Block-Matching Distortion Correction of Echo-Planar Images With Opposite Phase Encoding Directions

Hédouin, Renaud; Commowick, Olivier; Bannier, Élise; Scherrer, Benoît; Taquet, Maxime; Warfield, Simon K.; Barillot, Christian

doi:10.1109/tmi.2016.2646920

Cited by 31 publications

(37 citation statements)

References 30 publications

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“…For FMB and MPB+MPB/F methods, we used the implementations most commonly used by the research community. We note that there are MPB methods [ 35 ] and MPB/F methods [ 34 ] that report better results than the method used in this work, TOPUP, and a comparison of these promising techniques may be the subject of future work. There are a number of available RB implementations, with no single one of them being clearly more popular than the others, each with a large number of parameters and settings that can be optimised, but we do not believe the choice of specific implementation will change our conclusions.…”

Section: Discussionmentioning

confidence: 99%

“…The second type estimates a map of the B 0 inhomogeneities from acquired gradient-echo scans, and uses this along with some information about the diffusion acquisition protocol to correct for the distortions [ 24 – 28 ]. The third estimates the underlying distortions using additional EPI data that is acquired with different phase-encoding (PE) and thus contains different distortions [ 29 – 35 ]. This last class of techniques offer the additional opportunity to accurately recover lost signal information if the full dataset has been acquired with reversed phase-encoding.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative assessment of the susceptibility artefact and its interaction with motion in diffusion MRI

et al. 2017

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In this paper we evaluate the three main methods for correcting the susceptibility-induced artefact in diffusion-weighted magnetic-resonance (DW-MR) data, and assess how correction is affected by the susceptibility field’s interaction with motion. The susceptibility artefact adversely impacts analysis performed on the data and is typically corrected in post-processing. Correction strategies involve either registration to a structural image, the application of an acquired field-map or the use of additional images acquired with different phase-encoding. Unfortunately, the choice of which method to use is made difficult by the absence of any systematic comparisons of them. In this work we quantitatively evaluate these methods, by extending and employing a recently proposed framework that allows for the simulation of realistic DW-MR datasets with artefacts. Our analysis separately evaluates the ability for methods to correct for geometric distortions and to recover lost information in regions of signal compression. In terms of geometric distortions, we find that registration-based methods offer the poorest correction. Field-mapping techniques are better, but are influenced by noise and partial volume effects, whilst multiple phase-encode methods performed best. We use our simulations to validate a popular surrogate metric of correction quality, the comparison of corrected data acquired with AP and LR phase-encoding, and apply this surrogate to real datasets. Furthermore, we demonstrate that failing to account for the interaction of the susceptibility field with head movement leads to increased errors when analysing DW-MR data. None of the commonly used post-processing methods account for this interaction, and we suggest this may be a valuable area for future methods development.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative assessment of the susceptibility artefact and its interaction with motion in diffusion MRI

et al. 2017

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“…There are many software packages providing phase encoding based tools for correcting susceptibility distortions, e.g., animaDistortionCorrection (aDC) (Voss et al, 2006), animaBMDistortionCorrection (aBMDC) (Hedouin et al, 2017), DR-BUDDI (Irfanoglu et al, 2015), EPIC (Holland et al, 2010), HySCO (Ruthotto et al, 2013) and TOPUP (Andersson et al, 2003), summarized in Table 1. To date, there is no systematic comparison of existing phase encoding based methods for susceptibility distortion correction.…”

Section: Introductionmentioning

confidence: 99%

“…The lack of ground truth means that evaluations are typically indirect or qualitative (Jezzard and Balaban, 1995;Wu et al, 2008;Bhushan et al, 2012;Ruthotto et al, 2013;Fritz et al, 2014;Irfanoglu et al, 2015;Taylor et al, 2016;Hedouin et al, 2017;Wang et al, 2017;Irfanoglu et al, 2018). Only a few investigations have been carried out with the presence of a ground truth for evaluation of susceptibility distortion correction (Andersson et al, 2003;Esteban et al, 2014;Graham et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Only a few investigations have been carried out with the presence of a ground truth for evaluation of susceptibility distortion correction (Andersson et al, 2003;Esteban et al, 2014;Graham et al, 2017). Hedouin et al (2017) compared aDC, aBMDC and TOPUP using phantom data and human data. For the phantom data, both the aBMDC and TOPUP corrected images appear visually similar for correcting the DR-BUDDI resulted in the lowest variability between the two corrected images C AP+PA and C LR+RL , followed by TOPUP and then EPIC.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of six phase encoding based susceptibility distortion correction methods for diffusion MRI

