title: MR image synthesis by contrast learning on neighborhood ensembles.
abstract: Automatic processing of magnetic resonance images is a vital part of neuroscience research. Yet even the best and most widely used medical image processing methods will not produce consistent results when their input images are acquired with different pulse sequences. Although intensity standardization and image synthesis methods have been introduced to address this problem, their performance remains dependent on knowledge and consistency of the pulse sequences used to acquire the images. In this paper, an image synthesis approach that first estimates the pulse sequence parameters of the subject image is presented. The estimated parameters are then used with a collection of atlas or training images to generate a new atlas image having the same contrast as the subject image. This additional image provides an ideal source from which to synthesize any other target pulse sequence image contained in the atlas. In particular, a nonlinear regression intensity mapping is trained from the new atlas image to the target atlas image and then applied to the subject image to yield the particular target pulse sequence within the atlas. Both intensity standardization and synthesis of missing tissue contrasts can be achieved using this framework. The approach was evaluated on both simulated and real data, and shown to be superior in both intensity standardization and synthesis to other established methods.
authors.affiliation: Department of Computer Science, The Johns Hopkins University, United States. Electronic address: amod@cs.jhu.edu.
authors.forename: Amod
authors.initials: A
authors.lastname: Jog
authors.affiliation: Department of Computer Science, The Johns Hopkins University, United States; Department of Electrical and Computer Engineering, The Johns Hopkins University, United States.
authors.forename: Aaron
authors.initials: A
authors.lastname: Carass
authors.affiliation: The Henry M. Jackson Foundation for the Advancement of Military Medicine, United States.
authors.forename: Snehashis
authors.initials: S
authors.lastname: Roy
authors.affiliation: The Henry M. Jackson Foundation for the Advancement of Military Medicine, United States.
authors.forename: Dzung L
authors.initials: DL
authors.lastname: Pham
authors.affiliation: Department of Electrical and Computer Engineering, The Johns Hopkins University, United States.
authors.forename: Jerry L
authors.initials: JL
authors.lastname: Prince
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted
keywords: Brain; Magnetic resonance imaging; Pulse sequence; Synthesis