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: Our ULF-MRI system, which includes the <i>x</i>-, <i>y</i>-, and <i>z</i>-gradients (orange, red, and blue, respectively) and the magnet to generate the measurement field (green).

<p>The polarizing and excitation coils are not shown in the figure. B: The posterior view of the system shows 47 SQUID sensors covering the posterior parts of the head.</p

Brain images reconstructed by the regularized SENSE reconstructions with no acceleration (left column).

<p>The data consistency constraint (λ = 0) improves the image by showing a strong signal in the brain parenchyma (middle column). Further, the sparsity prior (λ = 0.1) suppresses the background noise significantly to better delineate the skull and the brain (right column). The pSNR was indicated in each image.</p

The simulated noiseless sum-of-squares (SoS) image from all 47 channels of the ULF-MRI system (left).

<p>At different SNRs compared to the direct SoS reconstruction, SNR can be improved by incorporating the data consistency constraint (λ = 0). Using the sparsity prior (λ = 0.03, 0.1, and 0.5), the residual error can be further reduced with low SNR acquisitions. The residual errors are reported at the lower-right corner of each reconstruction.</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