-

ers proposed that magnetic fields resulting from neuroelectrical

activity could interact with a spin population, through phase shifts

and dephasing [57,58]. Shortly thereafter, we presented the possi-

bility that resonant interaction between the neural magnetic fieldsand the spin population was possible in the ULF regime [14]. To-

gether, the prospect of acquiring an image of neural activity based

on the interaction between the neural magnetic fields and the spin

population has been called ‘‘direct neural current imaging,’’ or DNI.

DNI promises the tomographic certainty of volumetric fMRI, with

temporal resolution in direct relation to neural activation. One re-

ported experimental measurement of DNI at high field based on

the enhanced dephasing resulting from neural currents [59] has

been vigorously debated in the neuroimaging community [60]. It

is widely held that the authors did not convincingly differentiate

between a neural effect and the orders of magnitude larger ‘‘sus-

ceptibility artifact’’ caused by the BOLD signal. A key strength of

DNI at ULF is that the confounding susceptibility artifact is virtu-ally nonexistent. Moreover, the overlap between proton Larmor

frequency at ULF and the frequency band of cortical neural activity

permits resonant absorption by the population of proton spins of

magnetic energy supplied by the cortical activity. We reported

the observation of this effect in phantoms, in which we measured

the interaction of a weak current ($ 10 lA) with the ULF NMR sig-

nal [14]. The frequency spectrum of neural activity spans the range

from a few Hz to a few kHz, at most, and the Larmor frequency for

proton spins overlaps with this neural frequency spectrum only in

the ULF regime (recall that xL = 4 kHz, the high-end of the neural

frequency spectrum, for B 100 lT). Consequently, ‘‘resonant

absorption’’ can only occur at fields below 100lT, and may enable

measurement of the DNI effect, and could provide a tool to directly

measure neuronal population oscillations and other frequency-dependent neural population phenomena. In addition to DNI, re-

Fig. 8. Photographs (left) and schematics (right) of the combined MEG and ULF MRI systems. Upper: MEGMRI consortium hybrid system from [49]. Lower: LANL system [20].




cent work indicates that changes in electrical impedance at fre-

quencies below 100 Hz may provide another approach for imagingfunction [61,62]. These approaches may also lend themselves to

observation by ULF MRI where distributions of applied currents

can be imaged (magnetic resonance electrical impedance tomogra-

phy or MREIT) at ULF and resonant interaction between the applied

current and the spins may be employed to selectively measure the

change in the applied current while drastically reducing noise.

Although speculative, the impact that successful DNI or electrical

impedance tomography-based demonstration of fast neural imag-

ing would have on brain research cannot be overstated.

4.4. Contrast

Contrast in anatomical MRI can be most simply defined as thedifference between two tissue types in some measurable parame-

ter space, such as T 1 or T 2. The variation between parameters such

as T 1 and T 2 in different materials (i.e. tissues) provides the power-

ful and unique information that forms the foundation of MRI

images. For example, T 1 contrast is a basic tool in medical MRI. Dif-

ferences in T 1 between tissues (studied at a fixed magnetic field)

are routinely used for applications such as detection and character-

ization of brain lesions, studies of the gray/white matter junction

in cortex, and detection of cancer (to name just a few).

T 1 varies with field (frequency) and typically these variations

are more pronounced in the ULF regime, opening the prospect

for enhanced contrast at low fields. For example, T 1 for gray matter

changes from $100 ms at 46 lT to $1 s at 1.5 T [9]. Consequently,

it may be an advantage (if one is able to) to change the imagingfield to optimize the contrast.

The underlying reason why T 1 changes with magnetic field is

that the number of resonant protons available to transfer energyto the lattice changes with magnetic field. Specifically, only mag-

netic field fluctuations at the Larmor frequency can cause T 1 relax-

ation, and the frequency of these field fluctuations depends

strongly on the molecular dynamics. This is illustrated in Fig. 10,

which presents the time scales of the NMR fluctuations from vari-

ous molecular processes and compares them to proton Larmor fre-

quency. As one can see, HF MRI (in the 60–120 MHz range) largely

derives any contrast from intramolecular motion only whereas at

ULF, relaxation will be a function of (and thus able to probe)

numerous other processes.

