Department of Radiology, Stanford University, Stanford, California, USA.
J Magn Reson Imaging. 2013 Jan;37(1):243-8. doi: 10.1002/jmri.23750. Epub 2012 Jul 12.
To apply compressed sensing (CS) to in vivo multispectral imaging (MSI), which uses additional encoding to avoid magnetic resonance imaging (MRI) artifacts near metal, and demonstrate the feasibility of CS-MSI in postoperative spinal imaging.
Thirteen subjects referred for spinal MRI were examined using T2-weighted MSI. A CS undersampling factor was first determined using a structural similarity index as a metric for image quality. Next, these fully sampled datasets were retrospectively undersampled using a variable-density random sampling scheme and reconstructed using an iterative soft-thresholding method. The fully and undersampled images were compared using a 5-point scale. Prospectively undersampled CS-MSI data were also acquired from two subjects to ensure that the prospective random sampling did not affect the image quality.
A two-fold outer reduction factor was deemed feasible for the spinal datasets. CS-MSI images were shown to be equivalent or better than the original MSI images in all categories: nerve visualization: P = 0.00018; image artifact: P = 0.00031; image quality: P = 0.0030. No alteration of image quality and T2 contrast was observed from prospectively undersampled CS-MSI.
This study shows that the inherently sparse nature of MSI data allows modest undersampling followed by CS reconstruction with no loss of diagnostic quality.
将压缩感知（CS）应用于体内多光谱成像（MSI），该方法使用额外的编码来避免金属附近的磁共振成像（MRI）伪影，并证明 CS-MSI 在术后脊柱成像中的可行性。
对 13 名因脊柱 MRI 而就诊的患者进行了 T2 加权 MSI 检查。首先使用结构相似性指数作为图像质量指标确定 CS 欠采样因子。接下来，使用可变密度随机采样方案对这些完全采样数据集进行回顾性欠采样，并使用迭代软阈值方法进行重建。使用 5 分制对完全采样和欠采样图像进行比较。还从两名患者前瞻性地获取了 CS-MSI 数据，以确保前瞻性随机采样不会影响图像质量。
认为对于脊柱数据集，两倍的外部缩减因子是可行的。CS-MSI 图像在所有类别中均等同于或优于原始 MSI 图像：神经可视化：P = 0.00018；图像伪影：P = 0.00031；图像质量：P = 0.0030。从前瞻性欠采样 CS-MSI 中未观察到图像质量和 T2 对比的改变。
本研究表明，MSI 数据的固有稀疏性允许适度的欠采样，然后进行 CS 重建，而不会降低诊断质量。
