高保真3D多层扩散磁共振成像使用Slab-shifting进行谐化3D采集与重建及轮廓编码网络（SHARPEN）
High-fidelity 3D multi-slab diffusion MRI using Slab-shifting for Harmonized 3D Acquisition and Reconstruction with Profile Encoding Networks (SHARPEN)
AI总结：
SHARPEN通过层位移和轮廓编码网络实现高保真3D多层扩散MRI，提升成像质量和效率，适用于高分辨率在体研究。
摘要：
三维（3D）多层成像是一种有前景的高分辨率在体扩散磁共振成像（dMRI）方法，因为它与短TR（1-2秒）兼容，提供了最优信噪比（SNR）效率。然而，一个主要挑战是由于非理想层选择射频激发产生的层边界伪影。非矩形层轮廓会减少层边界处的信号强度，而相邻层之间的轮廓重叠会引入层间串扰，其中重复激发缩短了局部TR并限制了T1恢复。为了在不增加扫描时间的情况下缓解层边界伪影，我们基于层轮廓编码，提出Slab-shifting用于谐化3D采集与重建及轮廓编码网络（SHARPEN）。对于不同的扩散方向，SHARPEN在切片方向上应用跨体积视野位移，以提供互补的层轮廓编码，而无需延长采集时间。层轮廓通过一个轻量级的自监督神经网络进行估计，该网络利用跨位移采集的一致性和已知的层轮廓和扩散图像的物理性质，并相应地重建校正图像。SHARPEN通过模拟和前瞻性获取的高分辨率在体数据进行了验证，并展示了准确的层轮廓估计和稳健的边界伪影校正，即使在跨体积运动存在的情况下。SHARPEN不需要高质量的参考训练数据，并支持受试者特定的训练。其高效的GPU实现提供了比NPEN更快且更准确的校正，产生切片层面的定量轮廓，这些轮廓与参考2D采集的轮廓非常接近。SHARPEN使在3T临床扫描仪上以0.7毫米各向同性分辨率获得高质量dMRI成为可能，突显了其在神经科学研究中推进亚毫米dMRI的潜力。
英文摘要：
Three-dimensional (3D) multi-slab imaging is a promising approach for high-resolution in vivo diffusion MRI (dMRI) due to its compatibility with short TR (1-2 s), providing optimal signal-to-noise ratio (SNR) efficiency. A major challenge, however, is slab boundary artifacts arising from non-ideal slab-selective RF excitation. Non-rectangular slab profiles reduce signal intensity at slab boundaries, while profile overlap across adjacent slabs introduces inter-slab crosstalk, where repeated excitation shortens the local TR and limits T1 recovery. To mitigate slab boundary artifacts without increasing scan time, we build on slab profile encoding and propose Slab-shifting for Harmonized 3D Acquisition and Reconstruction with Profile Encoding Networks (SHARPEN). For different diffusion directions, SHARPEN applies inter-volume field-of-view shifts along the slice direction to provide complementary slab profile encoding without prolonging acquisition. Slab profiles are estimated using a lightweight self-supervised neural network that exploits consistency across shifted acquisitions and known physical properties of slab profiles and diffusion images, and corrected images are reconstructed accordingly. SHARPEN was validated using simulated and prospectively acquired high-resolution in vivo data and demonstrates accurate slab profile estimation and robust boundary artifact correction, even in the presence of inter-volume motion. SHARPEN does not require high-quality reference training data and supports subject-specific training. Its efficient GPU-based implementation delivers faster and more accurate correction than NPEN, yielding slice-wise quantitative profiles that closely match those from reference 2D acquisitions. SHARPEN enables high-quality dMRI at 0.7 mm isotropic resolution on a 3T clinical scanner, highlighting its potential to advance submillimeter dMRI for neuroscience research.
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