It has been proposed that these contrast differences, or ULF’s

ability to vary contrast because fields can vary so easily, may ren-

der the method superior to HF MRI for some applications even if

the SNR is reduced, because the contrast-to-noise, CNR is better[8].

We will briefly discuss here two recent examples put forth by

John Clarke’s team. In the first, using an agarose gel phantom de-

signed to mimic tissue properties, it was shown that T 1 contrast be-

tween two phantom tissue types was flat from 100 Hz to 10 kHz

and then had a dramatic T 1 dispersion until there appears essen-

tially no contrast past 10 MHz [8]. These results are summarized

in Fig. 11 from that work. In this example, it would appear that

ULF is essential to obtain any image contrast at all.

In more recent work [63], Clarke’s group showed that for ex-

cised prostate tissue samples, there was a significant intrinsic T 1contrast between healthy at cancerous tissue at 132lT. Their re-

sults indicate that T 1 progressively decreases as the percentage of

cancer increases, and that ULF MRI may be a viable non-invasivemethod for detection of cancer even with reduced SNR compared

Fig. 9. Left: Coronal slices of the human brain acquired with the hybrid MEG–MRI device. B p and Bm were 22 mT and 50 lT, respectively. Total imaging time was 92 min, with

8 averages. Right: T 2-weighted high-field-MR image of the same subject obtained at 3 T. The resolution of the high-field-MR image was reduced to match that of the ULF–MR

image. For more details, see [49].




to HF MRI. These encouraging results will need to be validated with

in vivo studies, but provide compelling argument for continued ef-

forts in ULF MRI.

4.5. Dispersion contrast: measuring T 1 at arbitrary field

In conventional MRI the change in T 1 as a function of magnetic

field cannot be easily exploited, because the main field of the mag-

net is fixed. However, there is no ideal field at which T 1 contrast is

maximized for all tissue types (see for example [64,23]). This var-

iability argues for another possible parameter for imaging contrast:

‘‘Dispersion contrast’’ in which the image is constructed on the ba-

sis of how contrast changes as a function of field, not simply a sin-

gle contrast value at a fixed field.

For example, T 1 changes dramatically as a function of magnetic

field for tissues with high protein concentrations, due to couplings

between the protons and quadrupolar 14N in protein molecule

backbones. Ungersma et al. [13] used this approach in Field Cycling

MRI to produce ‘‘protein weighted’’ images of the human wrist by

producing an image where T 1 in muscle was quite short due to thecross-coupling (at the minimum of a nitrogen dip) and another im-

age far from the dip. Since tissues besides muscle (e.g. fat) do not

show such dispersion. By subtraction of these two images an image

weighted to the presence of tissue with high protein content is pro-

duced. In principle this approach could be used for any tissues with

different T 1 values as a function of field.

Dispersion contrast (e.g. producing images based on howT 1 var-

ies at differing field strengths) is not possible in conventional high

field MRI, where magnetic field strength is fixed. Using methods

commonly employed in ULF MRI (pulsed fields, and variable

strength electromagnets) T 1 can be sampled over a wide range of

frequencies and this T 1 contrast dispersion can be used as an imag-

ing parameter. Unlike field cycling MRI, which uses at somewhat

higher fields, the ULF approach should be able to reach a widerrange of frequencies including very low frequencies below

<1 kHz. When the previously well-studied (at higher frequencies)

T 1 dispersion behavior of water was revisited using SQUID-based

ULF MRI [10] capable of attaining Hz level Larmor frequencies, a

previously unreported and dramatic dispersion due to a very slow

exchange process at $100 Hz was discovered. It is likely that other

effects remain to be discovered at ULF as well, and that these ef-

fects could be exploited for new MRI contrast.

The pre-polarization approach of ULF MRI also enables mea-

surement of T 1 at the pre-polarization field, and readout at essen-tially any field between B p and Bm. In one implementation, used to

inspect liquids by relaxometry [65,66], we showed that by measur-

ing T 1 in the pre-polarization field ($2 MHz) using different polar-

ization times, while measuring T 2 at the much lower measurement

field ($2 kHz) we are able to discriminate between many different

liquid types. For the material identification studies our team has

pursued, the relaxation dispersion provides unique information

that can be used to tell liquids apart.

