Automatic Spatial Calibration of Ultra-Low-Field MRI for High-Accuracy Hybrid MEG--MRI

With a hybrid MEG--MRI device that uses the same sensors for both modalities, the co-registration of MRI and MEG data can be replaced by an automatic calibration step. Based on the highly accurate signal model of ultra-low-field (ULF) MRI, we introduce a calibration method that eliminates the error sources of traditional co-registration. The signal model includes complex sensitivity profiles of the superconducting pickup coils. In ULF MRI, the profiles are independent of the sample and therefore well-defined. In the most basic form, the spatial information of the profiles, captured in parallel ULF-MR acquisitions, is used to find the exact coordinate transformation required. We assessed our calibration method by simulations assuming a helmet-shaped pickup-coil-array geometry. Using a carefully constructed objective function and sufficient approximations, even with low-SNR images, sub-voxel and sub-millimeter calibration accuracy was achieved. After the calibration, distortion-free MRI and high spatial accuracy for MEG source localization can be achieved. For an accurate sensor-array geometry, the co-registration and associated errors are eliminated, and the positional error can be reduced to a negligible level.Comment: 11 pages, 8 figures. This work is part of the BREAKBEN project and has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 68686

Evaluating the Performance of Ultra-Low-Field MRI for In-vivo 3D Current Density Imaging of the Human Head

Magnetic fields associated with currents flowing in tissue can be measured non-invasively by means of zero-field-encoded ultra-low-field magnetic resonance imaging (ULF MRI) enabling current density imaging (CDI) and possibly conductivity mapping of human head tissues. Since currents applied to a human are limited by safety regulations and only a small fraction of the current passes through the relatively high-resistive skull, a sufficient signal-to-noise ratio (SNR) may be difficult to obtain when using this method. In this work, we study the relationship between the image SNR and the SNR of the field reconstructions from zero-field-encoded data. We evaluate these results for two existing ULF MRI scanners, one ultra-sensitive single-channel system and one whole-head multi-channel system, by simulating sequences necessary for current-density reconstruction. We also derive realistic current-density and magnetic-field estimates from finite-element-method simulations based on a three-compartment head model. We found that existing ULF-MRI systems reach sufficient SNR to detect intra-cranial current distributions with statistical uncertainty below 10%. However, they also reveal that image artifacts influence the reconstruction quality. Further, our simulations indicate that current-density reconstruction in the scalp requires a resolution less than 5 mm and demonstrate that the necessary sensitivity coverage can be accomplished by multi-channel devices.Comment: 18 pages, 8 figures. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 68686

SQUIDs in biomagnetism : A roadmap towards improved healthcare

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

Conductive shield for ultra-low-field magnetic resonance imaging: theory and measurements of eddy currents

Peer reviewe

Magnetic-field modeling with surface currents. Part I. Physical and computational principles of bfieldtools

| openaire: EC/H2020/820393/EU//MACQSIMAL | openaire: EC/H2020/678578/EU//HRMEGSurface currents provide a general way to model magnetic fields in source-free volumes. To facilitate the use of surface currents in magneto-quasistatic problems, we have implemented a set of computational tools in a Python package named bfieldtools. In this work, we describe the physical and computational principles of this toolset. To be able to work with surface currents of the arbitrary shape, we discretize the currents on triangle meshes using piecewise-linear stream functions. We apply analytical discretizations of integral equations to obtain the magnetic field and potentials associated with the discrete stream function. In addition, we describe the computation of the spherical multipole expansion and a novel surface-harmonic expansion for surface currents, both of which are useful for representing the magnetic field in source-free volumes with a small number of parameters. Lastly, we share examples related to magnetic shielding and the surface-coil design using the presented tools.Peer reviewe

Magnetic field modeling with surface currents. Part II. Implementation and usage of bfieldtools

| openaire: EC/H2020/820393/EU//MACQSIMAL | openaire: EC/H2020/678578/EU//HRMEGWe present a novel open-source Python software package, bfieldtools, for magneto-quasistatic calculations using current densities on surfaces of arbitrary shape. The core functionality of the software relies on a stream-function representation of surface-current density and its discretization on a triangle mesh. Although this stream-function technique is well known in certain fields, to date, the related software implementations have not been published or have been limited to specific applications. With bfieldtools, we aimed to produce a general, easy-to-use, and well-documented open-source software. The software package is written purely in Python; instead of explicitly using lower-level languages, we address computational bottlenecks through extensive vectorization and use of the NumPy library. The package enables easy deployment, rapid code development, and facilitates application of the software to practical problems. In this paper, we describe the software package and give an extensive demonstration of its use with an emphasis on one of its main applications-coil design.Peer reviewe

A brain image with different number of averages reconstructed by the regularized SENSE reconstruction with no acceleration.

<p>The pSNR (cyan) and MSE (green) were reported in each image.</p

A hand sum-of-squares (SoS) image (left).

<p>The data consistency constraint (λ = 0) reduces significantly the noticeable vertical strip artifact (middle). Further, the sparsity prior (λ = 0.1) improves the reconstruction only marginally (right). The pSNR was indicated in each image.</p