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Robust Image Reconstruction using Multi-channel Spatial Nulling Maps: An Alternative to ESPIRiT?
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We develop a novel parallel imaging reconstruction method by extracting null-subspace bases of calibration data/matrix to calculate image-domain spatial nulling maps that contain both coil sensitivity and finite image support information. Images are reconstructed by solving a nulling system formed by multi-channel spatial nulling maps without any masking-related procedure (i.e., in existing SENSE/ESPIRiT for minimizing noise propagation). We demonstrate this method with 2D brain, knee and cardiac data under various conditions, yielding results highly comparable to ESPIRiT with optimal manual masking. Our proposed hybrid-domain reconstruction method is efficient, and more robust than existing ESPIRiT for parallel imaging in practice.
Title: Robust Image Reconstruction using Multi-channel Spatial Nulling Maps: An Alternative to ESPIRiT?
Description:
We develop a novel parallel imaging reconstruction method by extracting null-subspace bases of calibration data/matrix to calculate image-domain spatial nulling maps that contain both coil sensitivity and finite image support information.
Images are reconstructed by solving a nulling system formed by multi-channel spatial nulling maps without any masking-related procedure (i.
e.
, in existing SENSE/ESPIRiT for minimizing noise propagation).
We demonstrate this method with 2D brain, knee and cardiac data under various conditions, yielding results highly comparable to ESPIRiT with optimal manual masking.
Our proposed hybrid-domain reconstruction method is efficient, and more robust than existing ESPIRiT for parallel imaging in practice.
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