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Performance Analysis for Magnetic Resonance Imaging With Nonlinear Encoding Fields
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DOI：10.1109/TMI.2011.2169969.png)
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Nonlinear spatial encoding fields for magnetic resonance imaging (MRI) hold great promise to improve on the linear gradient approaches by, for example, enabling reduced imaging times. Imaging schemes that employ general nonlinear encoding fields are difficult to analyze using traditional measures. In particular, the resolution is spatially varying, characterized by a position-dependent point spread function (PSF). Likewise, the use of nonlinear encoding fields creates an additional spatial dependence on the signal-to-noise ratio (SNR). Although the two properties of resolution and SNR are linked, in this work we focus on the latter. To this end, we examine the pixel variance, which requires a computation that is often not feasible for nonlinear encoding schemes. This paper presents a general formulation for the performance analysis of imaging schemes using arbitrary encoding fields. The analysis leads to the derivation of a practical and computationally efficient performance metric, which is demonstrated through simulation examples.
Keyword:
Frame theory
nonlinear encoding fields
parallel imaging
signal-to-noise ratio (SNR)
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Reconstruction of MRI Data Encoded With Arbitrarily Shaped, Curvilinear, Nonbijective Magnetic Fields用任意形状，曲线，非双射磁场编码的MRI数据的重建
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