4.6. Contrast-to-noise – a cautionary tale

Although we have discussed the fact that the ULF regime may

offer enhanced or even unique contrast as compared to HF MRI,

we cannot leave the reader with the impression that greater T 1contrast always leads to a more informative image. Ultimately it

is the contrast-to-noise ratio (CNR) that matters.

An accurate comparison of both SNR and CNR between ULF and

HF instruments would require a detailed analysis of the acquisition

system and details of the protocol being used. However, for the

sake of illustration, let us consider the case where all other factors

equivalent between the systems except T 1 and SNR. We will then

use CNR as the figure of merit for our comparison.

For two tissues A and B,

CNR /S

r½eÀR At À eÀRBt ð12Þ

where the signal is S A = S Áexp(ÀR At ), and R A 1/T 1 A, and r is the

noise (which we assume to be the same for either tissue). For anytwo such tissues there will be some optimal evolution time when

the difference in signal, S A À S B, between them is maximized.

Let’s consider an example, using gray matter and white matter

in the human brain. From the gray/white matter data found in [23]

(see Fig. 13), the contrast difference appears to peak around

10 MHz ($0.23 T). One might infer from this result that it would

be better (from the point of view of maximizing contrast between

these two tissues) to produce an image at 0.23 T rather than 2.3 T

(100 MHz), but even in this very simple example things are not so

simple.

At 10 MHz R(gray)ffi 1.5 and R(white)ffi 3. At 100 MHz

R(gray)ffi 0.9 and R(white)ffi 1.4. Using Eq. (12), assuming no dif-

ferences in proton density, and the same initial SNR, 10 MHz is bet-

ter than 100 MHz in terms of CNR. But the SNR won’t be the samebetween ULF ($0.23 T) and $2.3 T of our example. Even if the dif-

Fig. 10. Time scales of the NMR fluctuations vs. proton Larmor frequency.

Fig. 11. T 1 dispersion in Agarose gel. Data are from [8].




ference in SNR for ULF were (optimistically) only half, CNR is better

at 100 MHz. Simply looking at T 1 values alone might be quite

misleading.

However, in the T 1 dispersion data presented in Fig. 11, at ULF

(1000 Hz) R A = 2.5 and RB = 5 (for 0.25% and 5% agarose gels respec-

tively), whereas by$12 MHz the differences between R A and RB are

non-existent. In this case the CNR is always better at ULF as long as

the SNR is high enough to produce a useful image.

4.7. The potential for portable systems

The low magnetic fields, relatively simple imaging field genera-

tion, and smaller requirement for cryogens open the possibility for

alternative MRI systems that might be smaller, cheaper, and/or

better suited for operation in unconventional locations (e.g. ships,field hospitals). For example, the system developed for liquid

explosive detection (Fig. 12) operated in an airport and has a foot-

print suggestive of portable MRI, Fig. 14.

To achieve these goals requires advances in several critical areas

including: (1) improved image quality; (2) developing a small-

footprint (size and power consumption) pre-polarization coil; (3)

removal of the requirement for a large magnetically shielded

room; (4) cryogenic cooling to remove the frequent refill times tra-

ditionally associated with SQUID cryostats. It is likely that (1) and

(2) will prove the most interesting areas for development in ULF

MRI. Although there is room for advancement, ULF MRI has been

demonstrated outside a heavy MSR [4] and is likely quite achiev-

able, provided no very low frequencies are required. Similarly,

cryogenic cooling has been used with SQUID-based ULF MRI sys-tems [17] and even for MEG [67].

5. Discussion of the future

While the trends in MRI will likely continue toward higher

fields, there are some reasons to be interested in MRI at the oppo-

site end of the spectrum. The advances over the next few years will

likely be critical for SQUID-based ULF MRI if it is to move past the

work of a handful of groups. For example if the approach can be

shown to add substantive value to MEG in terms of cost and/or

accuracy, this may prove incentive to advance the field. However,

even if very successful in this area, challenges exist. MEG instru-

ments are somewhat rare compared to conventional MRI (or evenfMRI). Thus the application space will not initially be large.

