The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
A Validation Study of Multicenter Diffusion Tensor Imaging: Reliability of Fractional Anisotropy and Diffusivity Values
Open accessAbstract
BACKGROUND AND PURPOSE: DTI is increasingly being used as a measure to study tissue damage in several neurologic diseases. Our aim was to investigate the comparability of DTI measures between different MR imaging magnets and platforms. MATERIALS AND METHODS: Two healthy volunteers underwent DTI on five 3T MR imaging scanners (3 Trios and 2 Signas) by using a matched 33 noncollinear diffusion-direction pulse sequence. Within each subject, a total of 16 white matter (corpus callosum, periventricular, and deep white matter) and gray matter (cortical and deep gray) ROIs were drawn on a single image set and then were coregistered to the other images. Mean FA, ADC, and longitudinal and transverse diffusivities were calculated within each ROI. Concordance correlations were derived by comparing ROI DTI values among each of the 5 magnets. RESULTS: Mean concordance for FA was 0.96; for both longitudinal and transverse diffusivities, it was 0.93; and for ADC, it was 0.88. Mean scan-rescan concordance was 0.96-0.97 for all DTI measures. Concordance correlations within platforms were, in general, better than those between platforms for all DTI measures (mean concordance of 0.96). CONCLUSIONS: We found that a 3T magnet and high-angular-resolution pulse sequence yielded comparable DTI measurements across different MR imaging magnets and platforms. Our results indicate that FA is the most comparable measure across magnets, followed by individual diffusivities. The comparability of DTI measures between different magnets supports the feasibility of multicentered clinical trials by using DTI as an outcome measure.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Although two different scanners were used (Siemens model Trio; scanner 1 = 285 subjects, 95.6 %; and scanner 2 = 13 subjects, 4.4 %), previous studies with this dataset have pointed out that this variability is negligible (Dickerson et al., 2008; Fox et al., 2012; Melzer et al., 2020; Noble et al., 2017a; Noble et al., 2017b; Zhou et al., 2018).”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Fox et al., 2012
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A Method for Registration of 3-D Shapes
Besl, P., & Mckay, N. D. (1992) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.121791 · Cited by 22,163
Numerical recipes in C: the art of scientific computing
(1993) · Choice Reviews Online · doi:10.5860/choice.30-5638 · Cited by 17,933
Numerical recipes in Pascal: the art of scientific computing
(1990) · Choice Reviews Online · doi:10.5860/choice.27-5158 · Cited by 12,040
Basser, P. J., & Pierpaoli, C. (1996) · Journal of Magnetic Resonance, Series B · doi:10.1006/jmrb.1996.0086 · Cited by 4,306
Estimation of the Effective Self-Diffusion Tensor from the NMR Spin Echo
Basser, P. J., Mattiello, J., & Bihan, D. L. (1994) · Journal of Magnetic Resonance Series B · doi:10.1006/jmrb.1994.1037 · Cited by 3,656
Toward a quantitative assessment of diffusion anisotropy
Pierpaoli, C. M., & Basser, P. J. (1996) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910360612 · Cited by 2,392
Jones, D. K. (2004) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20033 · Cited by 794
