A field map updating algorithm to improve fat-water spiral imaging

Purpose: An accurate field map is essential to separate fat and water signals in a dual-echo chemical shift encoded spiral MRI scan. A rapid low-resolution B₀ map prescan is usually performed before each exam. Occasional inaccuracy in these field map estimates can lead to misclassification of the water and fat signals as well as blurring artifacts in the reconstruction. The present work proposes a self-consistent model to evaluate residual field offsets according to the image data to improve the reconstruction quality and facilitate the scan efficiency. Theory and Methods: The proposed method compares the phase differences of the two-echo data after correcting for fat frequency offsets. A more accurate field map is approximated according to the phase discrepancies and improved image quality. Experiments were conducted with simulated off-resonance on a numerical phantom, five volunteer head scans, and four volunteer abdominal scans for validation. Results: The initial reconstruction of the demonstrated examples exhibit blurring artifacts and misregistration of fat and water because of the inaccuracy of the field map. The proposed method updates the field map to amend the fat and water estimation and improve image quality. Conclusions: This work presents a model to improve the quality of fat-water imaging of the spiral MRI by estimating a better field map from the acquired data. It allows reducing the field map pre-scans before each spiral scan under normal circumstances to increase scan efficiency.