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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