Jaejin Cho^{1,2}, Borjan Gagoski^{2,3}, Tae Hyung Kim^{1,2}, Qiyuan Tian^{1,2}, Robert Frost^{1,2}, Itthi Chatnuntawech^{4}, and Berkin Bilgic^{1,2,5}

^{1}Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States, ^{2}Department of Radiology, Harvard Medical School, Boston, MA, United States, ^{3}Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children’s Hospital, Boston, MA, United States, ^{4}National Nanotechnology Center, Pathum Thani, Thailand, ^{5}Harvard/MIT Health Sciences and Technology, Cambridge, MA, United States

We propose a wave-encoded model-based deep learning (wave-MoDL) method for joint multi-contrast image reconstruction with volumetric encoding using an interleaved look-locker acquisition sequence with T_{2} preparation pulse (3D-QALAS). Wave-MoDL enables a 2-minute acquisition at R=4x3-fold acceleration using a 32-channel array to provide T_{1}, T_{2}, and proton density maps at 1 mm isotropic resolution, from which standard contrast-weighted images can also be synthesized.

$$\textit{x}=\underset{\textit{x}}{\mathrm{argmin}}\sum_{m}^{M}{{\left\|\mathbf{W}_{m}\mathbf{\mathcal{F}}_y\mathbf{P}\mathbf{\mathcal{F}}_x\mathbf{C}\textit{x}_{m}-\textit{b}_{m}\right\|}_2^2}+\lambda_1{\left\|\mathit{\mathbf{N}}_k(\textit{x})\right\|}_2^2+\lambda_2{\left\|\mathit{\mathbf{N}}_i(\textit{x})\right\|}_2^2$$ $$=\underset{\textit{x}}{\mathrm{argmin}}\sum_{m}^{M}{{\left\|\mathbf{A}_{m}\textit{x}_{m}-\textit{b}_{m}\right\|}_2^2}+\lambda_1{\left\|\mathit{\mathbf{N}}_k(\textit{x})\right\|}_2^2+\lambda_2{\left\|\mathit{\mathbf{N}}_i(\textit{x})\right\|}_2^2$$

where $$$\textit{x}=\left\{\textit{x}_m|m=1...M\right\}$$$ are the reconstructed images, M is the number of contrasts, $$$\mathbf{W}_{m}$$$ is the subsampling mask for the m-th contrast, $$$\mathbf{P}$$$ is the wave point spread function in the k

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DOI: https://doi.org/10.58530/2022/0435