Doneva and BГ¶rnert proposed a different method to select the best subset coils by a ranking that quantifies the contribution of each coil to the final reconstructed image. It requires exact coil sensitivities therefore, this kind of coil compression is only fit for sensitivity-based PI reconstructions such as sensitivity encoding. demonstrated that array compression can be very effective by optimizing the signal-to-noise ratio (SNR) of the region of interest in the reconstructed image. Software coil compression is more flexible and accurate. But coil compression in hardware is not always optimal because it does not take the spatial coil sensitivity variation or the received data into consideration. implemented an efficient channel reduction method through hardware by combining eight head coil elements into three channels using the idea of the noise covariance of the receiver arrays. The concept of coil compression is to reduce the number of independent data streams before reconstruction by linearly combining raw data from multiple receiver channels into fewer virtual channels or selecting a small set of most important channels.Д¬oil compression can be applied in either hardware or software. In order to achieve improved data processing efficiency in the large array systems, coil compression was proposed. As the number of channels increases, however, problems of both data storage and processing complexity have emerged. This has significantly accelerated data acquisitions in magnetic resonance imaging (MRI) and led research to apply large coil arrays with up to even 128 independent receiver channels. In the past two decades, with the introduction of multichannel receivers, parallel imaging (PI) has experienced rapid advance from basic technological development to a wide range of clinical applications. Experimental results demonstrated that the GCC algorithm can achieve higher SNR and lower normalized root mean squared error values than the conventional principal component analysis approach in radial acquisitions. Both numerical simulation data and in vivo data were tested. GCC, which is different from directly compressing in -space, is performed separately in each spatial location along the fully sampled directions, then followed by an additional alignment step to guarantee the smoothness of the virtual coil sensitivities. In this work, geometric-decomposition coil compression (GCC) is applied to radial sampling (both linear-angle and golden-angle patterns are discussed) for better compression. Coil compression can effectively solve this problem by linearly combining the raw data from multiple coils into fewer virtual coils. However, longer reconstruction time and intensive computational cost have become significant concerns as the number of channels increases, especially in some iterative reconstructions. Receiver arrays with a large number of coil elements are becoming progressively available because of their increased signal-to-noise ratio (SNR) and enhanced parallel imaging performance.

Cad 2012 coil profile coil