The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Low-Cost High-Performance MRI
Open accessAbstract
Magnetic Resonance Imaging (MRI) is unparalleled in its ability to visualize anatomical structure and function non-invasively with high spatial and temporal resolution. Yet to overcome the low sensitivity inherent in inductive detection of weakly polarized nuclear spins, the vast majority of clinical MRI scanners employ superconducting magnets producing very high magnetic fields. Commonly found at 1.5–3 tesla (T), these powerful magnets are massive and have very strict infrastructure demands that preclude operation in many environments. MRI scanners are costly to purchase, site and maintain, with the purchase price approaching $1 M per tesla (T) of magnetic field. We present here a remarkably simple, non-cryogenic approach to high-performance human MRI at ultra-low magnetic field, whereby modern under-sampling strategies are combined with fully-refocused dynamic spin control using steady-state free precession techniques. At 6.5 mT (more than 450 times lower than clinical MRI scanners) we demonstrate (2.5 × 3.5 × 8.5) mm 3 imaging resolution in the living human brain using a simple, open-geometry electromagnet, with 3D image acquisition over the entire brain in 6 minutes. We contend that these practical ultra-low magnetic field implementations of MRI (<10 mT) will complement traditional MRI, providing clinically relevant images and setting new standards for affordable (<$50,000) and robust portable devices.
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.
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
2 retracted
SQUID-detected ultra-low field MRI
Espy, M. A., Matlashov, A. N., & Volegov, P. L. (2013) · Journal of Magnetic Resonance · doi:10.1016/j.jmr.2013.02.009 · Cited by 60
RetractedWITHDRAWN: SQUID-detected ultra-low field MRI
Espy, M. A., Matlashov, A. N., & Volegov, P. L. (2012) · Journal of Magnetic Resonance · doi:10.1016/j.jmr.2012.11.030 · Cited by 17
RetractedScale-space and edge detection using anisotropic diffusion
Perona, P., & Malik, J. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.56205 · Cited by 12,119
SENSE: Sensitivity encoding for fast MRI
Pruessmann, K. P., Weiger, M., Scheidegger, M. B., & Boesiger, P. (1999) · Magnetic Resonance in Medicine · doi:10.1002/(sici)1522-2594(199911)42:5<952::aid-mrm16>3.0.co;2-s · Cited by 6,251
Generalized autocalibrating partially parallel acquisitions (GRAPPA)
Griswold, M. A., Jakob, P. M., Heidemann, R. M., Nittka, M., Jellus, V., Wang, J., Kiefer, B., & Haase, A. (2002) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10171 · Cited by 5,303
Nanoscale magnetic sensing with an individual electronic spin in diamond
Maze, J. R., Stanwix, P. L., Hodges, J. S., Hong, S., Taylor, J. M., Cappellaro, P., Jiang, L., Dutt, M. V. G., Togan, E., Zibrov, A. S., et al. (2008) · Nature · doi:10.1038/nature07279 · Cited by 1,984
High-sensitivity diamond magnetometer with nanoscale resolution
Taylor, J. M., Cappellaro, P., Childress, L., Jiang, L., Budker, D., Hemmer, P. R., Yacoby, A., Walsworth, R., & Lukin, M. D. (2008) · Nature Physics · doi:10.1038/nphys1075 · Cited by 1,959
A subfemtotesla multichannel atomic magnetometer
Kominis, I. K., Kornack, T. W., Allred, J. C., & Romalis, M. V. (2003) · Nature · doi:10.1038/nature01484 · Cited by 1,631
Magnetic resonance fingerprinting
Ma, D., Gulani, V., Seiberlich, N., Liu, K., Sunshine, J. L., Duerk, J. L., & Griswold, M. A. (2013) · Nature · doi:10.1038/nature11971 · Cited by 1,577
The signal-to-noise ratio of the nuclear magnetic resonance experiment
Hoult, D. I., & Richards, R. E. (2011) · Journal of Magnetic Resonance · doi:10.1016/j.jmr.2011.09.018 · Cited by 995
Metadata
- Authors
- Sarracanie, M., LaPierre, C. D., Salameh, N., Waddington, D. E. J., Witzel, T., & Rosen, M. S.
- Venue
- Scientific Reports
- Published
- 2015-10-15
- Volume
- 5
- Issue
- 1
- Pages
- 15177-15177
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2186925649
- Cited by
- 295
- References
- 43
- FWCI
- 10.10
- Type
- journal article
- Language
- en
- Open access
- gold
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: Low-Cost High-Performance MRI. https://app.cochat.ai/papers/10.1038/srep15177
- The paper
- Sarracanie, M., LaPierre, C. D., Salameh, N., Waddington, D. E. J., Witzel, T., & Rosen, M. S. (2015). Low-Cost High-Performance MRI. Scientific Reports. https://doi.org/10.1038/srep15177
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.