@inproceedings{bb7d0a86a6ef400696bd8617a967a725,
title = "Evaluating the impact of intensity normalization on MR image synthesis",
abstract = "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.",
keywords = "brain MRI, image synthesis, intensity normalization",
author = "Reinhold, {Jacob C.} and Dewey, {Blake E.} and Aaron Carass and Prince, {Jerry L.}",
note = "Funding Information: This work was supported in part by the NIH/NINDS grant R01-NS070906 and by the National MS Society grant RG-1507-05243. Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; Medical Imaging 2019: Image Processing ; Conference date: 19-02-2019 Through 21-02-2019",
year = "2019",
doi = "10.1117/12.2513089",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Angelini, {Elsa D.} and Angelini, {Elsa D.} and Angelini, {Elsa D.} and Landman, {Bennett A.}",
booktitle = "Medical Imaging 2019",
}