Bootstrap estimation of the effect of instrument response function uncertainty on the reconstruction of fusion neutron sources.

Neutron imagers are important diagnostics for the inertial confinement fusion implosions at the National Ignition Facility. They provide two- and three-dimensional reconstructions of the neutron source shape that are key indicators of the overall performance. To interpret the shape results properly, it is critical to estimate the uncertainty in those reconstructions. There are two main sources of uncertainties: limited neutron statistics, leading to random errors in the reconstructed images, and incomplete knowledge of the instrument response function (the pinhole-dependent point spread function). While the statistical errors dominate the uncertainty for lower yield deuterium-tritium (DT) shots, errors due to the instrument response function dominate the uncertainty for DT yields on the order of 1016 neutrons or higher. In this work, a bootstrapping method estimates the uncertainty in a reconstructed image due to the incomplete knowledge of the instrument response function. The main reconstruction is created from the fixed collection of pinhole images that are best aligned with the neutron source. Additional reconstructions are then built using subsets of that collection of images. Variations in the shapes of these additional reconstructions originate solely from uncertainties in the instrument response function, allowing us to use them to provide an additional systematic uncertainty estimate.

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.

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.

Probabilistic forward model for electroencephalography source analysis.

Source localization by electroencephalography (EEG) requires an accurate model of head geometry and tissue conductivity. The estimation of source time courses from EEG or from EEG in conjunction with magnetoencephalography (MEG) requires a forward model consistent with true activity for the best outcome. Although MRI provides an excellent description of soft tissue anatomy, a high resolution model of the skull (the dominant resistive component of the head) requires CT, which is not justified for routine physiological studies. Although a number of techniques have been employed to estimate tissue conductivity, no present techniques provide the noninvasive 3D tomographic mapping of conductivity that would be desirable. We introduce a formalism for probabilistic forward modeling that allows the propagation of uncertainties in model parameters into possible errors in source localization. We consider uncertainties in the conductivity profile of the skull, but the approach is general and can be extended to other kinds of uncertainties in the forward model. We and others have previously suggested the possibility of extracting conductivity of the skull from measured electroencephalography data by simultaneously optimizing over dipole parameters and the conductivity values required by the forward model. Using Cramer-Rao bounds, we demonstrate that this approach does not improve localization results nor does it produce reliable conductivity estimates. We conclude that the conductivity of the skull has to be either accurately measured by an independent technique, or that the uncertainties in the conductivity values should be reflected in uncertainty in the source location estimates.

Spatio-temporal mapping of rat whisker barrels with fast scattered light signals.

Optical techniques offer a number of potential advantages for imaging dynamic spatio-temporal patterns of activity in neural tissue. The methods provide the wide field of view required to image population activation across networks, while allowing resolution of the detailed structure of individual cells. Optical probes can provide high temporal resolution without penetrating the tissue surface. However, functional optical imaging has been constrained by the small size of the signals and the sluggish nature of the metabolic and hemodynamic responses that are the basis of most existing methods. Here, we employ both high-speed CCD cameras and high-sensitivity photodiodes to optimize resolution in both space and time, together with dark-field illumination in the near-infrared, to record fast intrinsic scattering signals from rat somatosensory cortex in vivo. Optical responses tracked the physiological activation of cortical columns elicited by single whisker twitches. High-speed imaging produced maps that were initially restricted in space to individual barrels, and then spread over time. Photodiode recordings disclosed 400-600 Hz oscillatory responses, tightly correlated in frequency and phase to those seen in simultaneous electrical recordings. Imaging based on fast intrinsic light scattering signals eventually could provide high resolution dynamic movies of neural networks in action.

SQUID detected NMR in microtesla magnetic fields.

We have built an NMR system that employs a superconducting quantum interference device (SQUID) detector and operates in measurement fields of 2-25 microT. The system uses a pre-polarizing field from 4 to 30 mT generated by simple room-temperature wire-wound coils that are turned off during measurements. The instrument has an open geometry with samples located outside the cryostat at room-temperature. This removes constraints on sample size and allows us to obtain signals from living tissue. We have obtained 1H NMR spectra from a variety of samples including water, mineral oil, and a live frog. We also acquired gradient encoded free induction decay (FID) data from a water-plastic phantom in the microT regime, from which simple projection images were reconstructed. NMR signals from samples inside metallic containers have also been acquired. This is possible because the penetration skin depth is much greater at the low operating frequencies of this system than for conventional systems. Advantages to ultra-low field NMR measurements include lower susceptibility artifacts caused by high strength polarizing and measurement fields, and negligible line width broadening due to measurement field inhomogeneity, reducing the burden of producing highly homogeneous fields.