Diffusion tensor imaging of lesions and normal-appearing white matter in multiple sclerosis
Werring, D. J., Clark, C. A., Barker, G. J., Thompson, A. J., & Miller, D. H. (1999) · Neurology · doi:10.1212/wnl.52.8.1626 · Cited by 621
A Note on the Concordance Correlation Coefficient
Steichen, T. J., & Cox, N. J. (2002) · The Stata Journal Promoting communications on statistics and Stata · doi:10.1177/1536867x0200200206 · Cited by 430
Investigation of MS normal-appearing brain using diffusion tensor MRI with clinical correlations
Ciccarelli, O., Werring, D. J., Wheeler‐Kingshott, C. A. M. G., Barker, G. J., Parker, G. J. M., Thompson, A. J., & Miller, D. H. (2001) · Neurology · doi:10.1212/wnl.56.7.926 · Cited by 342
Cited by
Sorted by citations
Clinical trials in progressive multiple sclerosis: lessons learned and future perspectives
Ontaneda, D., Fox, R. J., & Chataway, J. (2015) · The Lancet Neurology · doi:10.1016/s1474-4422(14)70264-9 · Cited by 223
Self-citationGadie, A., Shafto, M. A., Leng, Y., Cam-CAN, & Kievit, R. (2017) · BMJ Open · doi:10.1136/bmjopen-2016-014920 · Cited by 224
A large-scale multicentre cerebral diffusion tensor imaging study in amyotrophic lateral sclerosis
Müller, H.-P., Turner, M. R., Großkreutz, J., Abrahams, S., Bede, P., Govind, V., Prudlo, J., Ludolph, A. C., Filippi, M., & Kassubek, J. (2016) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp-2015-311952 · Cited by 176
A purely confirmatory replication study of structural brain-behavior correlations
Boekel, W., Wagenmakers, E., Belay, L., Verhagen, J., Brown, S. D., & Forstmann, B. U. (2015) · Cortex · doi:10.1016/j.cortex.2014.11.019 · Cited by 175
Tamnes, C. K., Roalf, D. R., Goddings, A., & Lebel, C. A. (2017) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2017.12.002 · Cited by 164
Inter-site and inter-scanner diffusion MRI data harmonization
Mirzaalian, H., Ning, L., Savadjiev, P., Pasternak, O., Sylvain Bouix, A., Michailovich, O., Grant, G. A., Marx, C. E., Morey, R. A., Flashman, L. A., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.04.041 · Cited by 153
White Matter Integrity in Highly Traumatized Adults With and Without Post-Traumatic Stress Disorder
Fani, N., King, T. Z., Jovanovic, T., Glover, E. M., Bradley, B., Choi, K., Ely, T., Gutman, D. A., & Ressler, K. J. (2012) · Neuropsychopharmacology · doi:10.1038/npp.2012.146 · Cited by 135
Harmonization of Brain Diffusion MRI: Concepts and Methods
Pinto, M. S., Paolella, R., Billiet, T., Dyck, P. V., Guns, P., Jeurissen, B., Ribbens, A., den Dekker, A. J., & Sijbers, J. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00396 · Cited by 133
A watershed model of individual differences in fluid intelligence
Kievit, R., Davis, S. W., Griffiths, J. D., Correia, M., Cam‐CAN, & Henson, R. N. (2016) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2016.08.008 · Cited by 125
Palacios, E. M., Martin, A. J., Boss, M. A., Ezekiel, F., Chang, Y. S., Yuh, E. L., Vassar, M. J., Schnyer, D. M., MacDonald, C. L., Crawford, K. L., et al. (2017) · American Journal of Neuroradiology · doi:10.3174/ajnr.a5025 · Cited by 110
Achievements and obstacles of remyelinating therapies in multiple sclerosis
Stangel, M., Kuhlmann, T., Matthews, P. M., & Kilpatrick, T. J. (2017) · Nature Reviews Neurology · doi:10.1038/nrneurol.2017.139 · Cited by 116
Diffusion-MRI in neurodegenerative disorders
