Bydder M, Larkman D J, Hajnal J V
Robert Steiner MR Unit, MRC Clinical Sciences Centre, Imperial College School of Medicine, Hammersmith Hospital, London, UK.
Magn Reson Med. 2002 Mar;47(3):539-48. doi: 10.1002/mrm.10092.
It is well established that the optimal unbiased way to combine image data from array coils is a pixel-by-pixel sum of coil signals, with each signal weighted by the individual coil sensitivity at the location of the pixel. A pragmatic alternative combines the images from the coils as the square root of the sum of squares (SOS), which can reduce the signal-to-noise ratio (SNR) and introduce bias. This work describes how to replace coil sensitivity by an image-derived quantity that enables close to optimal signal combination up to a global intensity scaling. Typical scaling is by an individual coil sensitivity or a linear or SOS combination of the sensitivities of some or all of the coils in the array. The method decreases signal bias, improves SNR when coils have unequal noise levels, and can reduce image artifacts. It can produce phase-corrected data, which eliminates bias completely. In addition, the method allows images from arrays that include highly localized coils, such as a prostate coil and external pelvic array, to be combined with near-optimal SNR and an intensity modulation that makes them easier to view.
众所周知,将来自阵列线圈的图像数据进行组合的最佳无偏方法是对线圈信号进行逐个像素求和,每个信号由该像素位置处的单个线圈灵敏度加权。一种实用的替代方法是将来自线圈的图像组合为平方和的平方根(SOS),这会降低信噪比(SNR)并引入偏差。这项工作描述了如何用图像衍生的量来替代线圈灵敏度,该量能够在全局强度缩放的情况下实现接近最优的信号组合。典型的缩放是通过单个线圈灵敏度或阵列中部分或所有线圈灵敏度的线性或SOS组合。该方法可降低信号偏差,当线圈具有不相等的噪声水平时提高SNR,并可减少图像伪影。它可以产生相位校正后的数据,从而完全消除偏差。此外,该方法允许将来自包含高度局部化线圈(如前列腺线圈和外部盆腔阵列)的阵列的图像进行组合,具有接近最优的SNR以及使它们更易于查看的强度调制。