The DOI matches its Crossref record; 62 of the 64 references it cites were found in a registry or an index.
Random forest regression for magnetic resonance image synthesis
Open accessAbstract
By choosing different pulse sequences and their parameters, magnetic resonance imaging (MRI) can generate a large variety of tissue contrasts. This very flexibility, however, can yield inconsistencies with MRI acquisitions across datasets or scanning sessions that can in turn cause inconsistent automated image analysis. Although image synthesis of MR images has been shown to be helpful in addressing this problem, an inability to synthesize both T 2-weighted brain images that include the skull and FLuid Attenuated Inversion Recovery (FLAIR) images has been reported. The method described herein, called REPLICA, addresses these limitations. REPLICA is a supervised random forest image synthesis approach that learns a nonlinear regression to predict intensities of alternate tissue contrasts given specific input tissue contrasts. Experimental results include direct image comparisons between synthetic and real images, results from image analysis tasks on both synthetic and real images, and comparison against other state-of-the-art image synthesis methods. REPLICA is computationally fast, and is shown to be comparable to other methods on tasks they are able to perform. Additionally REPLICA has the capability to synthesize both T 2-weighted images of the full head and FLAIR images, and perform intensity standardization between different imaging datasets.
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Cites
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT
Martin Bland, J., & Altman, D. (1986) · The Lancet · doi:10.1016/s0140-6736(86)90837-8 · Cited by 47,965
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Cortical Surface-Based Analysis
Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910
A universal image quality index
Wang, Z., & Bovik, A. C. (2002) · IEEE Signal Processing Letters · doi:10.1109/97.995823 · Cited by 5,757
Current Methods in Medical Image Segmentation
Pham, D. L., Xu, C., & Prince, J. L. (2000) · Annual Review of Biomedical Engineering · doi:10.1146/annurev.bioeng.2.1.315 · Cited by 2,352
Unbiased average age-appropriate atlases for pediatric studies
Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., & Collins, D. L. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.07.033 · Cited by 2,290
Unbiased nonlinear average age-appropriate brain templates from birth to adulthood
Fonov, V., Evans, A., McKinstry, R. C., Almli, C. R., & Collins, D. L. (2009) · NeuroImage · doi:10.1016/s1053-8119(09)70884-5 · Cited by 2,257
Cited by
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Li, Y., & Ye, C. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04947-6_24
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Metadata
- Authors
- Jog, A., Carass, A., Roy, S., Pham, D. L., & Prince, J. L.
- Affiliations
- Johns Hopkins University (US)
- Henry M. Jackson Foundation (US)
- Corresponding author
- Jog, Amod
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2017-01
- Volume
- 35
- Pages
- 475-488
- ISSN
- 1361-8415 (ISSN-L)
- PMC
- PMC5099106
- OpenAlex
- W2517395172
- Cited by
- 212
- References
- 64
- Citations by year
- 2026: 12 · 2025: 24 · 2024: 29 · 2023: 25 · 2022: 20 · 2021: 39 · 2020: 25 · 2019: 14 · 2018: 14 · 2017: 9
- FWCI
- 10.99
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Medical Image Analysis (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIH (R21 EB012765)
- NIBIB (R01 EB017743)
- NINDS (R01 NS070906)
- Department of Defense
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Brain Tumor Detection and Classification
- Keywords
- MRI image synthesis, random forest regression, replicas, tissue contrast, pulse sequences
- MeSH terms
- Algorithms, Humans, Image Enhancement, Magnetic Resonance Imaging, Datasets as Topic
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