Goveas, J., O’Dwyer, L., Mascalchi, M., Cosottini, M., Diciotti, S., De Santis, S., Passamonti, L., Tessa, C., Toschi, N., & Giannelli, M. (2015) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2015.04.006 · Cited by 104
Albi, A., Pasternak, O., Minati, L., Marizzoni, M., David Bartrés-faz, A., Bargalló, N., Bosch, B., Rossini, P. M., Marra, C., Müller, B. W., et al. (2016) · Human Brain Mapping · doi:10.1002/hbm.23350 · Cited by 90
Longitudinal reliability of tract‐based spatial statistics in diffusion tensor imaging
Madhyastha, T. M., Mérillat, S., Hirsiger, S., Bezzola, L., Liem, F., Grabowski, T. J., & Jäncke, L. (2014) · Human Brain Mapping · doi:10.1002/hbm.22493 · Cited by 94
Jovicich, J., Marizzoni, M., Bosch, B., Bartrés‐Faz, D., Arnold, J., Benninghoff, J., Wiltfang, J., Roccatagliata, L., Picco, A., Nobili, F. M., et al. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.075 · Cited by 90
White Matter Integrity in Veterans With Mild Traumatic Brain Injury
Sorg, S. F., Delano‐Wood, L., Luc, N., Schiehser, D. M., Hanson, K. L., Nation, D. A., Lanni, E., Jak, A. J., Lu, K., Meloy, M. J., et al. (2013) · Journal of Head Trauma Rehabilitation · doi:10.1097/htr.0b013e31828a1aa4 · Cited by 87
Fani, N., King, T. Z., Shin, J., Srivastava, A., Brewster, R. C., Jovanovic, T., Bradley, B., & Ressler, K. J. (2016) · Depression and Anxiety · doi:10.1002/da.22483 · Cited by 80
Kronlage, M., Schwehr, V., Schwarz, D. F., Godel, T., Uhlmann, L., Heiland, S., Bendszus, M., & Bäumer, P. (2017) · European Radiology · doi:10.1007/s00330-017-5134-z · Cited by 78
Imaging outcome measures of neuroprotection and repair in MS
Oh, J., Ontaneda, D., Azevedo, C. J., Klawiter, E. C., Absinta, M., Arnold, D. L., Bakshi, R., Calabresi, P. A., Crainiceanu, C., Dewey, B. E., et al. (2019) · Neurology · doi:10.1212/wnl.0000000000007099 · Cited by 76
Self-citationAntonenko, D., Fromm, A. E., Thams, F., Grittner, U., Meinzer, M., & Flöel, A. (2023) · Nature Communications · doi:10.1038/s41467-023-38910-x · Cited by 74
White matter integrity alterations in post‐traumatic stress disorder
O'Doherty, D. C. M., Ryder, W., Paquola, C., Tickell, A. M., Chan, C., Hermens, D. F., Bennett, M. R., & Lagopoulos, J. (2017) · Human Brain Mapping · doi:10.1002/hbm.23920 · Cited by 75
Fox, R., Coffey, C., Cudkowicz, M., Gleason, T. L., Goodman, A., Klawiter, E., Matsuda, K., McGovern, M. M., Conwit, R., Naismith, R., et al. (2016) · Contemporary Clinical Trials · doi:10.1016/j.cct.2016.08.009 · Cited by 64
Self-citationSimultaneous Analysis and Quality Assurance for Diffusion Tensor Imaging
Lauzon, C. B., Asman, A. J., Esparza, M. L., Burns, S. S., Fan, Q., Gao, Y., Anderson, A. W., Davis, N., Cutting, L. E., & Landman, B. A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061737 · Cited by 65
Imaging as an Outcome Measure in Multiple Sclerosis
Ontaneda, D., & Fox, R. J. (2016) · Neurotherapeutics · doi:10.1007/s13311-016-0479-6 · Cited by 64
Self-citationDiffusion magnetic resonance imaging in preterm brain injury
Pandit, A. S., Ball, G., Edwards, A. D., & Counsell, S. J. (2013) · Neuroradiology · doi:10.1007/s00234-013-1242-x · Cited by 63
Fani, N., King, T. Z., Brewster, R. C., Srivastava, A., Stevens, J. S., Glover, E. M., Norrholm, S., Bradley, B., Ressler, K., & Jovanović, T. (2014) · Cortex; a journal devoted to the study of the nervous system and behavior · doi:10.1016/j.cortex.2014.11.006 · Cited by 62
