A novel phase-unwrapping method based on pixel clustering and local surface fitting with application to Dixon water–fat MRI
Junying Cheng
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
Search for more papers by this authorYingjie Mei
Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
Search for more papers by this authorBiaoshui Liu
Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
Search for more papers by this authorJijing Guan
Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
Search for more papers by this authorXiaoyun Liu
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
Search for more papers by this authorEd X. Wu
Laboratory of Biomedical Imaging and Signal Processing, the University of Hong Kong, Hong Kong SAR, China
Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong SAR, China
Search for more papers by this authorQianjin Feng
Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
Search for more papers by this authorCorresponding Author
Wufan Chen
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
Correspondence to: Yanqiu Feng, Ph.D., School of Biomedical Engineering, Southern Medical University, No. 1023 Shatai Nan Rd, Guangzhou, China 510515. Tel: + 86 20 6164 8294; Fax: + 86 20 6164 8274; E-mail: [email protected].Search for more papers by this authorCorresponding Author
Yanqiu Feng
Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
Correspondence to: Yanqiu Feng, Ph.D., School of Biomedical Engineering, Southern Medical University, No. 1023 Shatai Nan Rd, Guangzhou, China 510515. Tel: + 86 20 6164 8294; Fax: + 86 20 6164 8274; E-mail: [email protected].Search for more papers by this authorJunying Cheng
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
Search for more papers by this authorYingjie Mei
Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
Search for more papers by this authorBiaoshui Liu
Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
Search for more papers by this authorJijing Guan
Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
Search for more papers by this authorXiaoyun Liu
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
Search for more papers by this authorEd X. Wu
Laboratory of Biomedical Imaging and Signal Processing, the University of Hong Kong, Hong Kong SAR, China
Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong SAR, China
Search for more papers by this authorQianjin Feng
Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
Search for more papers by this authorCorresponding Author
Wufan Chen
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
Correspondence to: Yanqiu Feng, Ph.D., School of Biomedical Engineering, Southern Medical University, No. 1023 Shatai Nan Rd, Guangzhou, China 510515. Tel: + 86 20 6164 8294; Fax: + 86 20 6164 8274; E-mail: [email protected].Search for more papers by this authorCorresponding Author
Yanqiu Feng
Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
Correspondence to: Yanqiu Feng, Ph.D., School of Biomedical Engineering, Southern Medical University, No. 1023 Shatai Nan Rd, Guangzhou, China 510515. Tel: + 86 20 6164 8294; Fax: + 86 20 6164 8274; E-mail: [email protected].Search for more papers by this authorCorrection added after online publication 03 April 2017. The authors updated the model of the MR Scanner from “XGR-OPER” to “XGY-OPER” in the In Vivo Data Acquisition section.
Abstract
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.
Supporting Information
Additional supporting information may be found in the online version of this article
| Filename | Description |
|---|---|
| mrm26647-sup-0001-suppinfo.docx8 MB | Figs. S1–S25. Phase-unwrapping and water–fat separation results of the 1st through 25th slices in the 3T knee data using PRELUDE and CLOSE. Arrows indicate the locations where the two methods produced different results. |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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