Zero-DeepSub：利用3D-QALAS实现快速多参数定量MRI的零样本深度子空间重建
Zero-DeepSub: Zero-Shot Deep Subspace Reconstruction for Rapid Multiparametric Quantitative MRI Using 3D-QALAS
AI总结：
提出subspace QALAS与零样本Zero-DeepSub重建框架，在最高9倍加速下实现2分钟内1 mm各向同性全脑T1、T2、PD定量成像。
摘要：
目的：开发并评估两类方法：1）采用低秩子空间方法重建带T2准备脉冲的交错Look-Locker采集序列（3D-QALAS）时间序列图像，以实现准确、快速的T1和T2 mapping；2）通过结合针对具体扫描的基于深度学习的重建与子空间建模，提高子空间QALAS的保真度。方法：提出了用于3D-QALAS的低秩子空间方法（即subspace QALAS）和零样本深度学习子空间方法（即Zero-DeepSub），以利用3D-QALAS实现快速、高保真的T1和T2 mapping及时间分辨成像。使用ISMRM/NIST系统体模，通过与参考技术比较，评估了所提方法估计的T1和T2图的准确性与可重复性。在体内评估了所提结合Zero-DeepSub的subspace QALAS的重建性能，并在最高达9倍的高缩减因子下与传统QALAS进行比较。结果：体模实验表明，subspace QALAS相对于参考方法具有良好的线性，同时与传统QALAS相比减少了偏差并提高了精度，尤其是对于T2图。此外，体内结果表明，与传统QALAS相比，subspace QALAS具有更好的g因子图，并可减少体素模糊、噪声和伪影；结合Zero-DeepSub时，其在最高9倍加速下表现出稳健性能，从而能够在2分钟扫描时间内实现1 mm各向同性分辨率的全脑T1、T2和PD mapping。结论：所提出的subspace QALAS与Zero-DeepSub相结合，可实现高保真、快速的全脑多参数量化和时间分辨成像。
英文摘要：
Purpose: To develop and evaluate methods for 1) reconstructing 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) time-series images using a low-rank subspace method, which enables accurate and rapid T1 and T2 mapping, and 2) improving the fidelity of subspace QALAS by combining scan-specific deep-learning-based reconstruction and subspace modeling. Methods: A low-rank subspace method for 3D-QALAS (i.e., subspace QALAS) and zero-shot deep-learning subspace method (i.e., Zero-DeepSub) were proposed for rapid and high fidelity T1 and T2 mapping and time-resolved imaging using 3D-QALAS. Using an ISMRM/NIST system phantom, the accuracy and reproducibility of the T1 and T2 maps estimated using the proposed methods were evaluated by comparing them with reference techniques. The reconstruction performance of the proposed subspace QALAS using Zero-DeepSub was evaluated in vivo and compared with conventional QALAS at high reduction factors of up to 9-fold. Results: Phantom experiments showed that subspace QALAS had good linearity with respect to the reference methods while reducing biases and improving precision compared to conventional QALAS, especially for T2 maps. Moreover, in vivo results demonstrated that subspace QALAS had better g-factor maps and could reduce voxel blurring, noise, and artifacts compared to conventional QALAS and showed robust performance at up to 9-fold acceleration with Zero-DeepSub, which enabled whole-brain T1, T2, and PD mapping at 1 mm isotropic resolution within 2 min of scan time. Conclusion: The proposed subspace QALAS along with Zero-DeepSub enabled high fidelity and rapid whole-brain multiparametric quantification and time-resolved imaging.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