Fig. 12. Data and relaxation weighted images from a ULF MRI system designed to detect threat liquids indicated by circles. Top Row: photographs of items. Center Row: 2-D

images with threat detection. Bottom Row: 3-D slices through the items shown in row (a), far right. Data are from [66].

Fig. 13. Relaxation times of gray and white matter in the human brain as a function

of Larmor frequency. Data are from [23].




With regard to portable or low-cost MRI, there are already

many low-field MRI ($0.1 T) systems (e.g. PoleStar™) that fill

many potential applications such as imaging in the operating

room, albeit with a fixed field strength and small field-of-view.However, there may be some areas where the ability to re-orient,

remove, or change the strength of the magnetic fields is crucial.

The potential for unique contrast at ULF is one of the most interest-

ing and exciting areas for further study. Additionally the potential

for the resonant mechanisms (overlapping the Larmor frequency

with processes on a similar timescale) is quite unique and may en-

able totally new insights. We end by reminding the reader that

many aspects of imaging performance can be predicted, and ULF

MRI only makes sense for applications where the benefits out-

weigh the challenges.

Acknowledgement

This work was supported in part by the Los Alamos NationalLaboratory LDRD #20100097DR. The authors wish to thank their

colleague Dr. Jaakko Nieminen for providing us images of the MEG-

MRI system.

References

[1] W. Myers, D. Slichter, M. Hatridge, S. Busch, M. Mößle, R. McDermott, A.Trabesinger, J. Clarke, Calculated signal-to-noise ratio of MRI detected withSQUIDs and Faraday detectors in fields from 10 lT to 1.5 T, J. Magn. Reson. 186(2006) 182–192.

[2] A. Macovski, S. Conolly, Novel approaches to low cost MRI, Magn. Reson. Med.30 (1993) 221–230.

[3] H.C. Seton, J.M.S. Hutchinson, D.M. Bussel, A 4.2 K receiver coil and SQUIDamplifier used to improve the SNR of low-field magnetic resonance images of the human arm, Meas. Sci. Technol. 8 (1997) 198–207.

[4] J. Clarke, M. Hatridge, M. Mößle, SQUID-detected magnetic resonance imagingin microtesla fields, Annu. Rev. Biomed. Eng. 9 (2007) 389–413.

[5] P.E. Magnelind, J.J. Gomez, A.N. Matlashov, T. Owens, J.H. Sandin, P.L. Volegov,M.A. Espy, Co-Registration of Interleaved MEG and ULF MRI Using a 7 ChannelLow-Tc SQUID System, Trans. Appl. Supercond. 21 (3, pt.1) (2011) 456–460.

[6] V.S. Zotev, A.N. Matlashov, P.L. Volegov, I.M. Savukov, M.A. Espy, J.C. Mosher, J.J.

Gomez, R.H. Kraus, Microtesla MRI of the human brain combined with MEG, J.Mag. Reson. 194 (2008) 115–120.

[7] P.T. Vesanen, J.O. Nieminen, K.C.J. Zevenhoven, J. Dabek, L.T. Parkkonen, A.V.Zhdanov, J. Luomahaara, J. Hassel, J. Penttila, J. Simola, A.I. Ahonen, J.P. Makela,R.J. Ilmoniemi. Hybrid ultra-low-field MRI and magnetoencephalographysystem based on a commercial wholehead neuromagnetometer. to appear Magn. Reson. Med. <http://onlinelibrary.wiley.com/doi/10.1002/mrm.24413/pdf >.

[8] S.-K. Lee, M. Mossle, W. Myers, N. Kelso, A.H. Trabesinger, A. Pines, J. Clarke,SQUID-Detected MRI at 132 lT with T1- Weighted Contrast Established at10 lT-300 mT, Magn. Reson. Med. 53 (2005) 9–14.

[9] V.S. Zotev, A.N. Matlashov, I.M. Savukov, T. Owens, P.L. Volegov, J.J. Gomez,M.A. Espy, SQUID-Based Microtesla MRI for In Vivo Relaxometry of the HumanBrain, IEEE Trans. Appl. Supercond. 19 (3; pt.1) (2009) 823–826.

