Random forest regression for magnetic resonance image synthesis.
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 T2-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 T2-weighted images of the full head and FLAIR images, and perform intensity standardization between different imaging datasets.
Dept. of Computer Science, The Johns Hopkins University, United States. Electronic address: amod@cs.jhu.edu.
Amod
A
Jog
Dept. of Computer Science, The Johns Hopkins University, United States; Dept. of Electrical and Computer Engineering, The Johns Hopkins University, United States.
Aaron
A
Carass
The Henry M. Jackson Foundation for the Advancement of Military Medicine, United States.
Snehashis
S
Roy
The Henry M. Jackson Foundation for the Advancement of Military Medicine, United States.
Dzung L
DL
Pham
Dept. of Electrical and Computer Engineering, The Johns Hopkins University, United States.
Jerry L
JL
Prince
D000465:Algorithms; D066264:Datasets as Topic; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*
Image enhancement; Image synthesis; MRI; Neuroimaging; Random forests