Buehrer Martin, Pruessmann Klaas P, Boesiger Peter, Kozerke Sebastian
Institute for Biomedical Engineering, University of Zurich and Swiss Federal Institute of Technology, Zurich, Switzerland.
Magn Reson Med. 2007 Jun;57(6):1131-9. doi: 10.1002/mrm.21237.
Arrays with large numbers of independent coil elements are becoming increasingly available as they provide increased signal-to-noise ratios (SNRs) and improved parallel imaging performance. Processing of data from a large set of independent receive channels is, however, associated with an increased memory and computational load in reconstruction. This work addresses this problem by introducing coil array compression. The method allows one to reduce the number of datasets from independent channels by combining all or partial sets in the time domain prior to image reconstruction. It is demonstrated that array compression can be very effective depending on the size of the region of interest (ROI). Based on 2D in vivo data obtained with a 32-element phased-array coil in the heart, it is shown that the number of channels can be compressed to as few as four with only 0.3% SNR loss in an ROI encompassing the heart. With twofold parallel imaging, only a 2% loss in SNR occurred using the same compression factor.
具有大量独立线圈元件的阵列越来越容易获得，因为它们能提供更高的信噪比（SNR）并改善并行成像性能。然而，处理来自大量独立接收通道的数据在重建过程中会增加内存和计算负担。这项工作通过引入线圈阵列压缩来解决这个问题。该方法允许在图像重建之前通过在时域中组合全部或部分数据集来减少独立通道的数据集数量。结果表明，根据感兴趣区域（ROI）的大小，阵列压缩可能非常有效。基于使用心脏中的32元素相控阵线圈获得的二维体内数据，结果表明，在包含心脏的ROI中，通道数量可以压缩至低至四个，而SNR损失仅为0.3%。在使用两倍并行成像时，使用相同的压缩因子，SNR仅损失2%。
