Hao-Ting Kung^{1}, Sophia X. Cui^{2}, Jonas T. Kaplan^{3}, Anand A. Joshi^{1}, Richard M. Leahy^{1}, Krishna S. Nayak^{1}, Jay Acharya^{4}, and Justin P. Haldar^{1,3}

^{1}Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, United States, ^{2}Siemens Medical Solutions USA, Inc., Los Angeles, CA, United States, ^{3}Brain and Creativity Institute, University of Southern California, Los Angeles, CA, United States, ^{4}Department of Clinical Radiology, University of Southern California, Los Angeles, CA, United States

There has been substantial recent interest in MRI systems with lower $$$B_0$$$ field strengths, which can improve the value and accessibility of MRI. This work investigates the performance of diffusion tensor imaging on a prototype whole-body 0.55T system equipped with high-performance shielded gradients. Although the images suffer from noise contamination when using conventional image reconstruction techniques, we demonstrate that the use of an SNR-enhancing joint reconstruction technique can substantially reduce noise concerns, enabling high quality diffusion tensor imaging results. In addition, compared to diffusion data acquired on a conventional 3T scanner, the 0.55T images demonstrate substantially reduced susceptibility-induced geometric distortions.

In this work, we investigate the potential of high-resolution diffusion tensor brain imaging on a prototype 0.55T MRI system, with direct comparisons against data acquired from the same subjects on a conventional 3T system. Compared to previous investigations at a similar field strength,

To mitigate noise concerns, we employ an SNR-enhancing joint reconstruction (SER) approach that exploits the shared spatial structure of different diffusion images.

$$\{\hat{\mathbf{p}}_1,\hat{\mathbf{p}}_2,\cdots,\hat{\mathbf{p}}_Q\}=\arg\min_{\{\mathbf{p}_1,\mathbf{p}_2,\cdots,\mathbf{p}_Q\}}\sum^Q_{q=1}\|\mathbf{E}_q\mathbf{p}_q-\mathbf{k}_q\|^2_2+J(\mathbf{p}_1,\mathbf{p}_2,\cdots,\mathbf{p}_Q),$$

where $$$\mathbf{p}_q$$$ is the $$$q$$$th diffusion-encoded image, $$$\mathbf{k}_q$$$ is the corresponding multichannel k-space data, and $$$\mathbf{E}_q$$$ is the forward model that incorporates phase information (for partial Fourier reconstruction) and coil sensitivity maps. The SER regularization penalty $$$J(\cdot)$$$ utilizes a shared compound Markov Random Field edge model that encourages the reconstructed images to be spatially smooth, with correlation between the edge locations of different images.

Notably, the 0.55T images have substantially-reduced susceptibility-induced geometric distortion artifacts than the 3T images do, as expected from the lower field strength.

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- http://brainsuite.org/processing/diffusion/

**Figure 1.** A representative diffusion-weighted image from one of the subjects. We have chosen to show slices from the 0.55T and 3T results that depict similar (though not identical) anatomy.

**Figure 2. ** A representative quantitative MD map from one of the subjects. We have chosen to show slices from the 0.55T and 3T results that depict similar (though not identical) anatomy.

**Figure 3. ** A representative quantitative FA image from one of the subjects, where color-coding has been used to represent tissue orientation. We have chosen to show slices from the 0.55T and 3T results that depict similar (though not identical) anatomy.

**Figure 4. ** A representative trace image from one of the subjects. We have chosen to show slices from the 0.55T and 3T results that depict similar (though not identical) anatomy.