Purpose To enable acceleration in 3D multi‐echo gradient echo (mGRE) acquisition for myelin water imaging (MWI) by combining joint parallel imaging (JPI) and joint deep learning (JDL). Methods We implemented a multistep reconstruction process using both advanced parallel imaging and deep learning network which can utilize joint spatiotemporal components between the multi‐echo images to further accelerate 3D mGRE acquisition for MWI. In the first step, JPI was performed to estimate missing k‐space lines. Next, JDL was implemented to reduce residual artifacts and produce high‐fidelity reconstruction by using variable splitting optimization consisting of spatiotemporal denoiser block, data consistency block, and weighted average block. The proposed method was evaluated for MWI with 2D Cartesian uniform under‐sampling for each echo, enabling scan times of up to approximately 2 min for 2mm×2mm×2mm$2\ {\rm mm} \times 2\ {\rm mm} \times 2\ {\rm mm}$ 3D coverage. Results The proposed method showed acceptable MWI quality with improved quantitative values compared to both JPI and JDL methods individually. The improved performance of the proposed method was demonstrated by the low normalized mean‐square error and high‐frequency error norm values of the reconstruction with high similarity to the fully sampled MWI. Conclusion Joint spatiotemporal reconstruction approach by combining JPI and JDL can achieve high acceleration factors for 3D mGRE‐based MWI.

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Phase-based EPT algorithm is extremely sensitive to noise. Although many studies have investigated such as linear(Gaussian filter) or non-linear filter(TV norm) to cope with amplification, textured noise and staircasing effect still remain in phase image, which lead to conductivity error such as broadening boundary artifact or high std value in reconstructed conductivity maps. In this study, we propose a deep prior based denoising method, which achieve to not only suppress instability brought by noise amplification but reduce boundary error.

Phase-based Electrical properties tomography is a non-invasive imaging technique that uses MRI systems to measure the tissue conductivity. However, the conductivity reconstruction process causes problems such as noise amplification and boundary artifact. To address such limitations, several DL-based reconstruction methods were proposed. Building upon these works, we propose an ANN-based conductivity reconstruction method trained only on simulation dataset. The proposed method was studied with the aim of: (a) approaching ground-truth conductivity values, (b) noise-robustness, (c) higher image resolution, (d) generalization to clinical data. The feasibility was investigated on simulations and TSE in-vivo data (one healthy volunteer, two meningioma cases).