Cited by 213Advanced MRI Techniques and ApplicationsMedical Image Segmentation TechniquesBrain Tumor Detection and Classification

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Random forest regression for magnetic resonance image synthesis

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Jog, Amod, Carass, Aaron, Roy, Snehashis, Pham, Dzung L., Prince, Jerry L.·2017·Medical Image Analysis

DOI 10.1016/j.media.2016.08.009PMC PMC5099106

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Abstract

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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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
ORCID: Jog, Amod
Carass, Aaron
Roy, Snehashis
Pham, Dzung L.
Prince, Jerry L.
Venue: Medical Image Analysis
Publisher: Elsevier BV
Published: 2017-01
Volume: 35
Pages: 475-488
ISSN: 1361-8415 (ISSN-L)
DOI: 10.1016/j.media.2016.08.009
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: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (am, 248 d)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
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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Random forest regression for magnetic resonance image synthesis

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