Axion Dark Matter Search around 4.55 µeV with Dine-Fischler-Srednicki-Zhitnitskii Sensitivity.

We report an axion dark matter search at Dine-Fischler-Srednicki-Zhitnitskii sensitivity with the CAPP-12TB haloscope, assuming axions contribute 100% of the local dark matter density. The search excluded the axion-photon coupling g_{aγγ} down to about 6.2×10^{-16} GeV^{-1} over the axion mass range between 4.51 and 4.59 µeV at a 90% confidence level. The achieved experimental sensitivity can also exclude Kim-Shifman-Vainshtein-Zakharov axion dark matter that makes up just 13% of the local dark matter density. The CAPP-12TB haloscope will continue the search over a wide range of axion masses.

Near-Quantum-Noise Axion Dark Matter Search at CAPP around 9.5 µeV.

We report the results of an axion dark matter search over an axion mass range of 9.39-9.51 µeV. A flux-driven Josephson parametric amplifier (JPA) was added to the cryogenic receiver chain. A system noise temperature of as low as 200 mK was achieved, which is the lowest recorded noise among published axion cavity experiments with phase-insensitive JPA operation. In addition, we developed a two-stage scanning method which boosted the scan speed by 26%. As a result, a range of two-photon coupling in a plausible model for the QCD axion was excluded with an order of magnitude higher in sensitivity than existing limits.

First Results from an Axion Haloscope at CAPP around 10.7 µeV.

The Center for Axion and Precision Physics Research at the Institute for Basic Science is searching for axion dark matter using ultralow temperature microwave resonators. We report the exclusion of the axion mass range 10.7126-10.7186 µeV with near Kim-Shifman-Vainshtein-Zakharov (KSVZ) coupling sensitivity and the range 10.16-11.37 µeV with about 9 times larger coupling at 90% confidence level. This is the first axion search result in these ranges. It is also the first with a resonator physical temperature of less than 40 mK.

Development of advanced signal processing and source imaging methods for superparamagnetic relaxometry.

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.

Magnetic relaxometry as applied to sensitive cancer detection and localization.

BACKGROUND: Here we describe superparamagnetic relaxometry (SPMR), a technology that utilizes highly sensitive magnetic sensors and superparamagnetic nanoparticles for cancer detection. Using SPMR, we sensitively and specifically detect nanoparticles conjugated to biomarkers for various types of cancer. SPMR offers high contrast in vivo, as there is no superparamagnetic background, and bones and tissue are transparent to the magnetic fields. METHODS: In SPMR measurements, a brief magnetizing pulse is used to align superparamagnetic nanoparticles of a discrete size. Following the pulse, an array of superconducting quantum interference detectors (SQUID) sensors detect the decaying magnetization field. NP size is chosen so that, when bound, the induced field decays in seconds. They are functionalized with specific biomarkers and incubated with cancer cells in vitro to determine specificity and cell binding. For in vivo experiments, functionalized NPs are injected into mice with xenograft tumors, and field maps are generated to localize tumor sites. RESULTS: Superparamagnetic NPs developed here have small size dispersion. Cell incubation studies measure specificity for different cell lines and antibodies with very high contrast. In vivo animal measurements verify SPMR localization of tumors. Our results indicate that SPMR possesses sensitivity more than 2 orders of magnitude better than previously reported.

SQUID-detected ultra-low field MRI.

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 µT) 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 magnetization (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.

SQUID-detected ultra-low field MRI.

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 100mT) followed by read-out in a much weaker (1-100µT) 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 magnetization (i.e. signal) and inherently long acquisition times compared to conventional >1T 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.

SQUIDs vs. Induction Coils for Ultra-Low Field Nuclear Magnetic Resonance: Experimental and Simulation Comparison.

Nuclear magnetic resonance (NMR) is widely used in medicine, chemistry and industry. One application area is magnetic resonance imaging (MRI). Recently it has become possible to perform NMR and MRI in the ultra-low field (ULF) regime requiring measurement field strengths of the order of only 1 Gauss. This technique exploits the advantages offered by superconducting quantum interference devices or SQUIDs. Our group has built SQUID based MRI systems for brain imaging and for liquid explosives detection at airport security checkpoints. The requirement for liquid helium cooling limits potential applications of ULF MRI for liquid identification and security purposes. Our experimental comparative investigation shows that room temperature inductive magnetometers may provide enough sensitivity in the 3-10 kHz range and can be used for fast liquid explosives detection based on ULF NMR technique. We describe experimental and computer-simulation results comparing multichannel SQUID based and induction coils based instruments that are capable of performing ULF MRI for liquid identification.

