I have posted recently about the most cited (important?) papers in Medical Imaging in the last ten/five/two years here. Today I look for the most cited papers in the field of MRI. Interesting to note that these 3 papers were published in Neuroimage.
Most cited paper in Radiology, Nuclear Science and Medical Imaging Field about MRI:
- of the last 10 years with 1346 citations:
Ashburner, J., & Friston, K. (2005). Unified segmentation NeuroImage, 26 (3), 839-851 DOI: 10.1016/j.neuroimage.2005.02.018
This paper is the basis for the SPM framework, one of the most important in the field of MRI. Thus, it is understandable that this paper has a lot of citations, because most researchers who use this framework (and there are a lot, myself included) use this paper in their citations.
- of the last 5 years with 250 citations:
Klein, A., Andersson, J., Ardekani, B., Ashburner, J., Avants, B., Chiang, M., Christensen, G., Collins, D., Gee, J., Hellier, P., Song, J., Jenkinson, M., Lepage, C., Rueckert, D., Thompson, P., Vercauteren, T., Woods, R., Mann, J., & Parsey, R. (2009). Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration NeuroImage, 46 (3), 786-802 DOI: 10.1016/j.neuroimage.2008.12.037
I have to say that I am quite surprised by finding this paper on top. Brain MRI registration is nowadays considered almost a solved problem and I don't think there are many people looking into this anymore. However, it is always nice to put in your own paper: "I used this registration, because this paper says it is the best".
- of the last 2 years with 106 citations:
Smith, S., Miller, K., Salimi-Khorshidi, G., Webster, M., Beckmann, C., Nichols, T., Ramsey, J., & Woolrich, M. (2011). Network modelling methods for FMRI NeuroImage, 54 (2), 875-891 DOI: 10.1016/j.neuroimage.2010.08.063
I have talked about brain networks some times in this blog and this paper shows me that this topic has been hot in the last two years. This papers discusses different methods to obtain networks with fMRI data: "Many different methods are being used in the literature, but almost none has been carefully validated or compared for use on FMRI timeseries data."
Most cited paper in Radiology, Nuclear Science and Medical Imaging Field about MRI:
- of the last 10 years with 1346 citations:
Ashburner, J., & Friston, K. (2005). Unified segmentation NeuroImage, 26 (3), 839-851 DOI: 10.1016/j.neuroimage.2005.02.018
This paper is the basis for the SPM framework, one of the most important in the field of MRI. Thus, it is understandable that this paper has a lot of citations, because most researchers who use this framework (and there are a lot, myself included) use this paper in their citations.
- of the last 5 years with 250 citations:
Klein, A., Andersson, J., Ardekani, B., Ashburner, J., Avants, B., Chiang, M., Christensen, G., Collins, D., Gee, J., Hellier, P., Song, J., Jenkinson, M., Lepage, C., Rueckert, D., Thompson, P., Vercauteren, T., Woods, R., Mann, J., & Parsey, R. (2009). Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration NeuroImage, 46 (3), 786-802 DOI: 10.1016/j.neuroimage.2008.12.037
I have to say that I am quite surprised by finding this paper on top. Brain MRI registration is nowadays considered almost a solved problem and I don't think there are many people looking into this anymore. However, it is always nice to put in your own paper: "I used this registration, because this paper says it is the best".
- of the last 2 years with 106 citations:
Smith, S., Miller, K., Salimi-Khorshidi, G., Webster, M., Beckmann, C., Nichols, T., Ramsey, J., & Woolrich, M. (2011). Network modelling methods for FMRI NeuroImage, 54 (2), 875-891 DOI: 10.1016/j.neuroimage.2010.08.063
I have talked about brain networks some times in this blog and this paper shows me that this topic has been hot in the last two years. This papers discusses different methods to obtain networks with fMRI data: "Many different methods are being used in the literature, but almost none has been carefully validated or compared for use on FMRI timeseries data."
No comments:
Post a Comment