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IEEE Trans Med Imaging. 2010 Mar;29(3):950-9. doi: 10.1109/TMI.2010.2042299.

Auto-calibrated parallel imaging reconstruction for arbitrary trajectories using k-space sparse matrices (kSPA).

Author information

  • 1Brain Imaging and Analysis Center, School of Medicine, Duke University, Durham, NC 27705, USA. chunlei.liu@duke.edu

Abstract

Image acquisition of magnetic resonance imaging (MRI) can be accelerated by using multiple receiving coils simultaneously. The problem of reconstructing an unaliased image from partially sampled k-space data can be formulated as a large system of sparse linear equations. The k-space sparse matrix (kSPA) algorithm proposes to solve the system of equations by finding a sparse approximate inverse. This algorithm has been shown to accelerate the image reconstruction for a large number of coils. The original kSPA algorithm requires knowledge of coil sensitivities. Here, we propose and demonstrate an auto-calibrated kSPA algorithm that does not require the explicit computation of the coil sensitivity maps. We have also shown that calibration data, in principle, can be acquired at any region of k-space. This property applies to arbitrary sampling trajectories and all reconstruction algorithms based on k-space. In practice, because of its higher SNR, calibration data acquired at the center of k-space performed more favorably. Such auto-calibration can be advantageous in cases where an accurate sensitivity map is difficult to obtain.

PMID:
20199928
[PubMed - indexed for MEDLINE]
PMCID:
PMC2996098
Free PMC Article
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