静音、快速的3D多参数映射：基于磁化准备零回波时间MRI
Quiet, rapid 3D multiparametric mapping using magnetization-prepared zero echo time MRI
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中文总结 AI 辅助
MuPa-ZTE结合天然与磁化准备ZTE，实现4.5分钟内静音各向同性3D多参数映射，支持T1/T2稳健成像，并可通过深度学习去噪加速至2分钟。
中文摘要 AI 辅助
目的：介绍并评估MuPa-ZTE，一种结合天然和磁化准备零回波时间（ZTE）采集的静音、快速3D框架，用于多参数映射。方法：MuPa-ZTE将稳态天然ZTE与瞬态磁化准备ZTE相结合。评估了两种实现：用于表观质子密度、T1和T2映射的T2T1-ZTE，以及用于表观质子密度和T1映射的T1-ZTE。两者均在ISMRM/NIST系统体模和两名健康志愿者中进行了评估；T2T1-ZTE还在一名脑转移瘤患者中进行了展示。10分钟的体模和4.5分钟的体内采集被回顾性截断为3、2和1分钟，并使用和不使用基于深度学习的去噪进行重建。评估包括体模一致性、精度、短期重复性、表观信噪比、边缘锐度以及与全时长体内图的一致性。结果：T1估计值在不同实现、时长和重建中均接近体模标称值。在脑相关范围内，T2准确性在低至2分钟时仍得以保持，对较长T2值的敏感性有限。去噪通常降低了变异性并改善了短期重复性。在体内，4.5分钟内获得了1.1毫米各向同性的全脑图；去噪提高了表观信噪比，同时保持了边缘锐度，并在回顾性截断至2分钟后产生了有前景的图像质量。结论：MuPa-ZTE能够在4.5分钟内实现静音、各向同性的3D多参数映射，支持在脑相关范围内进行稳健的T1映射和T2映射，并有望通过基于深度学习的去噪加速至2分钟。
英文摘要
Purpose: To introduce and evaluate MuPa-ZTE, a quiet, rapid 3D framework combining native and magnetization-prepared zero echo time (ZTE) acquisitions for multiparametric mapping. Methods: MuPa-ZTE combines steady-state native ZTE with transient-state magnetization-prepared ZTE. Two implementations were evaluated: T2T1-ZTE for apparent proton density, T1, and T2 mapping, and T1-ZTE for apparent proton density and T1 mapping. Both were assessed in an ISMRM/NIST system phantom and two healthy volunteers; T2T1-ZTE was also demonstrated in a patient with brain metastases. The 10-minute phantom and 4.5-minute in vivo acquisitions were retrospectively truncated to 3, 2, and 1 minute and reconstructed with and without deep learning-based denoising. Evaluations included phantom agreement, precision, short-term repeatability, apparent SNR, edge sharpness, and consistency with full-duration in vivo maps. Results: T1 estimates remained close to nominal phantom values across implementations, durations, and reconstructions. T2 accuracy was maintained down to 2 minutes over the brain-relevant range, with limited sensitivity to longer T2 values. Denoising generally reduced variability and improved short-term repeatability. In vivo, 1.1-mm isotropic whole-brain maps were obtained in 4.5 minutes; denoising increased apparent SNR while preserving edge sharpness and yielded promising image quality after retrospective truncation to 2 minutes. Conclusion: MuPa-ZTE enables quiet, isotropic 3D multiparametric mapping within 4.5 minutes, supporting robust T1 mapping and T2 mapping over a brain-relevant range, with promising acceleration toward 2 minutes using deep learning-based denoising.
发表机构
- Erasmus MC（伊拉斯姆斯大学医学中心）
- GE HealthCare（通用电气医疗）
- Lund University（隆德大学）
- King’s College London（伦敦国王学院）
- TU Delft（代尔夫特理工大学）
机构由 AI 辅助整理，请以论文原文为准。