Peripheral nerve diffusion tensor imaging is reliable and reproducible
Simon, N. G., Lagopoulos, J., Gallagher, T., Kliot, M., & Kiernan, M. C. (2015) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.25056 · Cited by 56
Harmonizing Diffusion MRI Data Across Multiple Sites and Scanners
Mirzaalian, H., de Pierrefeu, A., Savadjiev, P., Pasternak, O., Sylvain Bouix, A., Kubicki, M. R., Westin, C., Shenton, M. E., & Yogesh Rathi, A. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24553-9_2 · Cited by 56
Easson, A. K., & McIntosh, A. R. (2019) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2019.100630 · Cited by 56
Huang, L., Kutch, J. J., Ellingson, B. M., Martucci, K. T., Harris, R. E., Clauw, D. J., Mackey, S., Mayer, E. A., Schaeffer, A. J., Apkarian, A. V., et al. (2016) · Pain · doi:10.1097/j.pain.0000000000000703 · Cited by 54
Neuroimaging in Blast-Related Mild Traumatic Brain Injury
Mu, W.-Y., Catenaccio, E., & Lipton, M. (2017) · The journal of head trauma rehabilitation · doi:10.1097/htr.0000000000000213 · Cited by 48
In utero diffusion tensor imaging of the fetal brain: A reproducibility study
András, J., Tuura, R., Kellenberger, C. J., & Scheer, I. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.06.013 · Cited by 54
Current and New Directions in MRI in Multiple Sclerosis
Klawiter, E. C. (2013) · CONTINUUM Lifelong Learning in Neurology · doi:10.1212/01.con.0000433283.00221.37 · Cited by 50
Venkatraman, V. K., Gonzalez, C. E., Landman, B. A., Goh, J. O. S., Reiter, D. A., An, Y., & Resnick, S. M. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.06.078 · Cited by 47
Bias Introduced by Multiple Head Coils in MRI Research: An 8 Channel and 32 Channel Coil Comparison
Panman, J. L., To, Y. Y., van der Ende, E. L., Poos, J. M., Jiskoot, L. C., Meeter, L. H. H., Dopper, E. G. P., Bouts, M. J. R. J., van Osch, M. J. P., Rombouts, S. A. R. B., et al. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00729 · Cited by 46
Koyama, T., Marumoto, K., Domen, K., & Miyake, H. (2013) · Neurologia medico-chirurgica · doi:10.2176/nmc.oa2012-0307 · Cited by 44
Fani, N., King, T. Z., Clendinen, C., Hardy, R. A., Surapaneni, S., Blair, J., White, S. F., Powers, A. D., Ely, T. D., Jovanović, T., et al. (2019) · Journal of Affective Disorders · doi:10.1016/j.jad.2019.04.098 · Cited by 47
Reproducibility of Structural and Diffusion Tensor Imaging in the TACERN Multi-Center Study
Prohl, A. K., Scherrer, B., Tomas-Fernandez, X., Filip‐Dhima, R., Kapur, K., Velasco‐Annis, C., Clancy, S., Carmody, E., Dean, M., Valle, M., et al. (2019) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2019.00024 · Cited by 46
Kievit, R., Fuhrmann, D., Borgeest, G. S., Simpson-Kent, I. L., & Henson, R. N. (2018) · Wellcome Open Research · doi:10.12688/wellcomeopenres.14241.2 · Cited by 42
Test–retest reproducibility of a multi‐atlas automated segmentation tool on multimodality brain MRI
de Rezende, T. J. R., de Campos, B. M., Hsu, J., Li, Y., Ceritoglu, C., Kutten, K. S., França, M. C., Mori, S., Miller, M. I., & Faria, A. V. (2019) · Brain and Behavior · doi:10.1002/brb3.1363 · Cited by 42
Multicenter dataset of multi-shell diffusion MRI in healthy traveling adults with identical settings
Tong, Q., He, H., Gong, T., Li, C., Liang, P., Qian, T., Sun, Y., Ding, Q., Li, K., & Zhong, J. (2020) · Scientific Data · doi:10.1038/s41597-020-0493-8 · Cited by 43
