Parallel imaging reconstruction using spatial nulling maps

Abstract

PurposeTo develop a robust parallel imaging reconstruction method using spatial nulling maps (SNMs).MethodsParallel reconstruction using null operations (PRUNO) is a k‐space reconstruction method where a k‐space nulling system is derived using null‐subspace bases of the calibration matrix. ESPIRiT reconstruction extends the PRUNO subspace concept by exploiting the linear relationship between signal‐subspace bases and spatial coil sensitivity characteristics, yielding a hybrid‐domain approach. Yet it requires empirical eigenvalue thresholding to mask the coil sensitivity information and is sensitive to signal‐ and null‐subspace division. In this study, we combine the concepts of null‐subspace PRUNO and hybrid‐domain ESPIRiT to provide a more robust reconstruction method that extracts null‐subspace bases of calibration matrix to calculate image‐domain SNMs. Multi‐channel images are reconstructed by solving an image‐domain nulling system formed by SNMs that contain both coil sensitivity and finite image support information, therefore, circumventing the masking‐related procedure. The proposed method was evaluated with multi‐channel 2D brain and knee data and compared to ESPIRiT.ResultsThe proposed hybrid‐domain method produced quality reconstruction highly comparable to ESPIRiT with optimal manual masking. It involved no masking‐related manual procedure and was tolerant of the actual division of null‐ and signal‐subspace. Spatial regularization could be also readily incorporated to reduce noise amplification as in ESPIRiT.ConclusionWe provide an efficient hybrid‐domain reconstruction method using multi‐channel SNMs that are calculated from coil calibration data. It eliminates the need for coil sensitivity masking and is relatively insensitive to subspace separation, therefore, presenting a robust parallel imaging reconstruction procedure in practice.