PURPOSE: The objective of this study was to develop background gradient correction method using excitation pulse profile compensation for accurate fat and T2* quantification in the liver. MATERIALS AND METHODS: In liver imaging using gradient echo, signal decay induced by linear background gradient is weighted by an excitation pulse profile and therefore hinders accurate quantification of T2* and fat. To correct this, a linear background gradient in the slice-selection direction was estimated from a B0 field map and signal decays were corrected using the excitation pulse profile. Improved estimation of fat fraction and T2* from the corrected data were demonstrated by phantom and in vivo experiments at 3 Tesla magnetic field. RESULTS: After correction, in the phantom experiments, the estimated T2* and fat fractions were changed close to that of a well-shimmed condition while, for in vivo experiments, the background gradients were estimated to be up to approximately 120 microT/m with increased homogeneity in T2* and fat fractions obtained. CONCLUSION: The background gradient correction method using excitation pulse profile can reduce the effect of macroscopic field inhomogeneity in signal decay and can be applied for simultaneous fat and iron quantification in 2D gradient echo liver imaging.
Compensation and Redress ; Iron ; Liver ; Magnetics ; Magnets

PURPOSE: The management of metal-induced field inhomogeneities is one of the major concerns of distortion-free magnetic resonance images near metallic implants. The recently proposed method called “Slice Encoding for Metal Artifact Correction (SEMAC)” is an effective spin echo pulse sequence of magnetic resonance imaging (MRI) near metallic implants. However, as SEMAC uses the noisy resolved data elements, SEMAC images can have a major problem for improving the signal-to-noise ratio (SNR) without compromising the correction of metal artifacts. To address that issue, this paper presents a novel reconstruction technique for providing an improvement of the SNR in SEMAC images without sacrificing the correction of metal artifacts. MATERIALS AND METHODS: Low-rank approximation in each coil image is first performed to suppress the noise in the slice direction, because the signal is highly correlated between SEMAC-encoded slices. Secondly, SEMAC images are reconstructed by the best linear unbiased estimator (BLUE), also known as Gauss-Markov or weighted least squares. Noise levels and correlation in the receiver channels are considered for the sake of SNR optimization. To this end, since distorted excitation profiles are sparse, l1 minimization performs well in recovering the sparse distorted excitation profiles and the sparse modeling of our approach offers excellent correction of metal-induced distortions. RESULTS: Three images reconstructed using SEMAC, SEMAC with the conventional two-step noise reduction, and the proposed image denoising for metal MRI exploiting sparsity and low rank approximation algorithm were compared. The proposed algorithm outperformed two methods and produced 119% SNR better than SEMAC and 89% SNR better than SEMAC with the conventional two-step noise reduction. CONCLUSION: We successfully demonstrated that the proposed, novel algorithm for SEMAC, if compared with conventional de-noising methods, substantially improves SNR and reduces artifacts.
Artifacts ; Least-Squares Analysis ; Magnetic Resonance Imaging* ; Methods ; Noise ; Signal-To-Noise Ratio