Compressed Sensing MRI via Two-stage Reconstruction

Abstract

Compressed sensing (CS) has been applied to magnetic resonance imaging for the acceleration of data collection. However, existing CS techniques usually produce images with residual artifacts, particularly at high reduction factors. In this paper, we propose a novel, two-stage reconstruction scheme, which takes advantage of the properties of k-space data and under-sampling patterns that are useful in CS. In this algorithm, the under-sampled k-space data is segmented into low-frequency and high-frequency domains. Then, in stage one, using dense measurements, the low-frequency region of k-space data is faithfully reconstructed. The fully reconstituted low-frequency k-space data from the first stage is then combined with the high-frequency k-space data to complete the second stage reconstruction of the whole of k-space. With this two-stage approach, each reconstruction inherently incorporates a lower data under-sampling rate and more homogeneous signal magnitudes than conventional approaches. Because the restricted isometric property is easier to satisfy, the reconstruction consequently produces lower residual errors at each step. Compared with a conventional CS reconstruction, for the cases of cardiac cine, brain and angiogram imaging, the proposed method achieves a more accurate reconstruction with an improvement of 2-4 dB in peak signal-to-noise ratio respectively, using reduction factors of up to 6.

High-resolution intracranial vessel wall imaging: imaging beyond the lumen

“…7 Thus a full protocol, including time-of-flight MR angiography (TOF MRA) for localisation and PD VISTA pre and post contrast can be performed in under 30 min. As MRI scanners improve in efficiency and field strength, coil elements improve and compressed sensing techniques become more efficient, sequences will become shortened and allow for improved coverage 26 (refer to table 1 for the IVW MR protocol used at our institution).…”

Exponential Wavelet Iterative Shrinkage Thresholding Algorithm for compressed sensing magnetic resonance imaging

“…Balavoine et al [1] studied the capacity of the standard ISTA to perform this task in real-time. Konar et al [22] [46] proposed a novel, two-stage reconstruction scheme, which took advantage of the properties of k-space data and under-sampling patterns that were useful in CS. Lingala et al [24] proposed a novel deformation corrected compressed sensing (DC-CS) framework to recover contrast enhanced dynamic magnetic resonance images from undersampled measurements.…”

Fast Compressed Sensing MRI Based on Complex Double-Density Dual-Tree Discrete Wavelet Transform

“…Besides, the substantial aliasing artifacts appear incoherent. In case that the sampling ratio is extremely low, it is almost impossible to remove the significant aliasing artifacts from real signals [6, 8, 9]. For the second approach, it is essential to find a suitable sparsifying transform to recover images from highly undersampling k -space data.…”