Naoharu Kobayashi1, Djaudat Idiyatullin1, Curtis A. Corum1, Michael Garwood1
1Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States
Linogram sampling, introduced to radial MRI about 20 years ago, is a semi-Cartesian k-space sampling method, where the sampling pattern is a concentric square (2D) or cubic grid (3D). For linogram sampling, there is a corresponding reconstruction algorithm known as linogram reconstruction that does not need explicit interpolation and simplifies the Jacobian calculation, i.e. density correction. Therefore, linogram reconstruction has potential to improve resolution, minimize interpolation errors, and reduce computational time. In this study, we show point spread functions and reconstructed images from 3D linogram data that were calculated with the three reconstruction methods: gridding, backprojection and linogram reconstruction.