@inproceedings{725e9ba1307b4a82ae6e70f323eca8f5,
title = "Using image synthesis for multi-channel registration of different image modalities",
abstract = "This paper presents a multi-channel approach for performing registration between magnetic resonance (MR) images with different modalities. In general, a multi-channel registration cannot be used when the moving and target images do not have analogous modalities. In this work, we address this limitation by using a random forest regression technique to synthesize the missing modalities from the available ones. This allows a single channel registration between two different modalities to be converted into a multi-channel registration with two mono-modal channels. To validate our approach, two openly available registration algorithms and five cost functions were used to compare the label transfer accuracy of the registration with (and without) our multi-channel synthesis approach. Our results show that the proposed method produced statistically significant improvements in registration accuracy (at an α level of 0.001) for both algorithms and all cost functions when compared to a standard multi-modal registration using the same algorithms with mutual information.",
keywords = "Image synthesis, Magnetic resonance imaging, Multi-channel image registration, Multi-modal image registration",
author = "Min Chen and Amog Jog and Aaron Carass and Prince, {Jerry L.}",
note = "Publisher Copyright: {\textcopyright} 2015 SPIE.; Medical Imaging 2015: Image Processing ; Conference date: 24-02-2015 Through 26-02-2015",
year = "2015",
doi = "10.1117/12.2082373",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Styner, {Martin A.} and Sebastien Ourselin",
booktitle = "Medical Imaging 2015",
}