Abstract
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. However, longer reconstruction time and intensive computational cost have become significant concerns as the number of channels increases, especially in some iterative reconstructions. Coil compression can effectively solve this problem by linearly combining the raw data from multiple coils into fewer virtual coils. 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. GCC, which is different from directly compressing in k-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. Both numerical simulation data and in vivo data were tested. 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.
Highlights
In the past two decades, with the introduction of multichannel receivers, parallel imaging (PI) [1, 2] has experienced rapid advance from basic technological development to a wide range of clinical applications
Compared with the reference image, the single coil compression (SCC) compression result shows more signal loss while the geometric-decomposition coil compression (GCC) looks similar to the reference image
The 10x difference map between SCC image and the reference image further indicates large compression error owing to the nonoptimal coil compression method
Summary
In the past two decades, with the introduction of multichannel receivers, parallel imaging (PI) [1, 2] has experienced rapid advance from basic technological development to a wide range of clinical applications. 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 [3,4,5,6]. King et al 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 [10]. Coil compression in hardware is not always optimal because it does not take the spatial coil sensitivity variation or the received data into consideration
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