A discrete convolution kernel for No-DC MRI. (9th July 2015)

• Record Type: Journal Article
• Authors: Zeng, Gengsheng L; Li, Ya
• Is Part Of: Inverse problems. Volume 31:Number 8(2015:Aug.)
• Publication Date: 2015-07-09
• Subjects: tomography -- image reconstruction -- fourier transform -- MRI; Inverse problems (Differential equations) -- Periodicals; 515.357
• DOI: 10.1088/0266-5611/31/8/085006
• Languages: English
• ISSNs: 0266-5611

Abstract

Abstract: An analytical inversion formula for the exponential Radon transform with an imaginary attenuation coefficient was developed in 2007 (2007 Inverse Problems 23 1963–71 ). The inversion formula in that paper suggested that it is possible to obtain an exact MRI (magnetic resonance imaging) image without acquiring low-frequency data. However, this un-measured low-frequency region (ULFR) in the k-space (which is the two-dimensional Fourier transform space in MRI terminology) must be very small. This current paper derives a FBP (filtered backprojection) algorithm based on You's formula by suggesting a practical discrete convolution kernel. A point spread function is derived for this FBP algorithm. It is demonstrated that the derived FBP algorithm can have a larger ULFR than that in the 2007 paper. The significance of this paper is that we present a closed-form reconstruction algorithm for a special case of under-sampled MRI data. Usually, under-sampled MRI data requires iterative (instead of analytical) algorithms with L1 -norm or total variation norm to reconstruct the image.