The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Abdominal CT With Model-Based Iterative Reconstruction (MBIR): Initial Results of a Prospective Trial Comparing Ultralow-Dose With Standard-Dose Imaging
Open accessAbstract
OBJECTIVE: The purpose of this study was to report preliminary results of an ongoing prospective trial of ultralow-dose abdominal MDCT. SUBJECTS AND METHODS: Imaging with standard-dose contrast-enhanced (n = 21) and unenhanced (n = 24) clinical abdominal MDCT protocols was immediately followed by ultralow-dose imaging of a matched series of 45 consecutively registered adults (mean age, 57.9 years; mean body mass index, 28.5). The ultralow-dose images were reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and model-based iterative reconstruction (MBIR). Standard-dose series were reconstructed with FBP (reference standard). Image noise was measured at multiple predefined sites. Two blinded abdominal radiologists interpreted randomly presented ultralow-dose images for multilevel subjective image quality (5-point scale) and depiction of organ-based focal lesions. RESULTS: Mean dose reduction relative to the standard series was 74% (median, 78%; range, 57-88%; mean effective dose, 1.90 mSv). Mean multiorgan image noise for low-dose MBIR was 14.7 ± 2.6 HU, significantly lower than standard-dose FBP (28.9 ± 9.9 HU), low-dose FBP (59.2 ± 23.3 HU), and ASIR (45.6 ± 14.1 HU) (p < 0.001). The mean subjective image quality score for low-dose MBIR (3.0 ± 0.5) was significantly higher than for low-dose FBP (1.6 ± 0.7) and ASIR (1.8 ± 0.7) (p < 0.001). Readers identified 213 focal noncalcific lesions with standard-dose FBP. Pooled lesion detection was higher for low-dose MBIR (79.3% [169/213]) compared with low-dose FBP (66.2% [141/213]) and ASIR (62.0% [132/213]) (p < 0.05). CONCLUSION: MBIR shows great potential for substantially reducing radiation doses at routine abdominal CT. Both FBP and ASIR are limited in this regard owing to reduced image quality and diagnostic capability. Further investigation is needed to determine the optimal dose level for MBIR that maintains adequate diagnostic performance. In general, objective and subjective image quality measurements do not necessarily correlate with diagnostic performance at ultralow-dose CT.
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
Computed Tomography — An Increasing Source of Radiation Exposure
Brenner, D. J., & Hall, E. J. (2007) · New England Journal of Medicine · doi:10.1056/nejmra072149 · Cited by 8,748
Pearce, M. S., Salotti, J. A., Little, M. P., McHugh, K., Lee, C., Kim, K. P., Howe, N. L., Ronckers, C. M., Rajaraman, P., Craft, A. W., et al. (2012) · The Lancet · doi:10.1016/s0140-6736(12)60815-0 · Cited by 3,735
Chen, G.-H., Tang, J., & Leng, S. (2008) · Medical Physics · doi:10.1118/1.2836423 · Cited by 1,141
Self-citationDeak, P. D., Smal, Y., & Kalender, W. A. (2010) · Radiology · doi:10.1148/radiol.10100047 · Cited by 858
Iterative Reconstruction Technique for Reducing Body Radiation Dose at CT: Feasibility Study
Hara, A. K., Paden, R. G., Silva, A. C., Kujak, J. L., Lawder, H. J., & Pavlicek, W. (2009) · American Journal of Roentgenology · doi:10.2214/ajr.09.2397 · Cited by 732
The Measurement, Reporting, and Management of Radiation Dose in CT
McCollough, C., Cody, D., Edyvean, S., Geise, R., Gould, B., Keat, N., Huda, W., Judy, P., Kalender, W., McNitt-Gray, M., et al. (2008) · doi:10.37206/97 · Cited by 569
Singh, S., Kalra, M. K., Hsieh, J., Licato, P. E., Do, S., Pien, H. H., & Blake, M. A. (2010) · Radiology · doi:10.1148/radiol.10092212 · Cited by 435
Mulshine, J. L., & Sullivan, D. C. (2005) · New England Journal of Medicine · doi:10.1056/nejmcp042630 · Cited by 419
Reducing Abdominal CT Radiation Dose With Adaptive Statistical Iterative Reconstruction Technique
Prakash, P., Kalra, M. K., Kambadakone, A., et al. (2010) · Investigative Radiology · doi:10.1097/rli.ob013e3181dzfeec · Cited by 366
Ionizing Radiation Exposure of the Population of the United States
Broga, D. (2009) · Medical Physics · doi:10.1118/1.3245881 · Cited by 326
Cited by
Deep learning with convolutional neural network in radiology
Yasaka, K., Akai, H., Kunimatsu, A., Kiryu, S., & Abe, O. (2018) · Japanese Journal of Radiology · doi:10.1007/s11604-018-0726-3 · Cited by 368
Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT
Akagi, M., Nakamura, Y., Higaki, T., Narita, K., Honda, Y., Zhou, J., Yu, Z., Akino, N., & Awai, K. (2019) · European Radiology · doi:10.1007/s00330-019-06170-3 · Cited by 346
Metadata
- Authors
- Pickhardt, P. J., Lubner, M. G., Kim, D. H., Tang, J., Ruma, J. A., del Rio, A. M., & Chen, G.-H.
- Venue
- American Journal of Roentgenology
- Publisher
- American Roentgen Ray Society
- Published
- 2012-12
- Volume
- 199
- Issue
- 6
- Pages
- 1266-1274
- ISSN
- 0002-9580 (ISSN-L)
- 0361-803X
- 1546-3141
- OpenAlex
- W2118902776
- Cited by
- 309
- References
- 24
- FWCI
- 27.37
- 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: Abdominal CT With Model-Based Iterative Reconstruction (MBIR): Initial Results of a Prospective Trial Comparing Ultralow-Dose With Standard-Dose Imaging. https://app.cochat.ai/papers/10.2214/ajr.12.9382
- The paper
- Pickhardt, P. J., Lubner, M. G., Kim, D. H., Tang, J., Ruma, J. A., del Rio, A. M., & Chen, G.-H. (2012). Abdominal CT With Model-Based Iterative Reconstruction (MBIR): Initial Results of a Prospective Trial Comparing Ultralow-Dose With Standard-Dose Imaging. American Journal of Roentgenology. https://doi.org/10.2214/ajr.12.9382
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.