Test-retest reliability of high spatial resolution diffusion tensor and diffusion kurtosis imaging
Shahim, P., Holleran, L., Kim, J. H., & Brody, D. L. (2017) · Scientific Reports · doi:10.1038/s41598-017-11747-3 · Cited by 39
Prasuhn, J., Heldmann, M., Münte, T. F., & Brüggemann, N. (2020) · Neurological Research and Practice · doi:10.1186/s42466-020-00092-y · Cited by 39
Andica, C., Kamagata, K., Hayashi, T., Hagiwara, A., Uchida, W., Saito, Y., Kamiya, K., Fujita, S., Akashi, T., Wada, A., et al. (2019) · Neuroradiology · doi:10.1007/s00234-019-02350-6 · Cited by 38
Zhou, X., Sakaie, K., Debbins, J. P., Narayanan, S., Fox, R. J., & Lowe, M. J. (2018) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2018.07.011 · Cited by 36
Self-citationMüller, H., Grön, G., Sprengelmeyer, R., Kassubek, J., Ludolph, A. C., Hobbs, N. Z., Cole, J. H., Roos, R. A. C., Dürr, A., Tabrizi, S. J., et al. (2013) · NeuroImage Clinical · doi:10.1016/j.nicl.2012.12.005 · Cited by 36
Boekel, W., Forstmann, B. U., & Keuken, M. C. (2016) · Psychophysiology · doi:10.1111/psyp.12769 · Cited by 42
Roßkopf, J., Müller, H., Dreyhaupt, J., Gorges, M., Ludolph, A. C., & Kassubek, J. (2015) · Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration · doi:10.3109/21678421.2014.977297 · Cited by 35
Longitudinal Progression Markers of Parkinson’s Disease: Current View on Structural Imaging
Yang, J., Burciu, R. G., & Vaillancourt, D. E. (2018) · Current Neurology and Neuroscience Reports · doi:10.1007/s11910-018-0894-7 · Cited by 35
Structural connectivity and risk for anhedonia after trauma: A prospective study and replication
Fani, N., Michopoulos, V., van Rooij, S. J. H., Clendinen, C., Hardy, R. A., Jovanović, T., ROTHBAUM, B. O., Ressler, K. J., & Stevens, J. S. (2019) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2019.05.009 · Cited by 34
Diagnosis and prognosis of Alzheimer's disease using brain morphometry and white matter connectomes
Wang, Y., Xu, C., Park, J. H., Lee, S., Stern, Y., Yoo, S., Kim, J. H., Kim, H. S., Cha, J., & Initiative, T. A. D. N. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101859 · Cited by 40
Alger, J. R., Ellingson, B. M., Ashe-McNalley, C., Woodworth, D. C., Labus, J. S., Farmer, M. A., Huang, L., Apkarian, A. V., Johnson, K. A., Mackey, S., et al. (2016) · NeuroImage Clinical · doi:10.1016/j.nicl.2015.12.009 · Cited by 34
Rakesh, D., Zalesky, A., & Whittle, S. (2022) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2022.01.006 · Cited by 37
Tong, Q., He, H., Gong, T., Li, C., Liang, P., Qian, T., Sun, Y., Ding, Q., Li, K., & Zhong, J. (2019) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2019.02.011 · Cited by 35
Owens‐Walton, C., Nir, T. M., Al–Bachari, S., Ambrogi, S., Anderson, T., Aventurato, Í. K., Cendes, F., Chen, Y.-L., Ciullo, V., Cook, P., et al. (2024) · npj Parkinson s Disease · doi:10.1038/s41531-024-00758-3 · Cited by 32
Siless, V., Hubbard, N. A., Jones, R. J., Wang, J., Lo, N., Bauer, C. C. C., Goncalves, M., Frosch, I. R., Norton, D. J., Vergara, G. A., et al. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102242 · Cited by 34
Im, K., Paldino, M. J., Poduri, A., Sporns, O., & Grant, P. E. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.08.011 · Cited by 31
Developing therapeutic strategies to promote myelin repair in multiple sclerosis
