Optimal design of on-scalp electromagnetic sensor arrays for brain source localisation.

Optically pumped magnetometers (OPMs) are quickly widening the scopes of noninvasive neurophysiological imaging. The possibility of placing these magnetic field sensors on the scalp allows not only to acquire signals from people in movement, but also to reduce the distance between the sensors and the brain, with a consequent gain in the signal-to-noise ratio. These advantages make the technique particularly attractive to characterise sources of brain activity in demanding populations, such as children and patients with epilepsy. However, the technology is currently in an early stage, presenting new design challenges around the optimal sensor arrangement and their complementarity with other techniques as electroencephalography (EEG). In this article, we present an optimal array design strategy focussed on minimising the brain source localisation error. The methodology is based on the Cramér-Rao bound, which provides lower error bounds on the estimation of source parameters regardless of the algorithm used. We utilise this framework to compare whole head OPM arrays with commercially available electro/magnetoencephalography (E/MEG) systems for localising brain signal generators. In addition, we study the complementarity between EEG and OPM-based MEG, and design optimal whole head systems based on OPMs only and a combination of OPMs and EEG electrodes for characterising deep and superficial sources alike. Finally, we show the usefulness of the approach to find the nearly optimal sensor positions minimising the estimation error bound in a given cortical region when a limited number of OPMs are available. This is of special interest for maximising the performance of small scale systems to ad hoc neurophysiological experiments, a common situation arising in most OPM labs.

Error Analysis of Accelerometer- and Magnetometer-Based Stationary Alignment.

This paper revisits the stationary attitude initialization problem, i.e., the stationary alignment, of Attitude and Heading Reference Systems (AHRSs). A detailed and comprehensive error analysis is proposed for four of the most representative accelerometer- and magnetometer-based stationary attitude determination methods, namely, the Three-Axis Attitude Determination (TRIAD), the QUaternion ESTimator (QUEST), the Factored Quaternion Algorithm (FQA), and the Arc-TANgent (ATAN). For the purpose of the error analysis, constant biases in the accelerometer and magnetometer measurements are considered (encompassing, hence, the effect of hard-iron magnetism), in addition to systematic errors in the local gravity and Earth magnetic field models (flux density magnitude, declination angle, and inclination angle). The contributions of this paper are novel closed-form formulae for the residual errors (normality, orthogonality, and alignment errors) developed in the computed Direction Cosine Matrices (DCM). As a consequence, analytical insight is provided into the problem, allowing us to properly compare the performance of the investigated alignment formulations (in terms of ultimate accuracy), as well as to remove some misleading conclusions reported in previous works. The adequacy of the proposed error analysis is validated through simulation and experimental results.

Biomagnetism of tumor in rats with Guerin's carcinoma after injection of ferromagnetic nanocomposite (Ferroplat): contactless measurement.

AIM: In order to develop fundamentally new technologies for non-invasive and safer diagnosis of cancer, we aimed to detect non-contact magnetic signals from a malignant tumor in animals treated or not-treated with the ferromagnetic nanocomposite Ferroplat. MATERIALS AND METHODS: Guerin's carcinoma was used as a model of tumor growth. The biomagnetism of the tumor was evaluated in the dynamics of its growth. Ten days after tumor transplantation, Ferroplat was administered intravenously to half of the animals with the tumor and to half of the control animals. The magnitude of the magnetic signals was determined 1 h and every two days after administration of the nanocomposite using a Superconducting Quantum Interference Device magnetometer of the original design. RESULTS: We have found that the magnetic signals coming from the tumor are significantly higher compared to control tumor-free animals. Intravenous administration of a ferromagnetic nanocomposite (Ferroplat: Fe3O4 + cisplatinum) led to a significant increase of the magnetic signal, especially in the tumor tissue, and inhibition of Guerin's carcinoma growth. Ferromagnetic nanoparticles (32.7 nm) are retained in malignant cells for a longer time than in normal ones. CONCLUSION: Tumor cells accumulate iron nanoparticles more intensively than normal ones. Nanocomposite Ferroplat can be used for a targeted delivery of cisplatin to malignant cells.

Exposure estimates based on broadband ELF magnetic field measurements versus the ICNIRP multiple frequency rule.

The evaluation of exposure to extremely low-frequency (ELF) magnetic fields using broadband measurement techniques gives satisfactory results when the field has essentially a single frequency. Nevertheless, magnetic fields are in most cases distorted by harmonic components. This work analyses the harmonic components of the ELF magnetic field in an outdoor urban context and compares the evaluation of the exposure based on broadband measurements with that based on spectral analysis. The multiple frequency rule of the International Commission on Non-ionizing Radiation Protection (ICNIRP) regulatory guidelines was applied. With the 1998 ICNIRP guideline, harmonics dominated the exposure with a 55% contribution. With the 2010 ICNIRP guideline, however, the primary frequency dominated the exposure with a 78% contribution. Values of the exposure based on spectral analysis were significantly higher than those based on broadband measurements. Hence, it is clearly necessary to determine the harmonic components of the ELF magnetic field to assess exposure in urban contexts.

Automatic determination of validity of input data used in ellipsoid fitting MARG calibration algorithms.

Ellipsoid fitting algorithms are widely used to calibrate Magnetic Angular Rate and Gravity (MARG) sensors. These algorithms are based on the minimization of an error function that optimizes the parameters of a mathematical sensor model that is subsequently applied to calibrate the raw data. The convergence of this kind of algorithms to a correct solution is very sensitive to input data. Input calibration datasets must be properly distributed in space so data can be accurately fitted to the theoretical ellipsoid model. Gathering a well distributed set is not an easy task as it is difficult for the operator carrying out the maneuvers to keep a visual record of all the positions that have already been covered, as well as the remaining ones. It would be then desirable to have a system that gives feedback to the operator when the dataset is ready, or to enable the calibration process in auto-calibrated systems. In this work, we propose two different algorithms that analyze the goodness of the distributions by computing four different indicators. The first approach is based on a thresholding algorithm that uses only one indicator as its input and the second one is based on a Fuzzy Logic System (FLS) that estimates the calibration error for a given calibration set using a weighted combination of two indicators. Very accurate classification between valid and invalid datasets is achieved with average Area Under Curve (AUC) of up to 0:98.

Measurement of liver iron overload: noninvasive calibration of MRI-R2* by magnetic iron detector susceptometer.

An accurate assessment of body iron accumulation is essential for the diagnosis and therapy of iron overload in diseases such as thalassemia or hemochromatosis. Magnetic iron detector susceptometry and MRI are noninvasive techniques capable of detecting iron overload in the liver. Although the transverse relaxation rate measured by MRI can be correlated with the presence of iron, a calibration step is needed to obtain the liver iron concentration. Magnetic iron detector provides an evaluation of the iron overload in the whole liver. In this article, we describe a retrospective observational study comparing magnetic iron detector and MRI examinations performed on the same group of 97 patients with transfusional or congenital iron overload. A biopsy-free linear calibration to convert the average transverse relaxation rate in iron overload (R(2) = 0.72), or in liver iron concentration evaluated in wet tissue (R(2) = 0.68), is presented. This article also compares liver iron concentrations calculated in dry tissue using MRI and the existing biopsy calibration with liver iron concentrations evaluated in wet tissue by magnetic iron detector to obtain an estimate of the wet-to-dry conversion factor of 6.7 ± 0.8 (95% confidence level).