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arXiv 1910.13422physics.med-phcs.NAeess.IVeess.SPmath.NA

基于正交投影的多线圈MRI重建精度研究

On the reconstruction accuracy of multi-coil MRI with orthogonal projections

Anna Breger, Gabriel Ramos Llorden, Gonzalo Vegas Sanchez - Ferrero, W. Scott Hoge, Martin Ehler, Carl-Fredrik Westin

AI总结:

本文在线性压缩框架下研究多线圈MRI重建的线圈合并步骤,通过随机投影和PCA分析发现L2误差与方差强相关但最小L2误差未必对应最佳视觉效果,且PCA压缩在视觉评估和SNR上优于rSOS等方法。

摘要:

相控阵中采用多线圈进行MRI信号采集如今已十分普遍。使用多个接收线圈可提高信噪比(SNR),并支持加速并行成像方法。其中一些方法(如GRAPPA或SPIRiT)会在k空间域中生成各线圈图像,这些图像需要经过合并才能形成最终图像。线圈合并通常是图像重建的最后一步,其中平方和的平方根(rSOS)方法被频繁使用。这种直接的方法对于高SNR的线圈图像效果良好,但在所有单个线圈均存在伪影或低SNR的图像中可能产生问题。我们旨在在线性压缩框架下分析最终的线圈合并步骤,包括主成分分析(PCA)。利用两个数据集(一个模拟数据集和一个活体数据集),我们使用随机投影作为整个正交投影空间的表示。这使我们能够通过多种重建精度指标研究图像空间中线性压缩的影响。具体而言,L2误差、方差、SNR和视觉结果作为描述最终图像质量的性能指标。我们研究了它们之间的关系,并观察到L2误差与方差强相关,但正如预期的那样,最小的L2误差并不一定对应最佳的视觉结果。在视觉评估和SNR方面,使用PCA的压缩优于所有其他方法,包括在未压缩图像空间数据上的rSOS。

英文摘要:

MRI signal acquisition with multiple coils in a phased array is nowadays commonplace. The use of multiple receiver coils increases the signal-to-noise ratio (SNR) and enables accelerated parallel imaging methods. Some of these methods, like GRAPPA or SPIRiT, yield individual coil images in the k-space domain which need to be combined to form a final image. Coil combination is often the last step of the image reconstruction, where the root sum of squares (rSOS) is frequently used. This straightforward method works well for coil images with high SNR, but can yield problems in images with artifacts or low SNR in all individual coils. We aim to analyze the final coil combination step in the framework of linear compression, including principal component analysis (PCA). With two data sets, a simulated and an in-vivo, we use random projections as a representation of the whole space of orthogonal projections. This allows us to study the impact of linear compression in the image space with diverse measures of reconstruction accuracy. In particular, the $L_2$ error, variance, SNR, and visual results serve as performance measures to describe the final image quality. We study their relationships and observe that the $L_2$ error and variance strongly correlate, but as expected minimal $L_2$ error does not necessarily correspond to the best visual results. In terms of visual evaluation and SNR, the compression with PCA outperforms all other methods, including rSOS on the uncompressed image space data.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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