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Proc SPIE Int Soc Opt Eng. 2019 Mar;10949. pii: 109493H. doi: 10.1117/12.2513089.

Evaluating the Impact of Intensity Normalization on MR Image Synthesis.

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Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA 21218.
F.M. Kirby Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA, 21205.
Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA 21218.


Image synthesis learns a transformation from the intensity features of an input image to yield a different tissue contrast of the output image. This process has been shown to have application in many medical image analysis tasks including imputation, registration, and segmentation. To carry out synthesis, the intensities of the input images are typically scaled-i.e., normalized-both in training to learn the transformation and in testing when applying the transformation, but it is not presently known what type of input scaling is optimal. In this paper, we consider seven different intensity normalization algorithms and three different synthesis methods to evaluate the impact of normalization. Our experiments demonstrate that intensity normalization as a preprocessing step improves the synthesis results across all investigated synthesis algorithms. Furthermore, we show evidence that suggests intensity normalization is vital for successful deep learning-based MR image synthesis.


brain MRI; image synthesis; intensity normalization

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