The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Medical image processing on the GPU – Past, present and future
Open accessOverview
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.
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
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812
Statistical parametric maps in functional imaging: A general linear approach
Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J. ‐., Frith, C. D., & Frackowiak, R. S. J. (1994) · Human Brain Mapping · doi:10.1002/hbm.460020402 · Cited by 9,688
Bilateral Filtering for Gray and Color Images
Tomasi, C., & Manduchi, R. (2002) · Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) · doi:10.1109/iccv.1998.710815 · Cited by 8,177 · cited 2×
“filtering (Tomasi and Manduchi, 1998; Elad, 2002), adaptive filtering (Knutsson et al.”
BackgroundMethodA Non-Local Algorithm for Image Denoising
Buades, A., Coll, B., & Morel, J.-M. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.38 · Cited by 7,616
Sparse MRI: The application of compressed sensing for rapid MR imaging
Lustig, M., Donoho, D., & Pauly, J. M. (2007) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21391 · Cited by 6,952
Nonparametric permutation tests for functional neuroimaging: A primer with examples
Nichols, T. E., & Holmes, A. P. (2002) · Human Brain Mapping · doi:10.1002/hbm.1058 · Cited by 6,256
Functional mapping of the human visual cortex by magnetic resonance imaging
Belliveau, J. W., Kennedy, D. N., McKinstry, R. C., Buchbinder, B. R., Weisskoff, R. M., Cohen, M. S., Vevea, J. M., Brady, T. J., & Rosen, B. R. (1991) · Science · doi:10.1126/science.1948051 · Cited by 1,883
(2008) · doi:10.1145/1401132.1401152 · Cited by 1,575
Computation of component image velocity from local phase information
Fleet, D. J., & Jepson, A. D. (1990) · International Journal of Computer Vision · doi:10.1007/bf00056772 · Cited by 1,085
Control over brain activation and pain learned by using real-time functional MRI
deCharms, R. C., Maeda, F., Glover, G. H., Ludlow, D., Pauly, J. M., Soneji, D., Gabrieli, J. D. E., & Mackey, S. C. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0505210102 · Cited by 876 · cited 2×
“It is also possible for the subject to monitor his or her own brain activity, for example to learn how to suppress chronic pain (deCharms et al., 2005).”
Background
Cited by
Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates
Eklund, A., Nichols, T. E., & Knutsson, H. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1602413113 · Cited by 3,487
Self-citationAn overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058
The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century
Maleki Varnosfaderani, S., & Forouzanfar, M. (2024) · Bioengineering · doi:10.3390/bioengineering11040337 · Cited by 931
Radiomics in breast cancer classification and prediction
Conti, A., Duggento, A., Indovina, I., Guerrisi, M., & Toschi, N. (2021) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2020.04.002 · Cited by 386
Medical image segmentation on GPUs – A comprehensive review
Smistad, E., Falch, T. L., Bozorgi, M., Elster, A. C., & Lindseth, F. (2015) · Medical Image Analysis · doi:10.1016/j.media.2014.10.012 · Cited by 264
Metadata
- Authors
- Eklund, A., Dufort, P., Forsberg, D., & LaConte, S. M.
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2013-12
- Volume
- 17
- Issue
- 8
- Pages
- 1073-1094
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W2150903265
- Cited by
- 397
- References
- 22
- FWCI
- 78.38
- Type
- journal article
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Medical image processing on the GPU – Past, present and future. https://app.cochat.ai/papers/10.1016/j.media.2013.05.008
- The paper
- Eklund, A., Dufort, P., Forsberg, D., & LaConte, S. M. (2013). Medical image processing on the GPU – Past, present and future. Medical Image Analysis. https://doi.org/10.1016/j.media.2013.05.008
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
