Effects of gradient non-linearity correction and intensity non-uniformity correction in longitudinal studies using structural image evaluation using normalization of atrophy (SIENA)
Purpose: To evaluate the effects of gradient nonlinearity correction and intensity nonuniformity correction on longitudinal (two‐year) changes in global and regional brain volumes. Materials and Methods: A total of 208 subjects (70 females and 138 males, age range = 38.1–83.0 years) were included in...
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| Published in | Journal of magnetic resonance imaging Vol. 32; no. 2; pp. 489 - 492 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken
Wiley Subscription Services, Inc., A Wiley Company
01.08.2010
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1053-1807 1522-2586 1522-2586 |
| DOI | 10.1002/jmri.22237 |
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| Abstract | Purpose:
To evaluate the effects of gradient nonlinearity correction and intensity nonuniformity correction on longitudinal (two‐year) changes in global and regional brain volumes.
Materials and Methods:
A total of 208 subjects (70 females and 138 males, age range = 38.1–83.0 years) were included in this study. Each subject was scanned twice, at an interval of approximately two years (range = 1.5–2.3 years). Three‐dimensional fast spoiled‐gradient recalled acquisition in the steady state (3D‐FSPGR) images corrected for gradient nonlinearity and/or intensity nonuniformity were compared with uncorrected 3D‐FSPGR images with use of structural image evaluation using normalization of atrophy 2.6 (SIENA).
Results:
The mean absolute deviations of percentage brain volume change (PBVC) values in the gradient nonlinearity ± intensity nonuniformity corrected images were significantly less than that in the uncorrected images, and the difference in the mean absolute deviation of PBVC was the most significant between the uncorrected images and the images corrected for both gradient nonlinearity and intensity nonuniformity. Voxel‐wise comparisons showed large significant differences between the uncorrected images and the corrected images.
Conclusion:
Correction for gradient nonlinearity and intensity nonuniformity reduces the variance of measured longitudinal changes in brain volumes and will improve accuracy for detecting subtle brain changes. J. Magn. Reson. Imaging 2010;32:489–492. © 2010 Wiley‐Liss, Inc. |
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| AbstractList | To evaluate the effects of gradient nonlinearity correction and intensity nonuniformity correction on longitudinal (two-year) changes in global and regional brain volumes.PURPOSETo evaluate the effects of gradient nonlinearity correction and intensity nonuniformity correction on longitudinal (two-year) changes in global and regional brain volumes.A total of 208 subjects (70 females and 138 males, age range = 38.1-83.0 years) were included in this study. Each subject was scanned twice, at an interval of approximately two years (range = 1.5-2.3 years). Three-dimensional fast spoiled-gradient recalled acquisition in the steady state (3D-FSPGR) images corrected for gradient nonlinearity and/or intensity nonuniformity were compared with uncorrected 3D-FSPGR images with use of structural image evaluation using normalization of atrophy 2.6 (SIENA).MATERIALS AND METHODSA total of 208 subjects (70 females and 138 males, age range = 38.1-83.0 years) were included in this study. Each subject was scanned twice, at an interval of approximately two years (range = 1.5-2.3 years). Three-dimensional fast spoiled-gradient recalled acquisition in the steady state (3D-FSPGR) images corrected for gradient nonlinearity and/or intensity nonuniformity were compared with uncorrected 3D-FSPGR images with use of structural image evaluation using normalization of atrophy 2.6 (SIENA).The mean absolute deviations of percentage brain volume change (PBVC) values in the gradient nonlinearity +/- intensity nonuniformity corrected images were significantly less than that in the uncorrected images, and the difference in the mean absolute deviation of PBVC was the most significant between the uncorrected images and the images corrected