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

HTS SQUID measurements of evoked magnetic fields from transverse hippocampal slices

We have developed a high-transition-temperature superconducting quantum interference device system aimed at neuromagnetic measurements of evoked fields from in vitro brain slices. Recordings from transverse hippocampal slices from rat have been performed and they showed neuromagnetic fields of ∼ 5 p

Development of a High-Tc SQUID-Based System for Neurophysiology Studies In-Vitro

In this paper we report on the development of a system based on a high-T c SQUID (HTS) sensor for measurements of the neuromagnetic field generated by neurons inside tissue slices. SQUIDs have successfully been measured inside the system. The system white noise level is lower than 7 pT/Hz 1/2 , which is only slightly higher than previously reported 4.5 pT/Hz 1/2 for the same kind of SQUID measured inside a superconducting shield. \ua9 2006 IOP Publishing Ltd

Expanded measurements from station Lindenberg (2016-08)

The prototypes of ultra-low-field (ULF) MRI scanners developed in recent years represent new, innovative, cost-effective and safer systems, which are suitable to be integrated in multi-modal (Magnetoencephalography and MRI) devices. Integrated ULF-MRI and MEG scanners could represent an ideal solution to obtain functional (MEG) and anatomical (ULF MRI) information in the same environment, without errors that may limit source reconstruction accuracy. However, the low resolution and signal-to-noise ratio (SNR) of ULF images, as well as their limited coverage, do not generally allow for the construction of an accurate individual volume conductor model suitable for MEG localization. Thus, for practical usage, a high-field (HF) MRI image is also acquired, and the HF-MRI images are co-registered to the ULF-MRI ones. We address here this issue through an optimized pipeline (SWIM—Sliding WIndow grouping supporting Mutual information). The co-registration is performed by an affine transformation, the parameters of which are estimated using Normalized Mutual Information as the cost function, and Adaptive Simulated Annealing as the minimization algorithm. The sub-voxel resolution of the ULF images is handled by a sliding-window approach applying multiple grouping strategies to down-sample HF MRI to the ULF-MRI resolution. The pipeline has been tested on phantom and real data from different ULF-MRI devices, and comparison with well-known toolboxes for fMRI analysis has been performed. Our pipeline always outperformed the fMRI toolboxes (FSL and SPM). The HF–ULF MRI co-registration obtained by means of our pipeline could lead to an effective integration of ULF MRI with MEG, with the aim of improving localization accuracy, but also to help exploit ULF MRI in tumor imaging.Peer reviewe

Distribution of d_RMS for SWIM and FSL.

<p>The violin plot of the distribution of d_RMS after running the consistency test over one hundred of different starting images for both SWIM and FSL, suggests that SWIM error is significantly lower than FSL coregistration error.</p

3D co-registration of ultra-low-field and high-field magnetic resonance images (data)

<p>Dataset used for "3D co-registration of ultra-low-field and high-field magnetic resonance images" submitted to PlosOne.</p

Comparison between SWIM and fMRI software packages.

<p>The results obtained with SWIM on the brain images recorded at 46 μT are compared with the outcomes of two different co-registration software packages routinely used for fMRI analysis. The corresponding NMI coefficients obtained after optimization are reported in the last row. The best result is obtained with SWIM. The co-registration procedure of fMRI processing software is very fast and efficient for fMRI analysis, but it is not adequate for co-registering ULF and HF images.</p

Schematic drawing illustrating the grouping procedure.

<p>The figure shows an example of the grouping procedure to down-sample a 2D image with a voxel size 1×1 mm² into another one with 2x2mm² resolution. We have four possible groupings, shown here in different colors (red, green, orange and pink). Each grouping relates to a different offset out of the possible four. Notably, at each grouping window a different value is obtained when voxels are averaged, showing how this procedure modifies the information content of the image, particularly when the image to be down-sampled has a large number of edges.</p

Co-registration of brain data recorded at 46 μT and 1.5 T.

<p>Panel a) Co-registration results on an <i>in-vivo</i> brain dataset recorded at 46 μT. a) Four sample slides. The left column represents the target ULF images, the middle column contains the HF co-registered images and the right column shows the overlap between the two image sets, where HF images are in grey tones and ULF images in green tones. b) and c) The joint histogram before and after co-registration, respectively. The red ellipses demarcate background voxels. These are badly aligned in the starting histogram, as suggested by the spread peak along the column corresponding to the background in the ULF image (0 gray value) and to background and head voxels in the HF image. In the final histogram the peak is concentrated close to the origin of the joint histogram, suggesting that background voxels are aligned in the final histogram. The violet ellipses represent some brain and skull structures that are misaligned in the starting configuration (the peak is wide); while in the final histogram a sharper peak is shown around ULF gray-level of 50. The yellow ellipses represent some structures with highest gray value like eyes and white matter that are less sharp in the starting joint histogram, while have a more clear structure in final configuration, demonstrating the good alignment of the images.</p

Co-registration of HF and ULF images at 50 μT.

<p>a) Co-registration of the dataset acquired at Aalto University. On the left the ULF image of the brain and on the right the down-sampled co-registered image at HF. b) NMI as function of the sliding window offset on the three (<i>x</i>, <i>y</i>, <i>z</i>) directions for <i>x</i>_off = 1, <i>y</i>_off = 1, <i>z</i>_off = 0.</p