用联合多尺度能量模型，快速重建高分辨率多对比MRI。
Fast multi-contrast MRI using joint multiscale energy model
- 构建基于CNN的多尺度能量模型，学习多对比图像联合分布。
- 在欠采样数据下实现多对比图像联合重建，细节更清晰。
- 适用于多种多对比成像，尤其适合3D MPNRAGE扫描。
高分辨率3D多对比MRI因扫描时间长而受限。本文提出一种基于CNN的多尺度能量模型，用于学习多对比图像的联合概率分布。将多对比图像从欠采样数据中的联合恢复问题建模为最大后验估计，其中学习到的能量函数作为先验。采用极大化-极小化算法求解优化问题。该方法利用不同对比之间的冗余信息，提升图像保真度。实验表明，相比独立重建各对比的方法，本方案能更好保留细部结构和对比度，重构结果更锐利。虽然本文聚焦3D MPNRAGE采集，但该方法可推广至任意多对比场景。
原文摘要 · Abstract (English)
The acquisition of 3D multicontrast MRI data with good isotropic spatial resolution is challenged by lengthy scan times. In this work, we introduce a CNN-based multiscale energy model to learn the joint probability distribution of the multi-contrast images. The joint recovery of the contrasts from undersampled data is posed as a maximum a posteriori estimation scheme, where the learned energy serves as the prior. We use a majorize-minimize algorithm to solve the optimization scheme. The proposed model leverages the redundancies across different contrasts to improve image fidelity. The proposed scheme is observed to preserve fine details and contrast, offering sharper reconstructions compared to reconstruction methods that independently recover the contrasts. While we focus on 3D MPNRAGE acquisitions in this work, the proposed approach is generalizable to arbitrary multi-contrast settings.
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