AI 中文总结
本研究提出高度加速的三维笛卡尔MPnRAGE序列，结合隐式神经表示重建，在R=20下5.39分钟获取1mm³图像，生成多个高质量反转对比图像，缩短扫描时间并提升重建质量。
AI 中文摘要
MPnRAGE能够在单次扫描中获取多个反转对比图像，从而实现定量T1映射、组织零对比以及标准MPRAGE合成。然而，当前三维扫描时间在临床上仍不实用，这推动了加速三维MPnRAGE的发展。本研究提供了一种高度加速的笛卡尔三维MPnRAGE序列，并采用联合隐式神经表示（INR）重建。该序列采用了定制的视图排序策略、翻转角调度和互补的可变密度泊松盘欠采样。在原本未使用的延迟时间内采集的校准数据用于灵敏度图估计和互补高频采样。通过回顾性欠采样，将十个INR重建的1.5 mm³反转图像与全采样参考图像进行评估。前瞻性加速的1 mm³图像在R=20（5.39分钟）下采集，证明了临床可行性。在高度加速数据上，INR重建优于子空间和迭代局部低秩重建。所提出的高度欠采样三维笛卡尔MPnRAGE结合INR重建，在显著缩短扫描时间的同时生成多个高质量的反转对比图像。与最先进的方法相比，扫描特定的INR重建提高了图像质量，同时减少了重建时间。
英文摘要
MPnRAGE enables multiple inversion contrast images in a single scan, allowing quantitative T1 mapping, tissue nulled contrasts, and standard MPRAGE synthesis. However, current 3D scan times remain clinically impractical, motivating accelerated 3D MPnRAGE. This work provides a highly accelerated Cartesian 3D MPnRAGE sequence with joint implicit neural representation (INR) reconstruction. The sequence uses a tailored view-ordering strategy, flip angle schedule and complementary variable-density Poisson-disk undersampling. Calibration data acquired during otherwise unused delay time are used for sensitivity map estimation and complementary high-frequency sampling. Ten INR-reconstructed inversion images at 1.5 mm$^3$ are evaluated against fully sampled references via retrospective undersampling. Prospectively accelerated 1 mm$^3$ images at R = 20 (5.39 min) demonstrate clinical feasibility. INR reconstruction outperforms subspace and iterative local low rank reconstruction on highly accelerated data. The proposed highly undersampled 3D Cartesian MPnRAGE with INR reconstruction generates multiple high-quality inversion contrasts in substantially reduced scan time. Scan-specific INR reconstruction improves image quality while reducing reconstruction time versus state-of-the-art methods.
