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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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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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