Abstract
Time-resolved imaging becomes popular in radiotherapy in that it significantly reduces blurring artifacts in volumetric images reconstructed from a set of 2D X-ray projection data. We aim at developing a neural network (NN) based machine learning algorithm that allows for reconstructing an instantaneous image from a single projection. In our approach, each volumetric image is represented as a deformation of a chosen reference image, in which the deformation is modeled as a linear combination of a few basis functions through principal component analysis (PCA). Based on this PCA deformation model, we train an ensemble of neural networks to find a mapping from a projection image to PCA coefficients. For image reconstruction, we apply the learned mapping on an instantaneous projection image to obtain the PCA coefficients, thus getting a deformation. Then, a volumetric image can be reconstructed by applying the deformation on the reference image. Experimentally, we show promising results on a set of simulated data.
Supported by the National Science Foundation’s Enriched Doctoral Training Program, DMS grant #1514808.
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Notes
- 1.
The projected image does not contain structural information, so CNN does not work very well.
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Rouf, S., Shen, C., Cao, Y., Davis, C., Jia, X., Lou, Y. (2020). A Neural Network Approach for Image Reconstruction from a Single X-Ray Projection. In: Zheng, Y., Williams, B., Chen, K. (eds) Medical Image Understanding and Analysis. MIUA 2019. Communications in Computer and Information Science, vol 1065. Springer, Cham. https://doi.org/10.1007/978-3-030-39343-4_18
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