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Abstract #2230

Generalized High-Pass-Filtered GRAPPA Reconstruction

Suhyung Park1, Jaeseok Park1

1Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea, Republic of

Parallel imaging techniques have been widely used to reduce total acquisition time and subject motion in clinical application by using the spatial information inherent in a multiple receiver coils. However, with increasing acceleration factors, they lead to residual artifacts and amplified noises over the whole image due to corrupted data with noise. To overcome these problems, several regularization approaches have been proposed using the framework of Tikhonov regularization, such as prior-regularized GRAPPA, but a direct tradeoff between image blurring and noise amplification still remain substantially. From a different viewpoint, high pass GRAPPA (HP-GRAPPA) tried to address this problem controlling low frequency energy with high pass filter (HPF), but was still challenging to find optimal high pass band in k-space. In this work, we propose generalized HP-GRAPPA (GHP-GRAPPA) resolving a high pass band problem as a new regularization approach with high accuracy and quality.