Baldassari, L. E., Feng, J. J., Clayton, B. L. L., Oh, S., Sakaie, K., Tesar, P. J., Wang, Y., & Cohen, J. A. (2019) · Expert Review of Neurotherapeutics · doi:10.1080/14737175.2019.1632192 · Cited by 29
Self-citationStrauß, S., Hulkower, M., Gulko, E., Zampolin, R. L., Gutman, D. A., Chitkara, M., Sears, M. Z., & Lipton, M. L. (2015) · Topics in Magnetic Resonance Imaging · doi:10.1097/rmr.0000000000000071 · Cited by 27
MR Scanner Systems Should Be Adequately Characterized in Diffusion-MRI of the Breast
Giannelli, M., Sghedoni, R., Iacconi, C., Iori, M., Traino, C., Guerrisi, M., Mascalchi, M., Toschi, N., & Diciotti, S. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086280 · Cited by 27
Microstructural anatomical differences between bilinguals and monolinguals
Singh, N. C., Rajan, A., Malagi, A., Ramanujan, K., Canini, M., Rosa, P. A. D., Raghunathan, P., Weekes, B., & Abutalebi, J. (2017) · Bilingualism Language and Cognition · doi:10.1017/s1366728917000438 · Cited by 30
The biological basis of intelligence: Benchmark findings
Hilger, K., Spinath, F. M., Troche, S. J., & Schubert, A. (2022) · Intelligence · doi:10.1016/j.intell.2022.101665 · Cited by 24
Yin, G., Li, T., Jin, S., Wang, N., Li, J., Wu, C., He, H., & Wang, J. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad178 · Cited by 26
A multi-center study: Intra-scan and inter-scan variability of diffusion spectrum imaging
Lemkaddem, A., Daducci, A., Vulliémoz, S., O’Brien, K., Lazeyras, F., Hauf, M., Wiest, R., Meuli, R., Seeck, M., Krüger, G., et al. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.04.045 · Cited by 26
Metzger‐Peter, K., Kremer, L. D., Edan, G., de Sousa, P. L., Lamy, J., Bagnard, D., Mensah‐Nyagan, A. G., Tricard, T., Mathey, G., Debouverie, M., et al. (2020) · Trials · doi:10.1186/s13063-020-04517-6 · Cited by 26
Koyama, T., Koumo, M., Uchiyama, Y., & Domen, K. (2017) · Journal of Stroke and Cerebrovascular Diseases · doi:10.1016/j.jstrokecerebrovasdis.2017.10.022 · Cited by 25
A Comparative evaluation of voxel-based spatial mapping in diffusion tensor imaging
Cabeen, R. P., Bastin, M. E., & Laidlaw, D. H. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.11.020 · Cited by 25
Diffusion MRI metrics and their relation to dementia severity: effects of harmonization approaches
Thomopoulos, S. I., Nir, T. M., Villalón‐Reina, J. E., Zavaliangos‐Petropulu, A., Maiti, P., Zheng, H., Nourollahimoghadam, E., Jahanshad, N., Thompson, P. M., & Initiative, A. D. N. (2021) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1117/12.2606337 · Cited by 22
Thieleking, R., Zhang, R., Paerisch, M., Wirkner, K., Anwander, A., Beyer, F., Villringer, A., & Witte, A. V. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10214987 · Cited by 21
Gunda, B. B., Porcher, R., Duering, M., Guichard, J., Mawet, J., Jouvent, É., Dichgans, M., & Chabriat, H. (2014) · PLoS ONE · doi:10.1371/journal.pone.0097173 · Cited by 21
Kumpulainen, V., Merisaari, H., Copeland, A., Silver, E. A., Pulli, E. P., Lewis, J. D., Saukko, E., Saunavaara, J. P., Karlsson, L., Karlsson, H., et al. (2022) · European Journal of Neuroscience · doi:10.1111/ejn.15785 · Cited by 19
András, J., Tuura, R., Kottke, R., Kellenberger, C. J., & Scheer, I. (2017) · European Radiology Experimental · doi:10.1186/s41747-017-0031-4 · Cited by 20
Machine Learning for Brain MRI Data Harmonisation: A Systematic Review
Wen, G., Shim, V., Holdsworth, S. J., Fernandez, J., Qiao, M., Kasabov, N. K., & Wang, A. (2023) · Bioengineering · doi:10.3390/bioengineering10040397 · Cited by 19
