Accelerated multi-contrast reconstruction for synthetic MRI using joint parallel imaging and variable splitting networks.

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Abstract

Synthetic magnetic resonance imaging (MRI) requires the acquisition of multi-contrast images to estimate quantitative parameter maps, such as T1 , T2 , and proton density (PD). The study aims to develop a multi-contrast reconstruction method based on joint parallel imaging (JPI) and joint deep learning (JDL) to enable further acceleration of synthetic MRI.
The JPI and JDL methods are extended and combined to improve reconstruction for better-quality, synthesized images. JPI is performed as a first step to estimate the missing k-space lines, and JDL is then performed to correct and refine the previous estimate with a trained neural network. For the JDL architecture, the original variable splitting network (VS-Net) is modified and extended to form a joint variable splitting network (JVS-Net) to apply to multi-contrast reconstructions. The proposed method is designed and tested for multi-dynamic multi-echo (MDME) images with Cartesian uniform under-sampling using acceleration factors between 4 and 8.
It is demonstrated that the nRMSE is lower and the structural similarity index (SSIM) values are higher with the proposed method compared to both the JPI and JDL methods individually. The method also demonstrates the potential to produce a set of synthesized contrast-weighted images that closely resemble those from the fully sampled acquisition without erroneous artifacts.
Combining JPI and JDL enables the reconstruction of highly accelerated synthetic MRIs.
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