Motion compensated whole-heart coronary cardiovascular magnetic resonance angiography using focused navigation (fNAV).
Motion compensated whole-heart coronary cardiovascular magnetic resonance angiography using focused navigation (fNAV).
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DOI：
10.1186/s12968-021-00717-4
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发表时间：
2021-03-29
期刊：
影响因子：
--
通讯作者：
Stuber M
中科院分区：
文献类型：
--
作者：
Roy CW;Heerfordt J;Piccini D;Rossi G;Pavon AG;Schwitter J;Stuber M
径向自导航 (RSN) 全心冠状动脉心血管磁共振血管造影 (CCMRA) 是一种自由呼吸技术，可估计和校正呼吸运动。然而，RSN 仅限于一维刚性校正，这对于呼吸模式复杂的患者来说通常是不够的。因此，这项工作的目标是通过将 3D 运动信息和数据的非刚性帧内采集校正合并到称为聚焦导航 (fNAV) 的框架中，提高 3D 径向 CCMRA 的鲁棒性和质量。我们将 fNAV 应用于数值模拟中的 500 个数据集、22 名健康受试者和 549 名心脏病患者。在每个队列中，我们将 fNAV 与 RSN 以及相同数据的呼吸解析超维黄金角径向稀疏并行 (XD-GRASP) 重建进行比较。记录每种方法的重建时间。运动估计精度通过模拟的 fNAV 和地面实况之间的相关性以及体内数据的 fNAV 和图像配准之间的相关性来衡量。在所有模拟数据集和健康受试者以及一部分患者中测量血管清晰度百分比。最后，由一位盲法专家评审员进行主观图像质量分析，他们为每个体内数据集选择最佳图像，并由两名评审员一致同意对一部分患者按李克特量表 0-4 进行评分。 fNAV 图像的重建时间显着高于 RSN（6.1±±2.1 分钟 vs 1.4±0.3 分钟，p<0.025），但显着低于 XD-GRASP（25.6±7.1 分钟，p<0.025）。总体而言，所有数据集的 fNAV 和参考位移估计值之间存在高度相关性 (0.73±0.29)。对于模拟数据、健康受试者和患者，fNAV 导致冠状动脉比所有其他重建方法显着更尖锐 (p<<0.01)。最后，在专家评审员的盲法评估中，fNAV 在 571 个数据集中的 444 个数据集中被选为最佳图像 (78%；p<0.001)，并且 fNAV 图像的共识等级 (2.6±0.6) 显着高于未校正 (1.7±0.7) (p<0.05)，RSN （1.9 ± 0.6）和XD-GRASP（1.8 ± 0.8）。 fNAV 是一种很有前途的技术，可提高 RSN 自由呼吸 3D 全心 CCMRA 的质量。这种新颖的呼吸自导航方法可以从采集的 1D 信号中导出 3D 非刚性运动估计，相对于 1D 平移校正以及 XD-GRASP 重建，图像清晰度在统计上显着改善。因此，有必要进一步研究该技术的诊断影响，以评估其完整的临床效用。
Radial self-navigated (RSN) whole-heart coronary cardiovascular magnetic resonance angiography (CCMRA) is a free-breathing technique that estimates and corrects for respiratory motion. However, RSN has been limited to a 1D rigid correction which is often insufficient for patients with complex respiratory patterns. The goal of this work is therefore to improve the robustness and quality of 3D radial CCMRA by incorporating both 3D motion information and nonrigid intra-acquisition correction of the data into a framework called focused navigation (fNAV). We applied fNAV to 500 data sets from a numerical simulation, 22 healthy subjects, and 549 cardiac patients. In each of these cohorts we compared fNAV to RSN and respiratory resolved extradimensional golden-angle radial sparse parallel (XD-GRASP) reconstructions of the same data. Reconstruction times for each method were recorded. Motion estimate accuracy was measured as the correlation between fNAV and ground truth for simulations, and fNAV and image registration for in vivo data. Percent vessel sharpness was measured in all simulated data sets and healthy subjects, and a subset of patients. Finally, subjective image quality analysis was performed by a blinded expert reviewer who chose the best image for each in vivo data set and scored on a Likert scale 0–4 in a subset of patients by two reviewers in consensus. The reconstruction time for fNAV images was significantly higher than RSN (6.1 ± 2.1 min vs 1.4 ± 0.3, min, p < 0.025) but significantly lower than XD-GRASP (25.6 ± 7.1, min, p < 0.025). Overall, there is high correlation between the fNAV and reference displacement estimates across all data sets (0.73 ± 0.29). For simulated data, healthy subjects, and patients, fNAV lead to significantly sharper coronary arteries than all other reconstruction methods (p < 0.01). Finally, in a blinded evaluation by an expert reviewer fNAV was chosen as the best image in 444 out of 571 data sets (78%; p < 0.001) and consensus grades of fNAV images (2.6 ± 0.6) were significantly higher (p < 0.05) than uncorrected (1.7 ± 0.7), RSN (1.9 ± 0.6), and XD-GRASP (1.8 ± 0.8). fNAV is a promising technique for improving the quality of RSN free-breathing 3D whole-heart CCMRA. This novel approach to respiratory self-navigation can derive 3D nonrigid motion estimations from an acquired 1D signal yielding statistically significant improvement in image sharpness relative to 1D translational correction as well as XD-GRASP reconstructions. Further study of the diagnostic impact of this technique is therefore warranted to evaluate its full clinical utility.
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影响因子：
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