23 March 2010 Optimizing kernel size in generalized auto-calibrating partially parallel acquisition in parallel magnetic resonance imaging
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Abstract
Parallel magnetic resonance imaging achieves reduction in scan time by collecting a partial set of signals using an array of receiving coils each with a local sensitivity pattern. An image is then reconstructed from the partial dataset using the additional information of coil sensitivity. GRAPPA (generalized auto calibrating partially parallel acquisitions) is one of the most successful reconstruction techniques in which the missing k-space lines are interpolated from the acquired data in the whole coil array using a convolution kernel estimated from a fully sampled data patch in the center of k-space. The interpolation kernel is usually small but fixed in size for all coils. Here, we show that a variable kernel with a size dependent on the coil sensitivity can lead to better image quality. The kernel size is estimated from the ratio of the coil sensitivities obtained from a reference scan or from the same dataset. Conventional GRAPPA kernel estimation and image reconstruction is modified to employ the variable-size kernel for improved reconstruction. The new technique shows improved image quality compared to GRAPPA.
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Haitham M. Ahmed, Haitham M. Ahmed, Refaat E. Gabr, Refaat E. Gabr, Abou-Bakr M. Youssef, Abou-Bakr M. Youssef, Yasser M. Kadah, Yasser M. Kadah, } "Optimizing kernel size in generalized auto-calibrating partially parallel acquisition in parallel magnetic resonance imaging", Proc. SPIE 7622, Medical Imaging 2010: Physics of Medical Imaging, 762258 (23 March 2010); doi: 10.1117/12.844597; https://doi.org/10.1117/12.844597
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