Premium
Seed prioritized unwrapping (SPUN) for MR phase imaging
Author(s) -
Ye Yongquan,
Zhou Fei,
Zong Jinguang,
Lyu Jingyuan,
Chen Yanling,
Zhang Shuheng,
Zhang Weiguo,
He Qiang,
Li Xueping,
Li Ming,
Zhang Qinglei,
Qing Zhao,
Zhang Bing
Publication year - 2019
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.26606
Subject(s) - imaging phantom , computer science , image quality , monte carlo method , algorithm , mean squared error , phase (matter) , robustness (evolution) , voxel , artificial intelligence , mathematics , optics , physics , statistics , image (mathematics) , biochemistry , chemistry , quantum mechanics , gene
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, N sp = 1 or 10 voxels for 2D and N sp = 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.