Phase unwrapping with graph cuts optimization and dual decomposition acceleration for 3D high‐resolution <scp>MRI</scp> data

Dong, Jianwu; Chen, Feng; Zhou, Dong; Liu, Tian; Yu, Zhaofei; Wang, Yi

doi:10.1002/mrm.26174

Cited by 33 publications

(32 citation statements)

References 19 publications

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“…Further improvement will be considered to tackle this issue, such as by incorporating global minimization. 18,26 Another limitation of the current implementation of SPUN is that it simply unwraps a voxel by reducing its absolute phase difference with the seed voxel to be less than π. Although efficient and generally competent, it will fail in certain scenarios such as phase poles 27 or the so-called CUSP artifacts due to suboptimal multichannel combination.…”

Section: Discussionmentioning

confidence: 99%

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Seed prioritized unwrapping (SPUN) for MR phase imaging

Ye¹,

Zhou

Zong

et al. 2018

Magnetic Resonance Imaging

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Background Region‐growing‐based phase unwrapping methods have the potential for lossless phase aliasing removal, but generally suffer from unwrapping error propagation associated with discontinuous phase and/or long calculation times. The tradeoff point between robustness and efficiency of phase unwrapping methods in the region‐growing category requires improvement. Purpose To demonstrate an accurate, robust, and efficient region‐growing phase unwrapping method for MR phase imaging applications. Study Type Prospective. Subjects, Phantom normal human subjects (10) / brain surgery patients (2) / water phantoms / computer simulation. Field Strength/Sequence 3 T/gradient echo sequences (2D and 3D). Assessment A seed prioritized unwrapping (SPUN) method was developed based on single‐region growing, prioritizing only a portion (eg, 100 seeds or 1% seeds) of available seed voxels based on continuity quality during each region‐growing iteration. Computer simulation, phantom, and in vivo brain and pelvis scans were performed. The error rates, seed percentages, and calculation times were recorded and reported. SPUN unwrapped phase images were visually evaluated and compared with Laplacian unwrapped results. Statistical Tests Monte Carlo simulation was performed on a 3D dipole phase model with a signal‐to‐noise ratio (SNR) of 1–9 dB, to obtain the mean and standard deviation of calculation error rates and calculation times. Results Simulation revealed a very robust unwrapping performance of SPUN, reaching an error rate of <0.4% even with SNR as low as 1 dB. For all in vivo data, SPUN was able to robustly unwrap the phase images of modest SNR and complex morphology with visually minimal errors and fast calculation speed (eg, <4 min for 368 × 312 × 128 data) when using a proper seed priority number, eg, Nsp = 1 or 10 voxels for 2D and Nsp = 1% for 3D data. Data Conclusion SPUN offers very robust and fast region‐growing‐based phase unwrapping, and does not require any tissue masking or segmentation, nor poses a limitation over imaging parameters. Level of Evidence: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:62–70.

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Section: Discussionmentioning

confidence: 99%

“…14,15 Global optimization methods remove phase aliasing by proposing models for the true phase map and performing optimization calculations. However, optimization performance depends on the specific model [16][17][18] or weighting function, 19 and may not provide a general solution over different scenarios.…”

mentioning

confidence: 99%

Seed prioritized unwrapping (SPUN) for MR phase imaging

Ye¹,

Zhou

Zong

et al. 2018

Magnetic Resonance Imaging

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“…For the third algorithm, a Laplacian unwrapping algorithm from the morphology enabled dipole inversion (MEDI) QSM toolbox was used and modified in‐house to resemble the algorithm introduced by Schofield et al (L‐Schofield‐Rounding). Additionally, a quality‐guided region‐growing method as proposed by Cusack et al (QG‐Cusack) and a graph‐cuts algorithm described by Dong et al (GC‐Dong) were selected from the MEDI toolbox as well. The last algorithm is a quality‐guided region‐growing approach, developed by Fortier and Levesque, showing promising results for regions with high‐susceptibility jumps in their whole‐head phantom (QG‐Fortier).…”

Section: Methodsmentioning

confidence: 99%

“…Energy minimization problem, solved by jump-move optimization 24 Quality-Guided Region Growing-Cusack (QG-Cusack)…”

Section: Laplacianmentioning

confidence: 99%

Analysis of different phase unwrapping methods to optimize quantitative susceptibility mapping in the abdomen

Bechler

Stabinska

Wittsack

2019

Magnetic Resonance in Med

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Purpose Quantitative susceptibility mapping (QSM) is heavily impacted by phase processing of gradient echo imaging data. So far, phase unwrapping algorithms have mostly been developed and tested for neuroimaging applications. In this work, a numerical human abdomen phantom was created and used to assess the feasibility of different phase unwrapping algorithms in abdominal QSM. Furthermore, in vivo data were acquired to evaluate consistency with the simulations. Methods Laplacian‐based, quality‐guided region growing and graph‐cuts unwrapping techniques were evaluated using the numerical phantom as well as an in vivo measurement. As a quality metric, root mean square error (RMSE) was calculated in order to analyze the performance of the examined unwrapping algorithms. Subsequently, susceptibility maps were generated from the resulting phase maps and compared to the ground truth. The evaluation was carried out on the whole phantom as well as individual organs. Results Graph‐cuts led to the most accurate and robust results among the investigated unwrapping methods. The other algorithms showed severe errors in regions with large susceptibility changes (i.e., around the lungs). Deviations from the ground‐truth susceptibility were higher than in the previous brain simulations for all tested algorithms. Conclusion Graph‐cuts–based unwrapping algorithms should be preferred in QSM studies in the human abdomen, where large susceptibility changes occur. For further improvement of QSM studies, unwrapping algorithms should be optimized for abdominal applications.

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“…The phase calculated from the complex MR data is generally wrapped into the range of (Àp, p], and phase unwrapping is usually required to recover the underlying true phase. A large number of phaseunwrapping algorithms have been proposed (2,(10)(11)(12)(13)(14). Phase-unwrapping methods are generally based on the assumption that the underlying true phase is smooth and the phase difference between adjacent pixels is less than p. If this assumption is satisfied, then the true phase map can be easily obtained (10).…”

Section: Introductionmentioning

confidence: 99%

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

Cheng

Mei

Liu

et al. 2017

Magnetic Resonance in Med

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Purpose: To develop and evaluate a novel 2D phaseunwrapping 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 phaseunwrapping 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 waterfat 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 p, 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,

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Phase unwrapping with graph cuts optimization and dual decomposition acceleration for 3D high‐resolution MRI data

Abstract: Dual decomposition significantly improves the computational efficiency of 3D graph cut-based phase unwrapping algorithms. Magn Reson Med 77:1353-1358, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Cited by 33 publications

References 19 publications

Seed prioritized unwrapping (SPUN) for MR phase imaging

Seed prioritized unwrapping (SPUN) for MR phase imaging

Analysis of different phase unwrapping methods to optimize quantitative susceptibility mapping in the abdomen

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

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