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Correction of vibration artifacts in DTI using phase-encoding reversal (COVIPER).

Mohammadi S, Nagy Z, Hutton C, Josephs O, Weiskopf N - Magn Reson Med (2011)

Bottom Line: We refined the model of vibration-induced echo shifts, showing that asymmetric k-space coverage in widely used Partial Fourier acquisitions results in locally differing signal loss in images acquired with reversed phase encoding direction (blip-up/blip-down).COVIPER was validated against low vibration reference data, resulting in an error reduction of about 72% in fractional anisotropy maps.COVIPER can be combined with other corrections based on phase encoding reversal, providing a comprehensive correction for eddy currents, susceptibility-related distortions and vibration artifact reduction.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, United Kingdom. siawoosh.mohammadi@ucl.ac.uk

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Quantification of the bias in FA for subjects S1–S3 using the root-mean-square FA-difference between affected (DTI1±) and reference (DTI2±) data within a region-of-interest based on: (a) the original data sets (ΔFAbias, Eq. 6a), (b) their arithmetic-mean combination (ΔFAmean, Eq. 6b), and (c) and their weighted-sum combination (ΔFAW, Eq. 6b). Furthermore, miscellaneous effects of the proposed correction method (ΔFAmisc) were assessed using the root-mean-square FA difference between the arithmetic mean and weighted-sum combination of the reference data (Eq. 6c) containing negligible vibration artifacts. The region-of-interest was constructed based on the rms(ε) maps of the affected blip-up and blip-down data (DTI1+ and DTI1−). The subject-averaged FA differences (dashed lines) were = 0.35 for the original data, = 0.18 for the arithmetic-mean data, = 0.1 for the weighted-sum data. Accordingly, the FA bias of the original data was reduced by 49% using the arithmetic-mean combination (ΔFAmean), COVIPER (ΔFAw) leads to an improvement of 72%, and the contribution of miscellaneous effects (ΔFAmisc) was 6%. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
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fig04: Quantification of the bias in FA for subjects S1–S3 using the root-mean-square FA-difference between affected (DTI1±) and reference (DTI2±) data within a region-of-interest based on: (a) the original data sets (ΔFAbias, Eq. 6a), (b) their arithmetic-mean combination (ΔFAmean, Eq. 6b), and (c) and their weighted-sum combination (ΔFAW, Eq. 6b). Furthermore, miscellaneous effects of the proposed correction method (ΔFAmisc) were assessed using the root-mean-square FA difference between the arithmetic mean and weighted-sum combination of the reference data (Eq. 6c) containing negligible vibration artifacts. The region-of-interest was constructed based on the rms(ε) maps of the affected blip-up and blip-down data (DTI1+ and DTI1−). The subject-averaged FA differences (dashed lines) were = 0.35 for the original data, = 0.18 for the arithmetic-mean data, = 0.1 for the weighted-sum data. Accordingly, the FA bias of the original data was reduced by 49% using the arithmetic-mean combination (ΔFAmean), COVIPER (ΔFAw) leads to an improvement of 72%, and the contribution of miscellaneous effects (ΔFAmisc) was 6%. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

Mentions: Figure 3 shows the FA and the root-mean-square of the error of the tensor fit, and Fig 4 shows the quantified bias in FA. For dataset DTI±1 the vibration-induced bias in FA was visible in at least one dataset (blip-up or blip-down) of each subject (Fig 3a,b, arrows), while the extent of the bias varied between individuals ( = 0.38; = 0.29; = 0.39; Fig 4). The artifact manifested itself in different regions for the blip-up (Fig 3a, yellow arrows) relative to the blip-down data (Fig 3b, red arrows). Averaged over subjects the standard arithmetic mean combination of blip-up and blip-down data reduced the vibration-induced bias in FA by 49% (from = 0.35 to = 0.18, Fig 4). In contrast, the proposed COVIPER correction based on weighted-sum combination of blip-up and blip-down data reduced the error in FA by 72% = 0.1) and the resulting maps showed better correspondence to the reference FA maps (Fig 3d,e). The contribution of miscellaneous effects that could not be attributed to vibration effects in the COVIPER method was about 6% (Fig 4). Note that the arithmetic-mean combination affects the tensor estimate for all regions showing vibration artifacts in the original blip-up or blip-down data, i.e., the union of affected regions in the blip-up and blip-down data. As a result the arithmetic-mean combination may show more widely spread artifacts (see regions highlighted by yellow and red arrows in Fig 3a–c), than any of the two original datasets (although usually at a lower level).


