Iterative image reconstruction that includes a total variation regularization for radial MRI.

Abstract

This paper presents an iterative image reconstruction method for radial encodings in MRI based on a total variation (TV) regularization. The algebraic reconstruction method combined with total variation regularization (ART_TV) is implemented with a regularization parameter specifying the weight of the TV term in the optimization process. We used numerical simulations of a Shepp-Logan phantom, as well as experimental imaging of a phantom that included a rectangular-wave chart, to evaluate the performance of ART_TV, and to compare it with that of the Fourier transform (FT) method. The trade-off between spatial resolution and signal-to-noise ratio (SNR) was investigated for different values of the regularization parameter by experiments on a phantom and a commercially available MRI system. ART_TV was inferior to the FT with respect to the evaluation of the modulation transfer function (MTF), especially at high frequencies; however, it outperformed the FT with regard to the SNR. In accordance with the results of SNR measurement, visual impression suggested that the image quality of ART_TV was better than that of the FT for reconstruction of a noisy image of a kiwi fruit. In conclusion, ART_TV provides radial MRI with improved image quality for low-SNR data; however, the regularization parameter in ART_TV is a critical factor for obtaining improvement over the FT.

DOI: 10.1007/s12194-015-0320-7

Cite this paper

@article{Kojima2015IterativeIR, title={Iterative image reconstruction that includes a total variation regularization for radial MRI.}, author={Shinya Kojima and Hiroyuki Shinohara and Takeyuki Hashimoto and Masami Hirata and Eiko Ueno}, journal={Radiological physics and technology}, year={2015}, volume={8 2}, pages={295-304} }