for both gradient nonlinearity and intensity nonuniformity. Voxel-wise comparisons showed large significant differences between the uncorrected images and the corrected images.RESULTSThe mean absolute deviations of percentage brain volume change (PBVC) values in the gradient nonlinearity +/- intensity nonuniformity corrected images were significantly less than that in the uncorrected images, and the difference in the mean absolute deviation of PBVC was the most significant between the uncorrected images and the images corrected for both gradient nonlinearity and intensity nonuniformity. Voxel-wise comparisons showed large significant differences between the uncorrected images and the corrected images.Correction for gradient nonlinearity and intensity nonuniformity reduces the variance of measured longitudinal changes in brain volumes and will improve accuracy for detecting subtle brain changes.CONCLUSIONCorrection for gradient nonlinearity and intensity nonuniformity reduces the variance of measured longitudinal changes in brain volumes and will improve accuracy for detecting subtle brain changes. Purpose: To evaluate the effects of gradient nonlinearity correction and intensity nonuniformity correction on longitudinal (two‐year) changes in global and regional brain volumes. Materials and Methods: A total of 208 subjects (70 females and 138 males, age range = 38.1–83.0 years) were included in this study. Each subject was scanned twice, at an interval of approximately two years (range = 1.5–2.3 years). Three‐dimensional fast spoiled‐gradient recalled acquisition in the steady state (3D‐FSPGR) images corrected for gradient nonlinearity and/or intensity nonuniformity were compared with uncorrected 3D‐FSPGR images with use of structural image evaluation using normalization of atrophy 2.6 (SIENA). Results: The mean absolute deviations of percentage brain volume change (PBVC) values in the gradient nonlinearity ± intensity nonuniformity corrected images were significantly less than that in the uncorrected images, and the difference in the mean absolute deviation of PBVC was the most significant between the uncorrected images and the images corrected for both gradient nonlinearity and intensity nonuniformity. Voxel‐wise comparisons showed large significant differences between the uncorrected images and the corrected images. Conclusion: Correction for gradient nonlinearity and intensity nonuniformity reduces the variance of measured longitudinal changes in brain volumes and will improve accuracy for detecting subtle brain changes. J. Magn. Reson. Imaging 2010;32:489–492. © 2010 Wiley‐Liss, Inc. To evaluate the effects of gradient nonlinearity correction and intensity nonuniformity correction on longitudinal (two-year) changes in global and regional brain volumes. A total of 208 subjects (70 females and 138 males, age range = 38.1-83.0 years) were included in this study. Each subject was scanned twice, at an interval of approximately two years (range = 1.5-2.3 years). Three-dimensional fast spoiled-gradient recalled acquisition in the steady state (3D-FSPGR) images corrected for gradient nonlinearity and/or intensity nonuniformity were compared with uncorrected 3D-FSPGR images with use of structural image evaluation using normalization of atrophy 2.6 (SIENA). The mean absolute deviations of percentage brain volume change (PBVC) values in the gradient nonlinearity +/- intensity nonuniformity corrected images were significantly less than that in the uncorrected images, and the difference in the mean absolute deviation of PBVC was the most significant between the uncorrected images and the images corrected for both gradient nonlinearity and intensity nonuniformity. Voxel-wise comparisons showed large significant differences between the uncorrected images and the corrected images. Correction for gradient nonlinearity and intensity nonuniformity reduces the variance of measured longitudinal changes in brain volumes and will improve accuracy for detecting subtle brain changes. |
| Author | Takao, Hidemasa Hayashi, Naoto Ohtomo, Kuni Kabasawa, Hiroyuki Abe, Osamu |
