Phase unwrapping with a rapid opensource minimum spanning tree algorithm (ROMEO)

Dymerska, Barbara; Eckstein, Korbinian; Bachratá, Beáta; Siow, Bernard; Trattnig, S.; Shmueli, Karin; Robinson, Simon

doi:10.1002/mrm.28563

Cited by 59 publications

(89 citation statements)

References 29 publications

(50 reference statements)

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“…For phase unwrapping, recent faster methods (Dymerska et al, 2021; Karsa and Shmueli, 2019) mean that this step can be performed sufficiently quickly and reliably that no longer poses an obstacle to phase imaging with EPI.…”

Section: Discussionmentioning

confidence: 99%

“…Phase wraps in raw phase images were removed by spatially unwrapping using ROMEO (Dymerska et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

“…This unsupervised, physiological data-free and automatic Physiological Regressor Estimation from Phase and mAgnItude sub-tR (PREPAIR) approach reveals both respiration and cardiac signals present in the fMRI time series for a wide range of TRs despite cardiac fluctuations being – considering the volume TR - critically sampled or undersampled. We adopt recently-developed approaches to coil combination (Eckstein et al, 2018) and unwrapping (Dymerska et al, 2021) combined with temporal reordering and slice filtering to derive physiological signals sampled at the slice repetition interval rather than the volume repetition interval. High-frequency fluctuations due to breathing motion and cardiac pulsatility are identified over nuisance signal variation.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised physiological noise correction of fMRI data using phase and magnitude information (PREPAIR)

Bancelin

Bachratá

Bollmann

et al. 2022

Preprint

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Of the sources of noise which affect BOLD fMRI, respiration and cardiac fluctuations are responsible for the largest part of the variance, particularly at high and ultra-high field. Existing approaches to removing physiological noise either use external recordings, which can be unwieldy and unreliable, or attempt to identify physiological noise from the magnitude fMRI data. Data-driven approaches are limited by sensitivity, temporal aliasing and the need for user interaction. In the light of the sensitivity of the phase of the MR signal to local changes in the field stemming from physiological processes, we have developed an unsupervised physiological noise correction method which uses the information carried in both the phase and the magnitude of EPI data. Our technique, Physiological Regressor Estimation from Phase and mAgnItude, sub-tR (PREPAIR) derives time series signals which are sampled at the slice TR from both phase and magnitude images. It allows physiological noise to be captured without aliasing, and efficiently removes other sources of signal fluctuations which are not related to physiology, prior to regressor estimation. We demonstrate that the physiological signal time courses identified with PREPAIR not only agree well with those from external devices, but also retrieve challenging cardiac dynamics. The removal of physiological noise was as effective as that achieved with the most commonly used approach based on external recordings, RETROICOR. In comparison with widely used physiological noise correction tools which do not use external signals, PESTICA and FIX, PREPAIR removed more respiratory and cardiac noise and achieved a larger increase in tSNR at both 3 T and 7 T.

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Section: Discussionmentioning

confidence: 99%

“…Phase wraps in raw phase images were removed by spatially unwrapping using ROMEO (Dymerska et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Unsupervised physiological noise correction of fMRI data using phase and magnitude information (PREPAIR)

Bancelin

Bachratá

Bollmann

et al. 2022

Preprint

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“…29 , 62 , 63 Phase unwrapping was performed using ROMEO. 57 , 58 Background-field correction was performed with PDF 64 and susceptibility calculation with STAR 65 using the Sepia toolbox. 66 …”

Section: Methodsmentioning

confidence: 99%

Quantitative susceptibility mapping of the head‐and‐neck using SMURF fat‐water imaging with chemical shift and relaxation rate corrections

Bachratá

Trattnig

Robinson

2021

Magnetic Resonance in Med

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“…8,9 The implementation of region-growing methods is straightforward and simple but vulnerable to noise. 10 Quality-guided path-based region growing methods 11,12 have been proposed to reduce the error caused by noise. Global optimization methods are insensitive to local noise but computationally intensive.…”

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confidence: 99%

The PHU‐NET: A robust phase unwrapping method for MRI based on deep learning

Cheng

Peng

Liang

et al. 2021

Magnetic Resonance in Med

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The phase derived from MR complex signals is directly dependent on the magnetic field and, thus, has its own physical and physiological relevance. Accurate estimation of phase information is fundamental to several quantitative MR applications, such as chemical-shift encoded imaging, 1 quantitative susceptibility mapping, 2,3 and temperature imaging. 4 However, the MR phase, extracted from complex data, wraps its value into 2π ranges, making the phase images incapable

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Phase unwrapping with a rapid opensource minimum spanning tree algorithm (ROMEO)

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 59 publications

References 29 publications

Unsupervised physiological noise correction of fMRI data using phase and magnitude information (PREPAIR)

Unsupervised physiological noise correction of fMRI data using phase and magnitude information (PREPAIR)

Quantitative susceptibility mapping of the head‐and‐neck using SMURF fat‐water imaging with chemical shift and relaxation rate corrections

The PHU‐NET: A robust phase unwrapping method for MRI based on deep learning

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