乳腺动态对比增强磁共振成像压缩感知中数据驱动与一般时间约束的比较。
Comparison of data-driven and general temporal constraints on compressed sensing for breast DCE MRI.
作者信息
Wang Ping N, Velikina Julia V, Strigel Roberta M, Henze Bancroft Leah C, Samsonov Alexey A, Cashen Ty A, Wang Kang, Kelcz Frederick, Johnson Kevin M, Korosec Frank R, Ersoz Ali, Holmes James H
机构信息
Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA.
Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA.
出版信息
Magn Reson Med. 2021 Jun;85(6):3071-3084. doi: 10.1002/mrm.28628. Epub 2020 Dec 11.
PURPOSE
Current breast DCE-MRI strategies provide high sensitivity for cancer detection but are known to be insufficient in fully capturing rapidly changing contrast kinetics at high spatial resolution across both breasts. Advanced acquisition and reconstruction strategies aim to improve spatial and temporal resolution and increase specificity for disease characterization. In this work, we evaluate the spatial and temporal fidelity of a modified data-driven low-rank-based model (known as MOCCO, model consistency condition) compressed-sensing (CS) reconstruction compared to CS with temporal total variation with radial acquisition for high spatial-temporal breast DCE MRI.
METHODS
Reconstruction performance was characterized using numerical simulations of a golden-angle stack-of-stars breast DCE-MRI acquisition at 5-second temporal resolution. Specifically, MOCCO was compared with CS total variation and conventional SENSE reconstructions. The temporal model for MOCCO was prelearned over the source data, whereas CS total variation was performed using a first-order temporal gradient sparsity transform.
RESULTS
The MOCCO reconstruction was able to capture rapid lesion kinetics while providing high image quality across a range of optimal regularization values. It also recovered kinetics in small lesions (1.5 mm) in line-profile analysis and error images, whereas g-factor maps showed relatively low and constant values with no significant artifacts. The CS-TV method demonstrated either recovery of high spatial resolution with reduced temporal accuracy using large regularization values, or recovery of rapid lesion kinetics with reduced image quality using low regularization values.
CONCLUSION
Simulations demonstrated that MOCCO with radial acquisition provides a robust imaging technique for improving temporal fidelity, while maintaining high spatial resolution and image quality in the setting of bilateral breast DCE MRI.
目的
当前的乳腺动态对比增强磁共振成像（DCE-MRI）策略对癌症检测具有高灵敏度，但已知在以高空间分辨率全面捕捉双侧乳腺快速变化的对比剂动力学方面存在不足。先进的采集和重建策略旨在提高空间和时间分辨率，并增加疾病特征描述的特异性。在本研究中，我们评估了一种改进的基于数据驱动低秩模型（称为MOCCO，模型一致性条件）的压缩感知（CS）重建与采用径向采集的具有时间总变分的CS重建在高时空乳腺DCE MRI中的空间和时间保真度。
方法
使用以5秒时间分辨率进行的黄金角星形堆叠乳腺DCE-MRI采集的数值模拟来表征重建性能。具体而言，将MOCCO与CS总变分和传统的灵敏度编码（SENSE）重建进行比较。MOCCO的时间模型是在源数据上预先学习的，而CS总变分是使用一阶时间梯度稀疏变换进行的。
结果
MOCCO重建能够捕捉快速的病变动力学，同时在一系列最佳正则化值范围内提供高图像质量。在线轮廓分析和误差图像中，它还能恢复小病变（1.5毫米）的动力学，而g因子图显示相对较低且恒定的值，无明显伪影。CS-TV方法在使用大正则化值时能恢复高空间分辨率但时间精度降低，或在使用低正则化值时能恢复快速病变动力学但图像质量降低。
结论
模拟表明，采用径向采集的MOCCO为改善时间保真度提供了一种强大的成像技术，同时在双侧乳腺DCE MRI中保持高空间分辨率和图像质量。
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