[10] S. Hartwig, J. Voigt, H-J. Scheer, H-H. Albrecht, M. Burghoff, L. Trahms, Nuclearmagnetic relaxation in water revisited, J. Chem. Phys. 135 (2011) 054201.

[11] J.K. Alford, B.K. Rutt, T.J. Scholl, W.B. Handler, B.A. Chronik, Delta relaxationenhanced mr: improving activation- specificity of molecular probes through

R1 dispersion imaging, Magn. Reson. Med. 61 (2009) 796–802.[12] P.E. Magnelind, I.M. Savukov, Y.T. Araya, M.A. Espy, A.N. Matlashov, P. Nath,P.L. Volegov, V.S. Zotev, Superparamagnetic nanoparticle contrast agents inultra-low field MRI, presented at the 50th ENC, March 29- April 3, PacificGrove, California, 2009.

[13] S. Ungersma, N. Matter, J. Hardy, R. Venook, A. Macovski, S. Conolly, G. Scott,Magnetic resonance imaging with T1 dispersion contrast, Mag. Res. Med. 55(2006) 1362–1371.

[14] R. Kraus, P. Volegov, A. Matlachov, M. Espy, Toward direct neural currentimaging by resonant mechanisms at ultra-low field, Neuroimage 39 (2008)310–317.

[15] M. Mößle, S-I. Han, W.R. Myers, S-K. Lee, N. Kelso, M. Hatridge, A. Pines, J.Clarke, SQUID-Detected Microtesla MRI in the Presence of Metal, J. Magn.Reson. 179 (2006) 146–151.

[16] A.N. Matlachov, P.L. Volegov, M.A. Espy, J.S. George, R.H. Kraus Jr., SQUIDdetected NMR in microtesla magnetic fields, J. Magn. Reson. 170 (2004) 1–7.

[17] B. Eom, K. Penanen, P.K. Day, I. Hahn, M.S. Cohen, Development of Cryogen-Free Ultra-Low Field MRI Instrument, United States Proc. Intl. Soc. Mag. Reson.Med. 17 (2009).

[18] P.L. Volegov, J.C. Mosher, M.A. Espy, R.H. Kraus Jr., On concomitant gradients inultra-low field MRI, J Magn. Reson. 175 (2005) 103–113.

Fig. 14. (Clockwise from upper left): Bottle liquid scanner (MagViz) for detection of threat liquids inside mini-MSR at the Albuquerque International airport; the MagViz

insert; d images of the human knee taken with the system; human subject. Data are from [66].


http://onlinelibrary.wiley.com/doi/10.1002/mrm.24413/pdf






[19] J.O. Nieminen, R.J. Ilmoniemi, Solving the problem of concomitant gradients inultra-low-field MRI J, Magn. Reson. 207 (2010) 213–219.

[20] A.N. Matlashov, E. Burmistrov, P.E. Magnelind, L. Schultz, A.V. Urbaitis, P.L.Volegov, J. Yoder, M.A. Espy, SQUID-based Systems for Co-registration of Ultra-Low Field Nuclear Magnetic Resonance Images and Magnetoencephalography,Physica C: Supercond. 482 (2012) 19–26.

[21] W.R. Myers, Potential applications of microtesla magnetic resonance imagingdetected using a superconducting quantum interference device, Ph.D. thesis(2006) http://escholarship.org/uc/item/19x7q88x#page-9.

[22] D.L. Lurie, S. Aime, S. Baroni, N.A. Booth, L.M. Broche, C.-H. Choi, G.R. Davies, S.

Ismail, D. O’hogain, K.J. Pine, Fast field cycling magnetic resonance imaging,Comptes Rendus Phys. 11 (2010) 136–148.

[23] H.W. Fischer, P.A. Rinck, Y. Van Haverbeke, R.N. Muller, Nuclear relaxation of human brain gray and white matter: analysis of field dependence andimplications for MRI, Magn. Reson. Med. 16 (1990) 317–334.