Sagawa, H., Fushimi, Y., Nakajima, S., Fujimoto, K., Miyake, K. K., Numamoto, H., Koizumi, K., Nambu, M., Kataoka, H., Nakamoto, Y., et al. (2020) · Magnetic Resonance in Medical Sciences · doi:10.2463/mrms.tn.2020-0061 · Cited by 18
Helmer, K. G., Chou, M., Preciado, R. I., Gimi, B., Rollins, N. K., Song, A. W., Turner, J. A., & Mori, S. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2217445 · Cited by 18
Patay, Z. J., Parra, C., Hawk, H. E., George, A., Li, Y., Scoggins, M., Broniscer, A., & Ogg, R. J. (2014) · The Cerebellum · doi:10.1007/s12311-014-0575-2 · Cited by 17
Deprez, S., de Ruiter, M. B., Bogaert, S., Peeters, R. R., Belderbos, J. S. A., Ruysscher, D. D., Schagen, S. B., Sunaert, S. G., Pullens, P., & Achten, E. (2018) · Neuroradiology · doi:10.1007/s00234-018-2017-1 · Cited by 18
Prokscha, T., Guo, J., Hirsch, S., Braun, J., Sack, I., Meyer, T., & Scheel, M. (2013) · Clinical Neuroradiology · doi:10.1007/s00062-013-0221-2 · Cited by 16
Test-Retest Reliability of Diffusion Tensor Imaging in Huntington’s Disease
Cole, J. H., Farmer, R. E., Rees, E. M., Johnson, H. J., Frost, C. D., Scahill, R. I., & Hobbs, N. Z. (2014) · PLoS Currents · doi:10.1371/currents.hd.f19ef63fff962f5cd9c0e88f4844f43b · Cited by 17
Lewis, A. F., Myers, M., Heiser, J., Kolar, M. B., Baird, J. F., & Stewart, J. C. (2020) · Human Brain Mapping · doi:10.1002/hbm.24961 · Cited by 16
Plitman, E., Bussy, A., Valiquette, V., Salaciak, A., Patel, R., Cupo, L., Béland, M., Tullo, S., Tardif, C. L., Rajah, M. N., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118172 · Cited by 16
Chianca, V., Albano, D., Rizzo, S., Maas, M. M., Sconfienza, L. M., & Grande, F. D. (2023) · Insights into Imaging · doi:10.1186/s13244-023-01374-0 · Cited by 14
Quantitative quality assurance in a multicenter HARDI clinical trial at 3 T
Zhou, X., Sakaie, K., Debbins, J. P., KIRSCH, J. E., Tatsuoka, C. M., Fox, R. J., & Lowe, M. J. (2016) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2016.08.022 · Cited by 14
Self-citationWang, X., Wang, Y., Gao, D., Zhao, Z., Wang, H., Wang, S., & Liu, S. (2023) · Japanese Journal of Radiology · doi:10.1007/s11604-023-01419-w · Cited by 14
McNamara, C., Sugrue, G., Murray, B., & MacMahon, P. J. (2017) · American Journal of Neuroradiology · doi:10.3174/ajnr.a5148 · Cited by 15
Min, J., Park, M., Choi, J. W., Jahng, G., & Moon, W. (2018) · Korean Journal of Radiology · doi:10.3348/kjr.2018.19.4.777 · Cited by 14
Κender, Ζ., Jende, J. M. E., Kurz, F. T., Tsilingiris, D., Schimpfle, L., Sulaj, A., von Rauchhaupt, E., Bartl, H., Mooshage, C. M., Göpfert, J. C., et al. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1046690 · Cited by 14
Paldino, M. J., Chu, Z. D., Chapieski, M. L., Golriz, F., & Zhang, W. (2017) · British Journal of Radiology · doi:10.1259/bjr.20160656 · Cited by 13
Monté, G. C., Segura, B., Strafella, A. P., van Eimeren, T., Ibarretxe‐Bilbao, N., Díez‐Cirarda, M., Eggers, C., Lucas‐Jiménez, O., Ojeda, N., Peña, J., et al. (2022) · Human Brain Mapping · doi:10.1002/hbm.25838 · Cited by 14
Gonzalez-Gomez, R., Ibañez, A., & Moguilner, S. (2023) · Network Neuroscience · doi:10.1162/netn_a_00285 · Cited by 10
White Matter Correlates of Domain-Specific Working Memory
Horne, A., Ding, J., Schnur, T. T., & Martin, R. C. (2022) · Brain Sciences · doi:10.3390/brainsci13010019 · Cited by 10
