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Abstract #3236

SPOT: SPIRiT Image Reconstruction with Custom Kernel Geometry

Yulin V Chang1, Marta Vidorreta2, Ze Wang3, and John A Detre2

1Radiology, University of Pennsylvania, Philadelphia, PA, United States, 2Neurology, University of Pennsylvania, Philadelphia, PA, United States, 3Hangzhou Normal University, Hangzhou, Zhejiang, China, People's Republic of

In SPIRiT image reconstruction the kernel usually consists of all elements within a square. The number of elements in such a kernel increases rapidly as the kernel size increases, especially for 3D reconstructions. Thus, a large kernel requires a sizable calibration region in k-space and demands significant time for calibration. In this work we proposed and validated a new image reconstruction approach that uses a custom SPIRiT kernel geometry, which we call SPOT. We show that a SPOT kernel is much faster to compute and results in no loss of image quality compared to the traditional SPIRiT kernel.

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