| Author_xml | – sequence: 1 givenname: Hidemasa surname: Takao fullname: Takao, Hidemasa email: takaoh-tky@umin.ac.jp organization: Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan – sequence: 2 givenname: Osamu surname: Abe fullname: Abe, Osamu organization: Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan – sequence: 3 givenname: Naoto surname: Hayashi fullname: Hayashi, Naoto organization: Department of Computational Diagnostic Radiology and Preventive Medicine, Graduate School of Medicine, University of Tokyo, Tokyo, Japan – sequence: 4 givenname: Hiroyuki surname: Kabasawa fullname: Kabasawa, Hiroyuki organization: Japan Applied Science Laboratory, GE Healthcare Japan Corporation, Tokyo, Japan – sequence: 5 givenname: Kuni surname: Ohtomo fullname: Ohtomo, Kuni organization: Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/20677282$$D View this record in MEDLINE/PubMed |
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| References | Leow AD, Klunder AD, Jack CR Jr, et al. Longitudinal stability of MRI for mapping brain change using tensor-based morphometry. Neuroimage 2006; 31: 627-640. Jovicich J, Czanner S, Han X, et al. MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: Reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths. Neuroimage 2009; 46: 177-192. Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 2002; 17: 825-841. Gunter JL, Shiung MM, Manduca A, Jack CR Jr. Methodological considerations for measuring rates of brain atrophy. J Magn Reson Imaging 2003; 18: 16-24. Lewis EB, Fox NC. Correction of differential intensity inhomogeneity in longitudinal MR images. Neuroimage 2004; 23: 75-83. Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 2001; 20: 45-57. Nichols TE, Holmes AP. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp 2002; 15: 1-25. Acosta-Cabronero J, Williams GB, Pereira JM, Pengas G, Nestor PJ. The impact of skull-stripping and radio-frequency bias correction on grey-matter segmentation for voxel-based morphometry. Neuroimage 2008; 39: 1654-1665. Smith SM. Fast robust automated brain extraction. Hum Brain Mapp 2002; 17: 143-155. Jovicich J, Czanner S, Greve D, et al. Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data. Neuroimage 2006; 30: 436-443. Smith SM, Zhang Y, Jenkinson M, et al. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage 2002; 17: 479-489. Chard DT, Parker GJ, Griffin CM, Thompson AJ, Miller DH. The reproducibility and sensitivity of brain tissue volume measurements derived from an SPM-based segmentation methodology. J Magn Reson Imaging 2002; 15: 259-267. Preboske GM, Gunter JL, Ward CP, Jack CR Jr. Common MRI acquisition non-idealities significantly impact the output of the boundary shift integral method of measuring brain atrophy on serial MRI. Neuroimage 2006; 30: 1196-1202. Shuter B, Yeh IB, Graham S, Au C, Wang SC. Reproducibility of brain tissue volumes in longitudinal studies: effects of changes in signal-to-noise ratio and scanner software. Neuroimage 2008; 41: 371-379. Clark KA, Woods RP, Rottenberg DA, Toga AW, Mazziotta JC. Impact of acquisition protocols and processing streams on tissue segmentation of T1 weighted MR images. Neuroimage 2006; 29: 185-202. Zheng W, Chee MW, Zagorodnov V. Improvement of brain segmentation accuracy by optimizing non-uniformity correction using N3. Neuroimage 2009; 48: 73-83. Smith SM, Jenkinson M, Woolrich MW, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 2004; 23: S208-S219. Sled JG, Zijdenbos AP, Evans AC. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 1998; 17: 87-97. Vovk U, Pernus F, Likar B. A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans Med Imaging 2007; 26: 405-421. Boyes RG, Gunter JL, Frost C, et al. Intensity non-uniformity correction using N3 on 3-T scanners with multichannel phased array coils. Neuroimage 2008; 39: 1752-1762. 2002; 17 2009; 46 1998; 17 2002; 15 2006; 30 2006; 31 2004; 23 2008; 39 2006; 29 2003; 18 2008; 41 2009; 48 2007; 26 2001; 20 e_1_2_6_20_2 e_1_2_6_8_2 e_1_2_6_7_2 e_1_2_6_18_2 e_1_2_6_9_2 e_1_2_6_19_2 e_1_2_6_4_2 e_1_2_6_3_2 e_1_2_6_6_2 e_1_2_6_5_2 e_1_2_6_12_2 e_1_2_6_13_2 e_1_2_6_2_2 e_1_2_6_10_2 e_1_2_6_11_2 e_1_2_6_21_2 e_1_2_6_16_2 e_1_2_6_17_2 e_1_2_6_14_2 e_1_2_6_15_2 |