Paldino, M. J., Hedges, K., Rodrigues, K., & Barboriak, D. P. (2014) · British Journal of Radiology · doi:10.1259/bjr.20140095 · Cited by 10
Schneider, R., Krieger, B., Ladopoulos, T., Ayzenberg, I., Biesalski, A., Gold, R., Bellenberg, B., & Lukas, C. (2022) · Brain Communications · doi:10.1093/braincomms/fcac172 · Cited by 9
Diffusion‐derived intravoxel‐incoherent motion anisotropy relates to CSF and blood flow
Voorter, P., Jansen, J. F. A., van der Thiel, M. M., van Dinther, M., Postma, A. A., van Oostenbrugge, R. J., Gurney‐Champion, O. J., Drenthen, G. S., & Backes, W. H. (2024) · Magnetic Resonance in Medicine · doi:10.1002/mrm.30294 · Cited by 9
White-Matter Pathways for Statistical Learning of Temporal Structures
Karlaftis, V. M., Wang, R., Shen, Y., Peter Tiňo, B., Williams, G. B., Welchman, A. E., & Kourtzi, Z. (2018) · eNeuro · doi:10.1523/eneuro.0382-17.2018 · Cited by 8
Clinical Significance of Diffusion Tensor Imaging in Metachromatic Leukodystrophy
Amedick, L. B., Martin, P., Beschle, J., Strölin, M., Wilke, M., Wolf, N. I., Pouwels, P. J. W., Hagberg, G. E., Klose, U., Naegele, T., et al. (2023) · Neuropediatrics · doi:10.1055/a-2073-4178 · Cited by 8
Rakesh, D., Dehestani, N., & Whittle, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-96023-6.00124-x · Cited by 9
Merisaari, H., Karlsson, L., Scheinin, N., Shulist, S. J., Lewis, J. D., Karlsson, H., & Tuulari, J. (2023) · bioRxiv · doi:10.1111/ejn.16135 · Cited by 6
Treit, S., Stolz, E., Rickard, J. N., McCreary, C. R., Bagshawe, M., Frayne, R., Lebel, C. A., Emery, D. J., & Beaulieu, C. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2022.826564 · Cited by 10
Kievit, R., Fuhrmann, D., Borgeest, G. S., Simpson-Kent, I. L., & Henson, R. N. (2018) · Wellcome Open Research · doi:10.12688/wellcomeopenres.14241.1 · Cited by 9
Metadata
- Authors
- Fox, R. J., Sakaie, K., Lee, J.-C., Debbins, J. P., Liu, Y., Arnold, D. L., Melhem, E. R., Smith, C. H., Philips, M. D., Lowe, M., & Fisher, E.
- Affiliations
- Montreal Neurological Institute and Hospital (CA)
- Corresponding author
- Fox, R.J.
- Venue
- American Journal of Neuroradiology
- Published
- 2011-12-15
- Volume
- 33
- Issue
- 4
- Pages
- 695-700
- ISSN
- 0195-6108 (ISSN-L)
- 1936-959X
- PMC
- PMC8050446
- OpenAlex
- W2321691898
- Cited by
- 137
- References
- 27
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 7 · 2023: 13 · 2022: 12 · 2021: 5 · 2020: 8 · 2019: 14 · 2018: 10 · 2017: 13 · 2016: 17 · 2015: 10 · 2014: 10 · 2013: 8 · 2012: 2
- FWCI
- 4.88
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- American Journal of Neuroradiology (publishedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Multiple Sclerosis Society
- Biogen
- Teva Pharmaceutical Industries
- National Institutes of Health
- Genentech
- National Institute of Neurological Disorders and Stroke (K23 NS 47211, K23NS047211)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- diffusion tensor imaging, fractional anisotropy, apparent diffusion coefficient, inter-scanner reproducibility, white matter, gray matter, high angular resolution diffusion MRI, 3T MRI
- MeSH terms
- Adult, Brain, Equipment Design, Humans, Image Enhancement, Male, Ohio, Sensitivity and Specificity, Reproducibility of Results, Anisotropy, Equipment Failure Analysis, Diffusion Magnetic Resonance Imaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review