Microtesla MRI with dynamic nuclear polarization.

Magnetic resonance imaging at microtesla fields is a promising imaging method that combines the pre-polarization technique and broadband signal reception by superconducting quantum interference device (SQUID) sensors to enable in vivo MRI at microtesla-range magnetic fields similar in strength to the Earth magnetic field. Despite significant advances in recent years, the potential of microtesla MRI for biomedical imaging is limited by its insufficient signal-to-noise ratio due to a relatively low sample polarization. Dynamic nuclear polarization (DNP) is a widely used approach that allows polarization enhancement by 2-4 orders of magnitude without an increase in the polarizing field strength. In this work, the first implementation of microtesla MRI with Overhauser DNP and SQUID signal detection is described. The first measurements of carbon-13 NMR spectra at microtesla fields are also reported. The experiments were performed at the measurement field of 96 µT, corresponding to Larmor frequency of 4 kHz for protons and 1 kHz for carbon-13. The Overhauser DNP was carried out at 3.5-5.7 mT fields using rf irradiation at 120 MHz. Objects for imaging included water phantoms and a cactus plant. Aqueous solutions of metabolically relevant sodium bicarbonate, pyruvate, alanine, and lactate, labeled with carbon-13, were used for NMR studies. All the samples were doped with TEMPO free radicals. The Overhauser DNP enabled nuclear polarization enhancement by factor as large as -95 for protons and as large as -200 for carbon-13, corresponding to thermal polarizations at 0.33 T and 1.1 T fields, respectively. These results demonstrate that SQUID-based microtesla MRI can be naturally combined with Overhauser DNP in one system, and that its signal-to-noise performance is greatly improved in this case. They also suggest that microtesla MRI can become an efficient tool for in vivo imaging of hyperpolarized carbon-13, produced by low-temperature dissolution DNP.

Noise Modeling From Conductive Shields Using Kirchhoff Equations.

Progress in the development of high-sensitivity magnetic-field measurements has stimulated interest in understanding the magnetic noise of conductive materials, especially of magnetic shields based on high-permeability materials and/or high-conductivity materials. For example, SQUIDs and atomic magnetometers have been used in many experiments with mu-metal shields, and additionally SQUID systems frequently have radio frequency shielding based on thin conductive materials. Typical existing approaches to modeling noise only work with simple shield and sensor geometries while common experimental setups today consist of multiple sensor systems with complex shield geometries. With complex sensor arrays used in, for example, MEG and Ultra Low Field MRI studies, knowledge of the noise correlation between sensors is as important as knowledge of the noise itself. This is crucial for incorporating efficient noise cancelation schemes for the system. We developed an approach that allows us to calculate the Johnson noise for arbitrary shaped shields and multiple sensor systems. The approach is efficient enough to be able to run on a single PC system and return results on a minute scale. With a multiple sensor system our approach calculates not only the noise for each sensor but also the noise correlation matrix between sensors. Here we will show how the algorithm can be implemented.

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 microT 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.

Parallel MRI at microtesla fields.

Parallel imaging techniques have been widely used in high-field magnetic resonance imaging (MRI). Multiple receiver coils have been shown to improve image quality and allow accelerated image acquisition. Magnetic resonance imaging at ultra-low fields (ULF MRI) is a new imaging approach that uses SQUID (superconducting quantum interference device) sensors to measure the spatially encoded precession of pre-polarized nuclear spin populations at microtesla-range measurement fields. In this work, parallel imaging at microtesla fields is systematically studied for the first time. A seven-channel SQUID system, designed for both ULF MRI and magnetoencephalography (MEG), is used to acquire 3D images of a human hand, as well as 2D images of a large water phantom. The imaging is performed at 46 mu T measurement field with pre-polarization at 40 mT. It is shown how the use of seven channels increases imaging field of view and improves signal-to-noise ratio for the hand images. A simple procedure for approximate correction of concomitant gradient artifacts is described. Noise propagation is analyzed experimentally, and the main source of correlated noise is identified. Accelerated imaging based on one-dimensional undersampling and 1D SENSE (sensitivity encoding) image reconstruction is studied in the case of the 2D phantom. Actual threefold imaging acceleration in comparison to single-average fully encoded Fourier imaging is demonstrated. These results show that parallel imaging methods are efficient in ULF MRI, and that imaging performance of SQUID-based instruments improves substantially as the number of channels is increased.