Eklund

2019

Preprint

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ITEA3 / VINNOVA funded project "Intelligence based iMprovement of Personalized treatment And Clinical workflow supporT" (IMPACT) Purpose: Susceptibility distortion impacts diffusion MRI data analysis and is typically corrected during preprocessing. Correction strategies involve three classes of methods: registration to a structural image, the use of a fieldmap or the use of images acquired with opposing phase encoding directions. It has been demonstrated that phase encoding based methods outperform the other two classes but unfortunately, the choice of which phase encoding based method to use is still an open question due to the absence of any systematic comparisons. Methods:In this paper we quantitatively evaluated six popular phase encoding based methods for correcting susceptibility distortions in diffusion MRI data. We employed a framework that allows for the simulation of realistic diffusion MRI data with susceptibility distortions. We evaluated the ability for methods to correct distortions by comparing the corrected data with the ground truth. Four diffusion tensor metrics (FA, MD, eigenvalues and eigenvectors) were calculated from the corrected data and compared with the ground truth. We also validated two popular indirect metrics using both simulated data and real data. The two indirect metrics are the difference between the corrected LR and AP data, and the FA standard deviation over the corrected LR, RL, AP and PA data. 1 2 XUAN GU ET AL. Results:We found that TOPUP offered the most accurate and robust correction than the other five methods when evaluating using both direct and indirect metrics. EPIC and HySCO performed well in correcting b 0 images but produced poor corrections for diffusion weighted volumes and they produced large errors for the four diffusion tensor metrics.We also demonstrated that the indirect metric gave a different ordering of correction quality than the direct metric. Conclusion:We suggest researchers to use TOPUP for susceptibility distortion correction. We also suggest that indirect metrics must be interpreted cautiously when evaluating methods for correcting susceptibility distortions in diffusion MRI data.

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Retrospective Distortion and Motion Correction for Free‐Breathing DW‐MRI of the Kidneys Using Dual‐Echo EPI and Slice‐to‐Volume Registration

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Afacan

Hoge

et al. 2020

Magnetic Resonance Imaging

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Background Diffusion‐weighted MRI (DW‐MRI) of the kidneys is a technique that provides information about the microstructure of renal tissue without requiring exogenous contrasts such as gadolinium, and it can be used for diagnosis in cases of renal disease and assessing response‐to‐therapy. However, physiological motion and large geometric distortions due to main B0 field inhomogeneities degrade the image quality, reduce the accuracy of quantitative imaging markers, and impede their subsequent clinical applicability. Purpose To retrospectively correct for geometric distortion for free‐breathing DW‐MRI of the kidneys at 3T, in the presence of a nonstatic distortion field due to breathing and bulk motion. Study Type Prospective. Subjects Ten healthy volunteers (ages 29–38, four females). Field Strength/Sequence 3T; DW‐MR dual‐echo echo‐planar imaging (EPI) sequence (10 b‐values and 17 directions) and a T2 volume. Assessment The distortion correction was evaluated subjectively (Likert scale 0–5) and numerically with cross‐correlation between the DW images at b = 0 s/mm2 and a T2 volume. The intravoxel incoherent motion (IVIM) and diffusion tensor (DTI) model‐fitting performance was evaluated using the root‐mean‐squared error (nRMSE) and the coefficient of variation (CV%) of their parameters. Statistical Tests Statistical comparisons were done using Wilcoxon tests. Results The proposed method improved the Likert scores by 1.1 ± 0.8 (P < 0.05), the cross‐correlation with the T2 reference image by 0.13 ± 0.05 (P < 0.05), and reduced the nRMSE by 0.13 ± 0.03 (P < 0.05) and 0.23 ± 0.06 (P < 0.05) for IVIM and DTI, respectively. The CV% of the IVIM parameters (slow and fast diffusion, and diffusion fraction for IVIM and mean diffusivity, and fractional anisotropy for DTI) was reduced by 2.26 ± 3.98% (P = 6.971 × 10−2), 11.24 ± 26.26% (P = 6.971 × 10−2), 4.12 ± 12.91% (P = 0.101), 3.22 ± 0.55% (P < 0.05), and 2.42 ± 1.15% (P < 0.05). Data Conclusion The results indicate that the proposed Di + MoCo method can effectively correct for time‐varying geometric distortions and for misalignments due to breathing motion. Consequently, the image quality and precision of the DW‐MRI model parameters improved. Level of Evidence 2 Technical Efficacy Stage 1

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Block-Matching Distortion Correction of Echo-Planar Images With Opposite Phase Encoding Directions

Cited by 31 publications

References 30 publications

Quantitative assessment of the susceptibility artefact and its interaction with motion in diffusion MRI

Quantitative assessment of the susceptibility artefact and its interaction with motion in diffusion MRI

Evaluation of six phase encoding based susceptibility distortion correction methods for diffusion MRI

Retrospective Distortion and Motion Correction for Free‐Breathing DW‐MRI of the Kidneys Using Dual‐Echo EPI and Slice‐to‐Volume Registration

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