[24] R. McDermott, A.H. Trabesinger, M. Mück, E.L. Hahn, A. Pines, J. Clarke, Liquid-state nmr and scalar couplings in microtesla magnetic fields, Science 22 (295)(2002) 2247.

[25] M. Burghoff, S. Hartwig, L. Trahms, Nuclear magnetic resonance in thenanotesla range, Appl. Phys. Lett. 87 (2005) 054103.

[26] J.R. Sims, J.B. Schillig, C.A. Swenson, D.L. Gardner, A.N. Matlashov, C.N.Ammerman, Low-noise pulsed pre-polarization magnet systems for ultra-low field NMR, IEEE Trans. Appl. Supercond. 20 (3) (2010) 752–755.

[27] J.O. Nieminen, P.T. Vesanen, K.C.J. Zevenhoven, J. Dabek, J. Hassel, J.Luomahaara, J.S. Penttila, R.J. Ilmoniemi, Avoiding eddy-current problems inultra-low-field MRI with self-shielded polarizing coils, J. Magn. Reson. 212(2011) 154–160.

[28] P. Vesanen, J. Nieminen, C. Zevenhoven, J. Dabek, J. Simola, J. Sarvas, R.Ilmoniemi, The spatial and temporal distortion of magnetic fields appliedinside a magnetically shielded room, IEEE Trans. Magn. 48 (1) (2012) 53–61.

[29] J.P. Reilly, Peripheral nerve stimulation by induced electric currents: exposureto time-varying magnetic fields, Med. Biol. Eng. Comput. 27 (1989) 101–110.

[30] M.S. Cohen, M.R. Weisskopf, R.R. Rzedian, H.L. Kantor, Sensory stimulation bytime-varying magnetic fields, Magn. Reson. Med. 14 (2) (1990) 409–414.

[31] T.F. Budinger, H. Fischer, D. Hentschel, H.E. Reinfelder, F. Schmitt, Physiologicaleffects of fast oscillating magnetic field gradients, J. Comput. Assist. Tomogr.15 (6) (1991) 909–914.

[32] Guidance for Industry: Guidance for the Submission Of PremarketNotifications for Magnetic Resonance Diagnostic Devices, Document issuedon: November 14, 1998, <http://www.fda.gov/downloads/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/ucm073818.pdf >.

[33] Criteria for Significant Risk Investigations of Magnetic Resonance DiagnosticDevices July 14, 2003 <http://www.fda.gov/downloads/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/ucm072688.pdf >.

[34] Supracon A G <http://www.supracon.com>.[35] M. Espy, S. Baguisa, D. Dunkerley, P. Magnelind, A. Matlashov, T. Owens, H.

Sandin, I. Savukov, L. Schultz, A. Urbaitis, P. Volegov, Progress on detection of

liquid explosives using ultra-low field MRI, IEEE Trans. Appl. Supercond. 21 (3pt. 1) (2011) 530–533.

[36] J. Luomahaara, P.T. Vesanen, J. Penttila, J.O. Nieminen, J. Dabek, J. Simola, M.Kiviranta, L. Gronberg, C.J. Zevenhoven, R.J. Ilmoniemi, J. Hassel, All-planarSQUIDs and pickup coils for combined MEG and MRI, Supercond. Sci. Technol.24 (2011) 075020.

[37] J.O. Nieminen, M. Burghoff, L. Trahms, R.J. Ilmoniemi Polarization encoding as anovel approach to MRI, J. Magn. Reson., 202 (2) (2010) 211–216.

[38] D. Regan, Human Brain Electrophysiology: Evoked Potentials And EvokedMagnetic Fields in Science and Medicine, Elsevier Science Publishing, NewYork, 1989.

[39] M. Hamalainen, R. Hari, R.J. Ilmoniemi, J. Knuutila, O.V. Lounasmaa, et al.,Magnetoencephalography – theory, instrumentation, and applications tononinvasive studies of the working human brain, Rev. Mod. Phys. 65 (2)(1993) 413–497.

[40] F.H. Lopes da Silva, H.J. Wieringa, M.J. Peters, Source localization of EEG versusMEG: Empirical comparison using visually evoked responses and theoreticalconsiderations, Brain Topograp. 4 (2) (1991) 133–142.

