Abstract
Sampling density compensation is an important step in non- cartesian image reconstruction. One of the common techniques to determine weights that compensate for differences in sampling density involves a convolution. A new convolution kernel is designed for sampling density attempting to minimize the error in a fully reconstructed image. The resulting weights obtained using this new kernel are compared with various previous methods, showing a reduction in reconstruction error. A computationally efficient algorithm is also presented that facilitates the calculation of the convolution of finite kernels. Both the kernel and the algorithm are extended to 3D.
Original language | English (US) |
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Pages (from-to) | 439-447 |
Number of pages | 9 |
Journal | Magnetic Resonance in Medicine |
Volume | 61 |
Issue number | 2 |
DOIs | |
State | Published - Feb 2009 |
Keywords
- Gridding
- Non-cartesian
- Psf weighting
- Sampling density
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging