Paper
21 March 2016 An adaptive undersampling scheme of wavelet-encoded parallel MR imaging for more efficient MR data acquisition
Hua Xie, John C. Bosshard, Jason E. Hill, Steven M. Wright, Sunanda Mitra
Author Affiliations +
Abstract
Magnetic Resonance Imaging (MRI) offers noninvasive high resolution, high contrast cross-sectional anatomic images through the body. The data of the conventional MRI is collected in spatial frequency (Fourier) domain, also known as kspace. Because there is still a great need to improve temporal resolution of MRI, Compressed Sensing (CS) in MR imaging is proposed to exploit the sparsity of MR images showing great potential to reduce the scan time significantly, however, it poses its own unique problems. This paper revisits wavelet-encoded MR imaging which replaces phase encoding in conventional MRI data acquisition with wavelet encoding by applying wavelet-shaped spatially selective radiofrequency (RF) excitation, and keeps the readout direction as frequency encoding. The practicality of wavelet encoded MRI by itself is limited due to the SNR penalties and poor time resolution compared to conventional Fourier-based MRI. To compensate for those disadvantages, this paper first introduces an undersampling scheme named significance map for sparse wavelet-encoded k-space to speed up data acquisition as well as allowing for various adaptive imaging strategies. The proposed adaptive wavelet-encoded undersampling scheme does not require prior knowledge of the subject to be scanned. Multiband (MB) parallel imaging is also incorporated with wavelet-encoded MRI by exciting multiple regions simultaneously for further reduction in scan time desirable for medical applications. The simulation and experimental results are presented showing the feasibility of the proposed approach in further reduction of the redundancy of the wavelet k-space data while maintaining relatively high quality.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hua Xie, John C. Bosshard, Jason E. Hill, Steven M. Wright, and Sunanda Mitra "An adaptive undersampling scheme of wavelet-encoded parallel MR imaging for more efficient MR data acquisition", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97843U (21 March 2016); https://doi.org/10.1117/12.2217636
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Cited by 1 scholarly publication.
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KEYWORDS
Magnetic resonance imaging

Wavelets

Brain

Data acquisition

Image quality

Imaging systems

Temporal resolution

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