Deep residual learning in CT physics: scatter correction for spectral CT
Autor: | Peter Prinsen, Ravindra Manjeshwar, Jens Wiegert, Shiyu Xu |
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Rok vydání: | 2017 |
Předmět: |
Computation
Monte Carlo method FOS: Physical sciences 02 engineering and technology Iterative reconstruction Residual Convolutional neural network Physics - Medical Physics Imaging phantom 030218 nuclear medicine & medical imaging Convolution 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Medical Physics (physics.med-ph) Projection (set theory) Algorithm |
DOI: | 10.48550/arxiv.1708.04151 |
Popis: | Recently, spectral CT has been drawing a lot of attention in a variety of clinical applications primarily due to its capability of providing quantitative information about material properties. The quantitative integrity of the reconstructed data depends on the accuracy of the data corrections applied to the measurements. Scatter correction is a particularly sensitive correction in spectral CT as it depends on system effects as well as the object being imaged and any residual scatter is amplified during the non-linear material decomposition. An accurate way of removing scatter is subtracting the scatter estimated by Monte Carlo simulation. However, to get sufficiently good scatter estimates, extremely large numbers of photons are required, which may lead to unexpectedly high computational costs. Other approaches model scatter as a convolution operation using kernels derived using empirical methods. These techniques have been found to be insufficient in spectral CT due to their inability to sufficiently capture object dependence. In this work, we develop a deep residual learning framework to address both issues of computation simplicity and object dependency. A deep convolution neural network is trained to determine the scatter distribution from the projection content in training sets. In test cases of a digital anthropomorphic phantom and real water phantom, we demonstrate that with much lower computing costs, the proposed network provides sufficiently accurate scatter estimation. |
Databáze: | OpenAIRE |
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