[41] M. Funke, T. Constantino, C. Van Orman, E. Rodin, Magnetoencephalography

and magnetic source imaging in epilepsy, Clin EEG Neurosci. 40 (4) (2009)271–280.

[42 ] H . X ia, A. Ben-Amar Baranga, D. Hoff man, M.V. Romalis,Magneoencephalography with an atomic magnetometer, Appl. Phys. Lett. 89(2006) 211104.

[43] P. Adjamian, G.R. Barnes, A. Hillebrand, I.E. Holliday, K.D. Singh, P.L. Furlong, E.Harrington, C.W. Barclay, P.J.G. Route, Co-registration of MEG with MRI usingbite-bar fiducials and surfacematching, Clin. Neurophys. 115 (2004) 691.

[44] V. Poghosyan, A.A. Ionnides, Precise mapping of early visual responses in spaceand time, Neuroimage 35 (2) (2007) 759–770.

[45] A.N. Matlachov, P.L. Volegov, M. Espy, R. Stolz, L. Fritzsch, V. Zakosarenko, H.-G.Meyer, R.H. Kraus Jr., Instrumentation for simultaneous detection of low fieldNMR and biomagnetic signals, IEEE Trans. Appl. Supercond. 15 (2) (2005) 676–679.

[46] P. Volegov, A.N. Matlachov, M.A. Espy, J.S. George, R.H. Kraus Jr., Simultaneousmagnetoencephalography and SQUID detected nuclear MR in microteslamagnetic fields, Magn. Reson. Med. 52 (3) (2004) 467–470.

[47] K.P. Pruessmann, M. Weiger, M.B. Scheidegger, P. Boesiger, SENSE: sensitivity

encoding for fast MRI, Magn. Reson. Med. 42 (5) (1999) 952–962.[48] V.S. Zotev, P.L. Volegov, A.N. Matlashov, M.A. Espy, J.C. Mosher, R.H. Kraus Jr.,

Parallel MRI at microtesla fields, J. Magn. Reson. 192 (2008) 197–208.[49] J. Nieminen, Ultra-low-field MRI: techniques and instrumentation for hybrid

MEG–MRI, Aalto University publication series doctoral dissertations (2012)<lib.tkk.fi/Diss/2012/isbn9789526046457/isbn9789526046457.pdf >.

[50] S-G. Kim and P. Bandettini, BOLD fmRI: A Guide to Functional Imaging forNeuroscientists, in: S. H. Faro, F. B. Mohamed, (Eds.), Principles of FunctionalMRI, Springer, <http://dx.doi.org/10.1007/978-1-4419-1329-6>, Library of Congress Control Number: 2010929273, (Chapter 1).

[51] M.L. Scholvinck, A. Maier, F.Q. Ye, J.H. Duyn, D.A. Leopold, Neural basis of global resting-state fMRI activity, PNAS 107 (22) (2010) 10238.

[52] C. Kayser and N. K. Logothetis, The Electrophysiological background of thefMRI Signal, fMRI: basics and clinical applications, in: S. Ulmer, O. Jansen,(Eds.), Springer, http://dx.doi.org/10.1007/978-3-540-68132-8, Library of Congress Control Number: 2009931051, (Chapter 4).

[53] S.-G. Kim, Quantification of relative cerebral blood flow change by flow-sensitive alternating inversion recovery (FAIR) technique: application tofunctional mapping, Magn. Reson. Med. 34 (1995) 293–301.

[54] K.K. Kwong, D.A. Chesler, R.M. Weisskoff, K.M. Donahue, T.L. Davis, L.Ostergaard, T.A. Campbell, B.R. Rosen, MR perfusion studies with T1-weighted echo planar imaging, Magn. Reson. Med. 34 (1995) 878–887.

[55] C. Schwarzbauer, S. Morrissey, A. Haase, Quantitative magnetic resonanceimaging of perfusion using magnetic labeling of water proton spins within thedetection slice, Magn. Reson. Med. 35 (1996) 540–546.

