用深度能量模型修复3D扩散MRI的切片伪影，提升图像清晰度。
Three-Dimensional Diffusion-Weighted Multi-Slab MRI With Slice Profile Compensation Using Deep Energy Model
- 在插件式ADMM框架中引入多尺度能量正则化，优化切片拼接
- 相比非正则化和TV正则化方法，图像质量显著提升
- 适合高分辨率临床与科研扩散MRI成像，尤其关注边界伪影问题
三维多切片采集是实现高分辨率扩散加权MRI最佳信噪比效率的常用技术。然而，该技术受限于切片边界伪影，导致强度波动和切片间混叠，降低解剖成像准确性。为解决此问题，本文提出一种在插件式ADMM框架中的正则化切片轮廓编码（PEN）方法，结合多尺度能量（MuSE）正则化，有效改善切片融合重建。实验表明，该方法相比非正则化及总变差（TV）正则化PEN方法，显著提升图像质量。所提正则化PEN框架为高分辨率3D扩散MRI提供了更鲁棒、高效的解决方案，有望在多种应用中实现更清晰、可靠的解剖成像。
原文摘要 · Abstract (English)
Three-dimensional (3D) multi-slab acquisition is a technique frequently employed in high-resolution diffusion-weighted MRI in order to achieve the best signal-to-noise ratio (SNR) efficiency. However, this technique is limited by slab boundary artifacts that cause intensity fluctuations and aliasing between slabs which reduces the accuracy of anatomical imaging. Addressing this issue is crucial for advancing diffusion MRI quality and making high-resolution imaging more feasible for clinical and research applications. In this work, we propose a regularized slab profile encoding (PEN) method within a Plug-and-Play ADMM framework, incorporating multi-scale energy (MuSE) regularization to effectively improve the slab combined reconstruction. Experimental results demonstrate that the proposed method significantly improves image quality compared to non-regularized and TV-regularized PEN approaches. The regularized PEN framework provides a more robust and efficient solution for high-resolution 3D diffusion MRI, potentially enabling clearer, more reliable anatomical imaging across various applications.
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