Correction of vibration artifacts in DTI using phase-encoding reversal (COVIPER).

Mohammadi S, Nagy Z, Hutton C, Josephs O, Weiskopf N - Magn Reson Med (2011)

Quantification of the bias in FA for subjects S1–S3 using the root-mean-square FA-difference between affected (DTI1±) and reference (DTI2±) data within a region-of-interest based on: (a) the original data sets (ΔFAbias, Eq. 6a), (b) their arithmetic-mean combination (ΔFAmean, Eq. 6b), and (c) and their weighted-sum combination (ΔFAW, Eq. 6b). Furthermore, miscellaneous effects of the proposed correction method (ΔFAmisc) were assessed using the root-mean-square FA difference between the arithmetic mean and weighted-sum combination of the reference data (Eq. 6c) containing negligible vibration artifacts. The region-of-interest was constructed based on the rms(ε) maps of the affected blip-up and blip-down data (DTI1+ and DTI1−). The subject-averaged FA differences (dashed lines) were = 0.35 for the original data, = 0.18 for the arithmetic-mean data, = 0.1 for the weighted-sum data. Accordingly, the FA bias of the original data was reduced by 49% using the arithmetic-mean combination (ΔFAmean), COVIPER (ΔFAw) leads to an improvement of 72%, and the contribution of miscellaneous effects (ΔFAmisc) was 6%. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
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fig04: Quantification of the bias in FA for subjects S1–S3 using the root-mean-square FA-difference between affected (DTI1±) and reference (DTI2±) data within a region-of-interest based on: (a) the original data sets (ΔFAbias, Eq. 6a), (b) their arithmetic-mean combination (ΔFAmean, Eq. 6b), and (c) and their weighted-sum combination (ΔFAW, Eq. 6b). Furthermore, miscellaneous effects of the proposed correction method (ΔFAmisc) were assessed using the root-mean-square FA difference between the arithmetic mean and weighted-sum combination of the reference data (Eq. 6c) containing negligible vibration artifacts. The region-of-interest was constructed based on the rms(ε) maps of the affected blip-up and blip-down data (DTI1+ and DTI1−). The subject-averaged FA differences (dashed lines) were = 0.35 for the original data, = 0.18 for the arithmetic-mean data, = 0.1 for the weighted-sum data. Accordingly, the FA bias of the original data was reduced by 49% using the arithmetic-mean combination (ΔFAmean), COVIPER (ΔFAw) leads to an improvement of 72%, and the contribution of miscellaneous effects (ΔFAmisc) was 6%. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
Mentions: Figure 3 shows the FA and the root-mean-square of the error of the tensor fit, and Fig 4 shows the quantified bias in FA. For dataset DTI±1 the vibration-induced bias in FA was visible in at least one dataset (blip-up or blip-down) of each subject (Fig 3a,b, arrows), while the extent of the bias varied between individuals ( = 0.38; = 0.29; = 0.39; Fig 4). The artifact manifested itself in different regions for the blip-up (Fig 3a, yellow arrows) relative to the blip-down data (Fig 3b, red arrows). Averaged over subjects the standard arithmetic mean combination of blip-up and blip-down data reduced the vibration-induced bias in FA by 49% (from = 0.35 to = 0.18, Fig 4). In contrast, the proposed COVIPER correction based on weighted-sum combination of blip-up and blip-down data reduced the error in FA by 72% = 0.1) and the resulting maps showed better correspondence to the reference FA maps (Fig 3d,e). The contribution of miscellaneous effects that could not be attributed to vibration effects in the COVIPER method was about 6% (Fig 4). Note that the arithmetic-mean combination affects the tensor estimate for all regions showing vibration artifacts in the original blip-up or blip-down data, i.e., the union of affected regions in the blip-up and blip-down data. As a result the arithmetic-mean combination may show more widely spread artifacts (see regions highlighted by yellow and red arrows in Fig 3a–c), than any of the two original datasets (although usually at a lower level).

Bottom Line: We refined the model of vibration-induced echo shifts, showing that asymmetric k-space coverage in widely used Partial Fourier acquisitions results in locally differing signal loss in images acquired with reversed phase encoding direction (blip-up/blip-down).COVIPER was validated against low vibration reference data, resulting in an error reduction of about 72% in fractional anisotropy maps.COVIPER can be combined with other corrections based on phase encoding reversal, providing a comprehensive correction for eddy currents, susceptibility-related distortions and vibration artifact reduction.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, United Kingdom. siawoosh.mohammadi@ucl.ac.uk

Show MeSH
Related in: MedlinePlus