[56] P. Magnelind, J. George, J. Gomez, A. Matlashov, A. Urbaitis, P. Volegov, M. Espy,Towards functional magnetic resonance imaging at ultra-low fields, presentedat the Superconductivity Centennial Conference in The Hague, TheNetherlands (2011).

[57] J. Bodurka, P.A. Bandettini, Toward direct mapping of neuronal activity: MRIdetection of ultraweak transient magnetic fields changes, Magn. Reson. Med.47 (2002) 1052–1058.

[58] J. Bodurka, A. Jesmanowicz, J.S. Hyde, H. Xu, L. Estkowski, S.J. Li, Current-induced magnetic resonance imaging, J Magn. Reson. 137 (1999) 265–271.

[59] J. Xiong, P.T. Fox, J.H. Gao, Directly mapping magnetic field effects of neuronalactivity by magnetic resonance imaging, Hum Brain Mapp 20 (2003) 41–49.

[60] M. Parkes, F.P. de Lange, P. Fries, I. Toni, D.G. Norris, Inability to directly detect

magnetic field changes associated with neuronal activity, Magn. Reson. Med.57 (2007) 411–416.

[61] O. Gilad, A. Ghosh, D. Oh, D.S. Holder, A method for recording resistancechanges non-invasively during neuronal depolarization with a view to imagingbrain activity with electrical impedance tomography, J. Neurosci. Methods 180(2009) 87–96.

[62] O. Gilad, D.S. Holder, Impedance changes recorded with scalp electrodesduring visual evoked responses: implications for Electrical ImpedanceTomography of fast neural activity, NeuroImage 47 (2009) 514–522.

[63] S. Busch, M. Hatridge, M. Mößle, W. Myers, T. Wong, M. Mück, K. Chew, K.Kuchinsky, J. Simko, J. Clarke, Measurements of T1-relaxation in ex vivoprostate tissue at 132 lt, Magn. Reson. Med. 67 (2012) 1138–1145.

[64] S.H. Koenig, R.D. Brown, Determinants of proton relaxation rates in tissue,Magn. Reson. Med. 1 (1984) 437–447.

[65] M. Espy, M. Flynn, J. Gomez, C. Hanson, R. Kraus, P. Magnelind, K. Maskaly, A.Matlashov, S. Newman, M. Peters, H. Sandin, I. Savukov, L. Schultz, A. Urbaitis,P. Volegov, V. Zotev, Applications of Ultra-Low Field Magnetic Resonance forImaging and Materials Studies, IEEE Trans. Appl. Supercond. 19 (3; pt.1) (2009)835–838.

[66] M. Espy, M. Flynn, J. Gomez, C. Hanson, R. Kraus, P. Magnelind, K. Maskaly, A.Matlashov, S. Newman, T. Owens, M. Peters, H. Sandin, I. Savukov, L. Schultz, A.Urbaitis, P. Volegov, V. Zotev, Ultra-low field MRI for the detection of liquidexplosives, Supercond. Sci. Technol. 23 (2010) 034023.

[67 ] T. Takeda, M. Okamoto, T. Miyazaki, N. Mor ita, K. Katagiri ,Magnetoencephalography, ISBN: 978-953-307-255-5 Helium CirculationSystem (HCS) for the MEG, <http://www.intechopen.com/books/magnetoencephalography/helium-circulation-system-hcs-for-the-meg >.


http://escholarship.org/uc/item/19x7q88x#page-9

http://www.fda.gov/downloads/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/ucm073818.pdf




http://www.supracon.com/

http://lib.tkk.fi/Diss/2012/isbn9789526046457/isbn9789526046457.pdf

http://dx.doi.org/10.1007/978-1-4419-1329-6

http://dx.doi.org/10.1007/978-3-540-68132-8

http://www.intechopen.com/books/magnetoencephalography/helium-circulation-system-hcs-for-the-meg




http://dx.doi.org/10.1007/978-3-540-68132-8

http://dx.doi.org/10.1007/978-1-4419-1329-6

http://lib.tkk.fi/Diss/2012/isbn9789526046457/isbn9789526046457.pdf

http://www.supracon.com/





http://escholarship.org/uc/item/19x7q88x#page-9

Squid Magnet

Documents

Transcript of Squid Magnet