| References_xml | – reference: Vovk U, Pernus F, Likar B. A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans Med Imaging 2007; 26: 405-421. – reference: Acosta-Cabronero J, Williams GB, Pereira JM, Pengas G, Nestor PJ. The impact of skull-stripping and radio-frequency bias correction on grey-matter segmentation for voxel-based morphometry. Neuroimage 2008; 39: 1654-1665. – reference: Jovicich J, Czanner S, Han X, et al. MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: Reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths. Neuroimage 2009; 46: 177-192. – reference: Chard DT, Parker GJ, Griffin CM, Thompson AJ, Miller DH. The reproducibility and sensitivity of brain tissue volume measurements derived from an SPM-based segmentation methodology. 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Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp 2002; 15: 1-25. – reference: Shuter B, Yeh IB, Graham S, Au C, Wang SC. Reproducibility of brain tissue volumes in longitudinal studies: effects of changes in signal-to-noise ratio and scanner software. Neuroimage 2008; 41: 371-379. – reference: Gunter JL, Shiung MM, Manduca A, Jack CR Jr. Methodological considerations for measuring rates of brain atrophy. J Magn Reson Imaging 2003; 18: 16-24. – reference: Jovicich J, Czanner S, Greve D, et al. Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data. Neuroimage 2006; 30: 436-443. – reference: Lewis EB, Fox NC. Correction of differential intensity inhomogeneity in longitudinal MR images. Neuroimage 2004; 23: 75-83. – reference: Preboske GM, Gunter JL, Ward CP, Jack CR Jr. Common MRI acquisition non-idealities significantly impact the output of the boundary shift integral method of measuring brain atrophy on serial MRI. Neuroimage 2006; 30: 1196-1202. – reference: Smith SM, Jenkinson M, Woolrich MW, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 2004; 23: S208-S219. – reference: Smith SM. Fast robust automated brain extraction. Hum Brain Mapp 2002; 17: 143-155. – reference: Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 2001; 20: 45-57. – reference: Sled JG, Zijdenbos AP, Evans AC. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 1998; 17: 87-97. – reference: Smith SM, Zhang Y, Jenkinson M, et al. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. 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analysis and implementation as FSL publication-title: Neuroimage – volume: 30 start-page: 436 year: 2006 end-page: 443 article-title: Reliability in multi‐site structural MRI studies: effects of gradient non‐linearity correction on phantom and human data publication-title: Neuroimage – volume: 41 start-page: 371 year: 2008 end-page: 379 article-title: Reproducibility of brain tissue volumes in longitudinal studies: effects of changes in signal‐to‐noise ratio and scanner software publication-title: Neuroimage – volume: 20 start-page: 45 year: 2001 end-page: 57 article-title: Segmentation of brain MR images through a hidden Markov random field model and the expectation‐maximization algorithm publication-title: IEEE Trans Med Imaging – volume: 15 start-page: 1 year: 2002 end-page: 25 article-title: Nonparametric permutation tests for functional neuroimaging: a primer with examples publication-title: Hum Brain Mapp – volume: 46 start-page: 177 year: 2009 end-page: 192 article-title: 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To evaluate the effects of gradient nonlinearity correction and intensity nonuniformity correction on longitudinal (two‐year) changes in global and... To evaluate the effects of gradient nonlinearity correction and intensity nonuniformity correction on longitudinal (two-year) changes in global and regional... |
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| SubjectTerms | Adult Aged Aged, 80 and over Atrophy - pathology Brain - pathology Brain Mapping brain volume Female gradient non-linearity Humans Image Processing, Computer-Assisted - methods intensity non-uniformity Longitudinal Studies longitudinal study Magnetic Resonance Imaging - methods Male Middle Aged SIENA Software |
| Title | Effects of gradient non-linearity correction and intensity non-uniformity correction in longitudinal studies using structural image evaluation using normalization of atrophy (SIENA) |
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