  Objective: To develop an automatic image normalization algorithm for
intensity correction of images from breast dynamic contrast-enhanced magnetic
resonance imaging (DCE-MRI) acquired by different MRI scanners with various
imaging parameters, using only image information. Methods: DCE-MR images of 460
subjects with breast cancer acquired by different scanners were used in this
study. Each subject had one T1-weighted pre-contrast image and three
T1-weighted post-contrast images available. Our normalization algorithm
operated under the assumption that the same type of tissue in different
patients should be represented by the same voxel value. We used four
tissue/material types as the anchors for the normalization: 1) air, 2) fat
tissue, 3) dense tissue, and 4) heart. The algorithm proceeded in the following
two steps: First, a state-of-the-art deep learning-based algorithm was applied
to perform tissue segmentation accurately and efficiently. Then, based on the
segmentation results, a subject-specific piecewise linear mapping function was
applied between the anchor points to normalize the same type of tissue in
different patients into the same intensity ranges. We evaluated the algorithm
with 300 subjects used for training and the rest used for testing. Results: The
application of our algorithm to images with different scanning parameters
resulted in highly improved consistency in pixel values and extracted radiomics
features. Conclusion: The proposed image normalization strategy based on tissue
segmentation can perform intensity correction fully automatically, without the
knowledge of the scanner parameters. Significance: We have thoroughly tested
our algorithm and showed that it successfully normalizes the intensity of
DCE-MR images. We made our software publicly available for others to apply in
their analyses.
