The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Automatic quality assessment in structural brain magnetic resonance imaging
Open accessAbstract
MRI has evolved into an important diagnostic technique in medical imaging. However, reliability of the derived diagnosis can be degraded by artifacts, which challenge both radiologists and automatic computer‐aided diagnosis. This work proposes a fully‐automatic method for measuring image quality of three‐dimensional (3D) structural MRI. Quality measures are derived by analyzing the air background of magnitude images and are capable of detecting image degradation from several sources, including bulk motion, residual magnetization from incomplete spoiling, blurring, and ghosting. The method has been validated on 749 3D T1‐weighted 1.5T and 3T head scans acquired at 36 Alzheimer's Disease Neuroimaging Initiative (ADNI) study sites operating with various software and hardware combinations. Results are compared against qualitative grades assigned by the ADNI quality control center (taken as the reference standard). The derived quality indices are independent of the MRI system used and agree with the reference standard quality ratings with high sensitivity and specificity (>85%). The proposed procedures for quality assessment could be of great value for both research and routine clinical imaging. It could greatly improve workflow through its ability to rule out the need for a repeat scan while the patient is still in the magnet bore. Magn Reson Med, 2009. © 2009 Wiley‐Liss, Inc.
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The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
Jack, C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., L. Whitwell, J., Ward, C., et al. (2008) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21049 · Cited by 4,347
Cited by
MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites
Esteban, O., Birman, D., Schaer, M., Koyejo, O. O., Poldrack, R. A., & Gorgolewski, K. J. (2017) · PLOS ONE · doi:10.1371/journal.pone.0184661 · Cited by 920
Enhancing studies of the connectome in autism using the autism brain imaging data exchange II
Di Martino, A., O’Connor, D., Chen, B., Alaerts, K., Anderson, J. S., Assaf, M., Balsters, J. H., Baxter, L., Beggiato, A., Bernaerts, S., et al. (2017) · Scientific Data · doi:10.1038/sdata.2017.10 · Cited by 710
Weiner, M. W., Veitch, D. P., Aisen, P. S., Beckett, L. A., Cairns, N. J., Cedarbaum, J., Green, R. C., Harvey, D., Jack, C. R., Jagust, W., et al. (2015) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2014.11.001 · Cited by 348
Self-citationAlexander-Bloch, A., Clasen, L., Stockman, M., Ronan, L., Lalonde, F., Giedd, J., & Raznahan, A. (2016) · Human Brain Mapping · doi:10.1002/hbm.23180 · Cited by 178
Early cerebral small vessel disease and brain volume, cognition, and gait
Smith, E. E., O'Donnell, M., Dagenais, G., Lear, S. A., Wielgosz, A., Sharma, M., Poirier, P., Stotts, G., Black, S. E., Strother, S., et al. (2015) · Annals of Neurology · doi:10.1002/ana.24320 · Cited by 207
Metadata
- Authors
- Mortamet, B., Bernstein, M. A., Jack, C. R., Gunter, J. L., Ward, C., Britson, P. J., Meuli, R., Thiran, J., & Krueger, G.
- Venue
- Magnetic Resonance in Medicine
- Publisher
- Wiley
- Published
- 2009-06-12
- Volume
- 62
- Issue
- 2
- Pages
- 365-372
- ISSN
- 0740-3194 (ISSN-L)
- 1522-2594
- OpenAlex
- W2030309005
- Cited by
- 170
- References
- 1
- FWCI
- 3.05
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Mortamet, B., Bernstein, M. A., Jack, C. R., Gunter, J. L., Ward, C., Britson, P. J., Meuli, R., Thiran, J., & Krueger, G. (2009). Automatic quality assessment in structural brain magnetic resonance imaging. Magnetic Resonance in Medicine. https://doi.org/10.1002/mrm.21992
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