A novel phase-unwrapping method based on pixel clustering and local surface fitting with application to Dixon water-fat MRI.

PURPOSE: To develop and evaluate a novel 2D phase-unwrapping method that works robustly in the presence of severe noise, rapid phase changes, and disconnected regions. THEORY AND METHODS: The MR phase map usually varies rapidly in regions adjacent to wraps. In contrast, the phasors can vary slowly, especially in regions distant from tissue boundaries. Based on this observation, this paper develops a phase-unwrapping method by using a pixel clustering and local surface fitting (CLOSE) approach to exploit different local variation characteristics between the phase and phasor data. The CLOSE approach classifies pixels into easy-to-unwrap blocks and difficult-to-unwrap residual pixels first, and then sequentially performs intrablock, interblock, and residual-pixel phase unwrapping by a region-growing surface-fitting method. The CLOSE method was evaluated on simulation and in vivo water-fat Dixon data, and was compared with phase region expanding labeler for unwrapping discrete estimates (PRELUDE). RESULTS: In the simulation experiment, the mean error ratio by CLOSE was less than 1.50%, even in areas with signal-to-noise ratio equal to 0.5, phase changes larger than π, and disconnected regions. For 350 in vivo knee and ankle images, the water-fat swap ratio of CLOSE was 4.29%, whereas that of PRELUDE was 25.71%. CONCLUSIONS: The CLOSE approach can correctly unwrap phase with high robustness, and benefit MRI applications that require phase unwrapping. Magn Reson Med 79:515-528, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

A novel phase-unwrapping method by using phase-jump detection and local surface fitting: application to Dixon water-fat MRI.

PURPOSE: This study aims to develop an accurate and robust phase-unwrapping method that works effectively under severe noise, rapid-varying phase, and disconnected regions for water-fat Dixon MRI. METHODS: The proposed method first segments the phase map into blocks by automatically detecting phase jumps, and then clusters the pixels near phase jumps into residual pixels. Thereafter, the proposed method sequentially performs intrablock, interblock, and residual-pixel unwrapping using the local surface fitting approach. To address intrablock wraps, the proposed method segments each block into subblocks using the phase partition approach and then performs inter-subblock unwrapping using a block-growing approach. The phase derivative variance is used as the quality criterion to determine the region-growing path of residual pixels. The performance of the proposed method was evaluated on simulation and in vivo Dixon data. RESULTS: The proposed method obtained accurate phase-unwrapping results in the simulation experiment with severe noise, rapid-varying phase, and disconnected regions, and the mean and SD error ratio was 0.26 ± 0.07%. For 505 in vivo knee and ankle images, the total water-fat swap ratio by the proposed method was 1.78%, whereas those by phase region expanding labeler for unwrapping discrete estimates and clustering and local surface fitting were 38.42% and 7.72%, respectively. CONCLUSION: The proposed method achieves accurate and robust performance in phase unwrapping and can benefit phase-related MRI applications such as Dixon water-fat separation.

[Quantitative magnetic resonance imaging of brain iron deposition: comparison between quantitative susceptibility mapping and transverse relaxation rate (R2*) mapping].

OBJECTIVE: To evaluate the accuracy and sensitivity of quantitative susceptibility mapping (QSM) and transverse relaxation rate (R2*) mapping in the measurement of brain iron deposition. METHODS: Super paramagnetic iron oxide (SPIO) phantoms and mouse models of Parkinson's disease (PD) related to iron deposition in the substantia nigra (SN) underwent 7.0 T magnetic resonance (MR) scans (Bruker, 70/16) with a multi-echo 3D gradient echo sequence, and the acquired data were processed to obtain QSM and R2*. Linear regression analysis was performed for susceptibility and R2* in the SPIO phantoms containing 5 SPIO concentrations (30, 15, 7.5, 3.75 and 1.875 µg/mL) to evaluate the accuracy of QSM and R2* in quantitative iron analysis. The sensitivities of QSM and R2* mapping in quantitative detection of brain iron deposition were assessed using mouse models of PD induced by 1-methyl-4-phenyl-1,2,3,6-tetrahy-dropyridine (MPTP) in comparison with the control mice. RESULTS: In SPIO phantoms, QSM provided a higher accuracy than R2* mapping and their goodness-of-fit coefficients (R2) were 0.98 and 0.89, respectively. In the mouse models of PD and control mice, the susceptibility of the SN was significantly higher in the PD models (5.19∓1.58 vs 2.98∓0.88, n=5; P<0.05), while the R2* values were similar between the two groups (20.22∓0.94 vs 19.74∓1.75; P=0.60). CONCLUSION: QSM allows more accurate and sensitive detection of brain iron deposition than R2*, and the susceptibility derived by QSM can be a potentially useful biomarker for studying PD.

Morphology-adaptive total variation for the reconstruction of quantitative susceptibility map from the magnetic resonance imaging phase.

Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging technique that quantifies the magnetic susceptibility distribution within biological tissues. QSM calculates the underlying magnetic susceptibility by deconvolving the tissue magnetic field map with a unit dipole kernel. However, this deconvolution problem is ill-posed. The morphology enabled dipole inversion (MEDI) introduces total variation (TV) to regularize the susceptibility reconstruction. However, MEDI results still contain artifacts near tissue boundaries because MEDI only imposes TV constraint on voxels inside smooth regions. We introduce a Morphology-Adaptive TV (MATV) for improving TV-regularized QSM. The MATV method first classifies imaging target into smooth and nonsmooth regions by thresholding magnitude gradients. In the dipole inversion for QSM, the TV regularization weights are a monotonically decreasing function of magnitude gradients. Thus, voxels inside smooth regions are assigned with larger weights than those in nonsmooth regions. Using phantom and in vivo datasets, we compared the performance of MATV with that of MEDI. MATV results had better visual quality than MEDI results, especially near tissue boundaries. Preliminary brain imaging results illustrated that MATV has potential to improve the reconstruction of regions near tissue boundaries.