The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
The osteoarthritis initiative: report on the design rationale for the magnetic resonance imaging protocol for the knee
Open accessAbstract
Summary Objectives To report on the process and criteria for selecting acquisition protocols to include in the osteoarthritis initiative (OAI) magnetic resonance imaging (MRI) study protocol for the knee. Methods Candidate knee MR acquisition protocols identified from the literature were first optimized at 3 Tesla (T). Twelve knees from 10 subjects were scanned one time with each of 16 acquisitions considered most likely to achieve the study goals and having the best optimization results. The resultant images and multi-planar reformats were evaluated for artifacts and structural discrimination of articular cartilage at the cartilage–fluid, cartilage–fat, cartilage–capsule, cartilage–meniscus and cartilage–cartilage interfaces. Results The five acquisitions comprising the final OAI MRI protocol were assembled based on the study goals for the imaging protocol, the image evaluation results and the need to image both knees within a 75 min time slot, including positioning. For quantitative cartilage morphometry, fat-suppressed, 3D dual-echo in steady state (DESS) acquisitions appear to provide the best universal cartilage discrimination. Conclusions The OAI knee MRI protocol provides imaging data on multiple articular structures and features relevant to knee OA that will support a broad range of existing and anticipated measurement methods while balancing requirements for high image quality and consistency against the practical considerations of a large multi-center cohort study. Strengths of the final knee MRI protocol include cartilage quantification capabilities in three planes due to multi-planar reconstruction of a thin slice, high spatial resolution 3D DESS acquisition and the multiple, non-fat-suppressed image contrasts measured during the T2 relaxation time mapping acquisition.
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Bottomley, P. A., Foster, T. H., Argersinger, R. E., & Pfeifer, L. M. (1984) · Medical Physics · doi:10.1118/1.595535 · Cited by 1,192
The Association of Bone Marrow Lesions with Pain in Knee Osteoarthritis
Felson, D. T., Chaisson, C. E., Hill, C. L., Totterman, S. M. S., Gale, M. E., Skinner, K. M., Kazis, L., & Gale, D. R. (2001) · Annals of Internal Medicine · doi:10.7326/0003-4819-134-7-200104030-00007 · Cited by 1,002
Bone Marrow Edema and Its Relation to Progression of Knee Osteoarthritis
Felson, D. T., McLaughlin, S., Goggins, J., LaValley, M. P., Gale, M. E., Totterman, S., Li, W., Hill, C., & Gale, D. (2003) · Annals of Internal Medicine · doi:10.7326/0003-4819-139-5_part_1-200309020-00008 · Cited by 761
Cartilage MRI T2 Relaxation Time Mapping: Overview and Applications
Mosher, T. J., & Dardzinski, B. J. (2004) · Seminars in Musculoskeletal Radiology · doi:10.1055/s-2004-861764 · Cited by 605
POTTER, H. G., LINKLATER, J. M., ALLEN, A. A., HANNAFIN, J. A., & HAAS, S. B. (1998) · The Journal of Bone & Joint Surgery · doi:10.2106/00004623-199809000-00005 · Cited by 590
Musculoskeletal MRI at 3.0 T: Relaxation Times and Image Contrast
Gold, G. E., Han, E., Stainsby, J., Wright, G., Brittain, J., & Beaulieu, C. (2004) · American Journal of Roentgenology · doi:10.2214/ajr.183.2.1830343 · Cited by 517
Eckstein, F., Cicuttini, F., Raynauld, J.-P., Waterton, J. C., & Peterfy, C. (2006) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2006.02.026 · Cited by 418
Self-citationQuantitative MRI of cartilage and bone: degenerative changes in osteoarthritis
Eckstein, F., Burstein, D., & Link, T. M. (2006) · NMR in Biomedicine · doi:10.1002/nbm.1063 · Cited by 343
Kühn, K., D’Lima, D. D., Hashimoto, S., & Lotz, M. (2004) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2003.09.015 · Cited by 317
Eckstein, F., Hudelmaier, M., Wirth, W., Kiefer, B., Jackson, R., Yu, J., Eaton, C. B., & Schneider, E. (2006) · Annals of the Rheumatic Diseases · doi:10.1136/ard.2005.039370 · Cited by 275
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Martel-Pelletier, J., Barr, A. J., Cicuttini, F. M., Conaghan, P. G., Cooper, C., Goldring, M. B., Goldring, S. R., Jones, G., Teichtahl, A. J., & Pelletier, J.-P. (2016) · Nature Reviews Disease Primers · doi:10.1038/nrdp.2016.72 · Cited by 1,877
Hunter, D. J., Guermazi, A., Lo, G. H., Grainger, A. J., Conaghan, P. G., Boudreau, R. M., & Roemer, F. W. (2011) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2011.05.004 · Cited by 1,069
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Chaudhari, A. S., Fang, Z., Kogan, F., Wood, J., Stevens, K. J., Gibbons, E. K., Lee, J. H., Gold, G. E., & Hargreaves, B. A. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27178 · Cited by 406
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Metadata
- Authors
- Peterfy, C. G., Schneider, E., & Nevitt, M.
- Venue
- Osteoarthritis and Cartilage
- Publisher
- Elsevier BV
- Published
- 2008-12
- Volume
- 16
- Issue
- 12
- Pages
- 1433-1441
- ISSN
- 1063-4584 (ISSN-L)
- OpenAlex
- W2132697886
- Cited by
- 684
- References
- 26
- FWCI
- 14.44
- Type
- journal article
- Language
- en
- Open access
- bronze
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