Department of Radiology, Weill Cornell Medical College, New York, New York, USA.
Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA.
Magn Reson Med. 2022 Mar;87(3):1583-1594. doi: 10.1002/mrm.29057. Epub 2021 Oct 31.
To improve accuracy and speed of quantitative susceptibility mapping plus quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) -based oxygen extraction fraction (OEF) mapping using a deep neural network (QQ-NET).
The 3D multi-echo gradient echo images were acquired in 34 ischemic stroke patients and 4 healthy subjects. Arterial spin labeling and diffusion weighted imaging (DWI) were also performed in the patients. NET was developed to solve the QQ model inversion problem based on Unet. QQ-based OEF maps were reconstructed with previously introduced temporal clustering, tissue composition, and total variation (CCTV) and NET. The results were compared in simulation, ischemic stroke patients, and healthy subjects using a two-sample Kolmogorov-Smirnov test.
In the simulation, QQ-NET provided more accurate and precise OEF maps than QQ-CCTV with 150 times faster reconstruction speed. In the subacute stroke patients, OEF from QQ-NET had greater contrast-to-noise ratio (CNR) between DWI-defined lesions and their unaffected contralateral normal tissue than with QQ-CCTV: 1.9 ± 1.3 vs 6.6 ± 10.7 (p = 0.03). In healthy subjects, both QQ-CCTV and QQ-NET provided uniform OEF maps.
QQ-NET improves the accuracy of QQ-based OEF with faster reconstruction.
利用深度神经网络（QQ-NET）提高定量磁化率映射加定量血氧水平依赖幅度（QSM+qBOLD 或 QQ）氧提取分数（OEF）图的准确性和速度。
在 34 名缺血性脑卒中患者和 4 名健康受试者中采集了 3D 多回波梯度回波图像。患者还进行了动脉自旋标记和弥散加权成像（DWI）。NET 是基于 U-net 开发的，用于解决 QQ 模型反演问题。利用先前介绍的时间聚类、组织成分和全变差（CCTV）和 NET 重建基于 QQ 的 OEF 图。通过双样本 Kolmogorov-Smirnov 检验，在模拟、缺血性脑卒中患者和健康受试者中比较结果。
在模拟中，与 QQ-CCTV 相比，QQ-NET 提供了更准确和更精确的 OEF 图，重建速度快 150 倍。在亚急性期脑卒中患者中，与 QQ-CCTV 相比，来自 QQ-NET 的 OEF 在 DWI 定义的病变与其未受影响的对侧正常组织之间具有更高的对比噪声比（CNR）：1.9±1.3 比 6.6±10.7（p=0.03）。在健康受试者中，QQ-CCTV 和 QQ-NET 均提供均匀的 OEF 图。
QQ-NET 提高了基于 QQ 的 OEF 的准确性，同时加快了重建速度。
