11,809 research outputs found
An Unsupervised Learning Model for Deformable Medical Image Registration
We present a fast learning-based algorithm for deformable, pairwise 3D
medical image registration. Current registration methods optimize an objective
function independently for each pair of images, which can be time-consuming for
large data. We define registration as a parametric function, and optimize its
parameters given a set of images from a collection of interest. Given a new
pair of scans, we can quickly compute a registration field by directly
evaluating the function using the learned parameters. We model this function
using a convolutional neural network (CNN), and use a spatial transform layer
to reconstruct one image from another while imposing smoothness constraints on
the registration field. The proposed method does not require supervised
information such as ground truth registration fields or anatomical landmarks.
We demonstrate registration accuracy comparable to state-of-the-art 3D image
registration, while operating orders of magnitude faster in practice. Our
method promises to significantly speed up medical image analysis and processing
pipelines, while facilitating novel directions in learning-based registration
and its applications. Our code is available at
https://github.com/balakg/voxelmorph .Comment: 9 pages, in CVPR 201
Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images
Positron Emission Tomography (PET) is a functional imaging modality widely
used in neuroscience studies. To obtain meaningful quantitative results from
PET images, attenuation correction is necessary during image reconstruction.
For PET/MR hybrid systems, PET attenuation is challenging as Magnetic Resonance
(MR) images do not reflect attenuation coefficients directly. To address this
issue, we present deep neural network methods to derive the continuous
attenuation coefficients for brain PET imaging from MR images. With only Dixon
MR images as the network input, the existing U-net structure was adopted and
analysis using forty patient data sets shows it is superior than other Dixon
based methods. When both Dixon and zero echo time (ZTE) images are available,
we have proposed a modified U-net structure, named GroupU-net, to efficiently
make use of both Dixon and ZTE information through group convolution modules
when the network goes deeper. Quantitative analysis based on fourteen real
patient data sets demonstrates that both network approaches can perform better
than the standard methods, and the proposed network structure can further
reduce the PET quantification error compared to the U-net structure.Comment: 15 pages, 12 figure
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