通过专用重建改进多延迟动脉自旋标记
Improving Multi-Delay-ASL through specialized reconstruction
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中文总结 AI 辅助
本研究将专用ASL重建方法ASL-TGV扩展到多延迟数据，在高分辨率pCASL测试-重测数据集上验证了其能显著提高灌注加权图像信噪比（灰质62%、白质35%）和脑血流量图的可重复性，并已集成至BART工具箱。
中文摘要 AI 辅助
目的：尽管图像重建迄今为止在动脉自旋标记（ASL）研究中受到的关注相对较少，但它有潜力解决ASL中的若干挑战。除了通过增加欠采样和减少测量伪影来加速测量外，它还可以提高给定数据集的信噪比（SNR）以及检查的可重复性。方法：本研究重点将一种专用的ASL重建方法（ASL-TGV）（Spann等人，2020年）扩展到多延迟数据，并将其应用于高分辨率pCASL测试-重测数据集。为了展示不仅灌注加权图像（PWIs）的改进，还估计了脑血流量和动脉通过时间，并使用受试者内变异系数（wsCV）、组内相关系数（ICC）和均方根误差（RMSE）估计了测试-重测可靠性。结果：使用ASL-TGV从高度欠采样的单次激发数据重建的灌注加权图像，与来自相同数据的完全采样参考相比，即使在去噪后也明显改善，尤其是在长PLD和最外层切片中。在灰质和白质感兴趣区中计算的SNR分别提高了62%和35%。由ASL-TGV图像生成的CBF图具有更好的测试-重测可靠性。结论：ASL-TGV现已在伯克利高级重建工具箱（BART）中实现，可与任何类型的ASL标记或数据采集以及任何现有的图像后处理流程一起使用，并能提高PWIs的SNR和CBF图的可重复性。
英文摘要
Purpose: Although image reconstruction has received relatively little attention in ASL research to date, it has the potential to address several challenges in ASL. As well as speeding up measurements by increasing undersampling and reducing measurement artifacts, it can improve the signal-to-noise ratio (SNR) of a given data set and the reproducibility of examinations. Methods: This work focuses on extending a dedicated ASL reconstruction approach (ASL-TGV) (Spann et al. (2020)) to multi-delay data and applying it to a high-resolution pCASL test-retest dataset. To show not only improvement in the Perfusion Weighted Images (PWIs), Cerebral Blood Flow and Arterial Transit Time was estimated and the test-retest reliability was estimated using the within subject Coefficient of Variance (wsCV), the Intraclass Correlation Coefficient (ICC) and Root-Mean-Squared-Error (RMSE). Results: The Perfusion-Weighted-Images reconstructed from highly undersampled single-shot data using ASL-TGV are clearly improved compared to a fully-sampled reference from the same data even after denoising, especially for long PLDs and the outermost slices. SNR calculated in grey and white matter ROIs shows an improvement of 62\% and 35\% respectively. The CBF maps produced from the ASL-TGV images have an improved test-retest reliability. Conclusion: ASL-TGV, which is now implemented in the Berkeley Advanced Reconstruction Toolbox (BART), can be used with any type of ASL labeling or data acquisition and any existing image post-processing pipeline and can improve SNR for the PWIs and reproducibility of the CBF maps.
发表机构
- Graz University of Technology（格拉茨工业大学）
- Institute of Biomedical Imaging, TU Graz（TU格拉茨生物医学研究所）
- Laboratory of Functional MRI Technology, Mark & Mary Stevens Neuroimaging & Informatics Institute, University of Southern California（南加州大学马克与玛丽·史蒂文斯神经成像与信息学研究所功能磁共振技术实验室）
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