用深度能量模型联合重建与校正，提升多 slab 扩散 MRI 的图像质量。
Regularized joint reconstruction and slab combination for accelerated three-dimensional multi-slab diffusion-weighted imaging using multi-scale energy models
- 构建双线性模型，同时优化图像体积与激发剖面，实现联合重建。
- 在不同加速因子下显著减少边界伪影，保持扩散对比度和结构一致性。
- 适合需要高分辨率扩散成像的临床研究与科研人员使用。
本文提出基于能量的轮廓编码方法（EPEN），一种用于从欠采样三维多 slab k 空间数据中进行高分辨率扩散加权 MRI 联合重建的框架，旨在抑制 slab 边界伪影的同时保留精细解剖细节。EPEN 使用双线性前向模型描述多 slab 采集过程，将扩散加权图像体和 slab 激发轮廓均作为未知变量处理。重建被建模为最大后验概率优化问题，包含三项：高斯数据保真项（确保与采集的 k 空间测量一致）、基于 CNN 的深度能量先验（表示干净扩散加权图像的负对数分布），以及约束估计 slab 轮廓趋向初始估计值的二次正则项。学习到的能量先验梯度引导重建向无伪影图像分布收敛。非凸目标函数通过交替最小化求解：图像体更新采用共轭梯度优化的主极小化方案，slab 轮廓更新通过正则化最小二乘法估计。在多种加速因子和 slab 配置下，与传统边界校正方法相比，EPEN 显著降低 slab 边界伪影，同时提升结构一致性和扩散对比度。结果表明，EPEN 能在统一优化框架内实现鲁棒的 3D 多 slab 扩散 MRI 重建与 slab 轮廓校正，依赖于深度能量图像先验。
原文摘要 · Abstract (English)
This work presents Energy-based Profile Encoding, EPEN, a joint reconstruction framework for high-resolution diffusion-weighted MRI from undersampled 3D multi-slab k-space acquisitions, designed to suppress slab-boundary artifacts while preserving fine anatomical detail. EPEN formulates the multi-slab acquisition process using a bilinear forward model in which both the diffusion-weighted image volume and slab excitation profiles are treated as unknown variables. Reconstruction is posed as a maximum a posteriori optimization problem with three components: a Gaussian data-fidelity term enforcing consistency with the acquired k-space measurements, a CNN-based deep energy prior that represents the negative log distribution of clean diffusion-weighted images, and a quadratic regularization term that constrains the estimated slab profiles toward an initial profile estimate. The gradient of the learned energy prior guides accelerated reconstruction toward an artifact-free image distribution. The resulting nonconvex objective is solved using alternating minimization, with image-volume updates performed through a majorize-minimize scheme using conjugate-gradient optimization and slab-profile updates estimated by regularized least squares. Across multiple acceleration factors and slab configurations, EPEN substantially reduced slab-boundary artifacts compared with conventional slab-boundary correction methods, while improving structural consistency and preserving diffusion-weighted contrast. These results demonstrate that EPEN enables robust joint 3D multi-slab diffusion MRI reconstruction and slab-profile correction within a unified optimization framework supported by deep energy-based image priors.
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