STEP: Self-supporting tailored k-space estimation for parallel imaging reconstruction

Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine
Zechen ZhouChun Yuan

Abstract

A new subspace-based iterative reconstruction method, termed Self-supporting Tailored k-space Estimation for Parallel imaging reconstruction (STEP), is presented and evaluated in comparison to the existing autocalibrating method SPIRiT and calibrationless method SAKE. In STEP, two tailored schemes including k-space partition and basis selection are proposed to promote spatially variant signal subspace and incorporated into a self-supporting structured low rank model to enforce properties of locality, sparsity, and rank deficiency, which can be formulated into a constrained optimization problem and solved by an iterative algorithm. Simulated and in vivo datasets were used to investigate the performance of STEP in terms of overall image quality and detail structure preservation. The advantage of STEP on image quality is demonstrated by retrospectively undersampled multichannel Cartesian data with various patterns. Compared with SPIRiT and SAKE, STEP can provide more accurate reconstruction images with less residual aliasing artifacts and reduced noise amplification in simulation and in vivo experiments. In addition, STEP has the capability of combining compressed sensing with arbitrary sampling trajectory. Using k-space partition...Continue Reading

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Citations

Dec 2, 2017·Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine·Jing ChengDong Liang

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