Pseudo-Polar Fourier Transform-Based Compressed Sensing MRI.
The use of radial k-space trajectories has drawn strong interest from researchers for their potential in developing fast imaging methods in magnetic resonance imaging (MRI). Compared with conventional Cartesian trajectories, radial sampling collects more data from the central k-space region and the radially sampled data are more incoherent. These properties are very suitable for compressed sensing (CS)-based fast imaging. When reconstructing under-sampled radial data with CS, regridding and inverse-regridding are needed to transfer data between the image and frequency domains. In each CS iteration, two-dimensional interpolations are implemented twice in the regridding and inverse-regridding, introducing errors and undermining reconstruction quality. To overcome these problems, a radial-like pseudo-polar (PP) trajectory is proposed for the CS MRI applications. The PP trajectory preserves all the essential features of radial trajectory and allows an image reconstruction with PP fast Fourier transform (PPFFT) instead of interpolations. This paper attempts to investigate the performance of PP trajectory-based CS-MRI. In CS-based image reconstruction, the transformation of PP-sampled k-space data into the image domain is realized through PPFFT, which is based on the standard one-dimensional FFT and the fractional Fourier transform. To evaluate the effectiveness of the proposed methods, both numerical and experimental data are used to compare the new methods with conventional approaches. The proposed method provided high-quality reconstruction of the MR images with over 2-dB gain in peak signal-to-noise ratio while keeping structural similarity over 0.88 in different situations. Compared with the conventional radial sampling-based CS MRI methods, the proposed method achieves a more accurate reconstruction with respect to image detail/edge preservation and artifact suppression. The successful implementation of the PP subsampling-based CS scheme provides a practical and accurate CS-based rapid imaging method for clinical applications.
Yang
Y
Yang
Feng
F
Liu
Mingyan
M
Li
Jin
J
Jin
Ewald
E
Weber
Qinghuo
Q
Liu
Stuart
S
Crozier
D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D044962:Data Compression / Q000379:methods*; D005583:Fourier Analysis; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio