427 research outputs found
Multi-Modality Generative Adversarial Networks with Tumor Consistency Loss for Brain MR Image Synthesis
Magnetic Resonance (MR) images of different modalities can provide
complementary information for clinical diagnosis, but whole modalities are
often costly to access. Most existing methods only focus on synthesizing
missing images between two modalities, which limits their robustness and
efficiency when multiple modalities are missing. To address this problem, we
propose a multi-modality generative adversarial network (MGAN) to synthesize
three high-quality MR modalities (FLAIR, T1 and T1ce) from one MR modality T2
simultaneously. The experimental results show that the quality of the
synthesized images by our proposed methods is better than the one synthesized
by the baseline model, pix2pix. Besides, for MR brain image synthesis, it is
important to preserve the critical tumor information in the generated
modalities, so we further introduce a multi-modality tumor consistency loss to
MGAN, called TC-MGAN. We use the synthesized modalities by TC-MGAN to boost the
tumor segmentation accuracy, and the results demonstrate its effectiveness.Comment: 5 pages, 3 figures, accepted to IEEE ISBI 202
Generating Diffusion MRI scalar maps from T1 weighted images using generative adversarial networks
Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive
microstructure assessment technique. Scalar measures, such as FA (fractional
anisotropy) and MD (mean diffusivity), quantifying micro-structural tissue
properties can be obtained using diffusion models and data processing
pipelines. However, it is costly and time consuming to collect high quality
diffusion data. Here, we therefore demonstrate how Generative Adversarial
Networks (GANs) can be used to generate synthetic diffusion scalar measures
from structural T1-weighted images in a single optimized step. Specifically, we
train the popular CycleGAN model to learn to map a T1 image to FA or MD, and
vice versa. As an application, we show that synthetic FA images can be used as
a target for non-linear registration, to correct for geometric distortions
common in diffusion MRI
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