Singh Khural B, Baer-Beck M, Fournie E, Stierstorfer K, Huang Y, Maier A (2022)
Publication Type: Journal article, Original article
Publication year: 2022
URI: https://iopscience.iop.org/article/10.1088/2057-1976/ac47fc
The problem of data truncation in Computed Tomography (CT) is caused
by the missing data when the patient exceeds the Scan Field of View (SFOV) of a CT
scanner. The reconstruction of a truncated scan produces severe truncation artifacts
both inside and outside the SFOV. We have employed a deep learning-based approach
to extend the field of view and suppress truncation artifacts. Thereby, our aim is
to generate a good estimate of the real patient data and not to provide a perfect
and diagnostic image even in regions beyond the SFOV of the CT scanner. This
estimate could then be used as an input to higher order reconstruction algorithms
[1]. To evaluate the influence of the network structure and layout on the results,
three convolutional neural networks (CNNs), in particular a general CNN called
ConvNet, an autoencoder, and the U-Net architecture have been investigated in this
paper. Additionally, the impact of L1, L2, structural dissimilarity and perceptual
loss functions on the neural network’s learning have been assessed and evaluated.
The evaluation of data set comprising 12 truncated test patients demonstrated that
the U-Net in combination with the structural dissimilarity loss showed the best
performance in terms of image restoration in regions beyond the SFOV of the CT
scanner. Moreover, this network produced the best mean absolute error, L1, L2, and
structural dissimilarity evaluation measures on the test set compared to other applied
networks. Therefore, it is possible to achieve truncation artifact removal using deep
learning techniques.
APA:
Singh Khural, B., Baer-Beck, M., Fournie, E., Stierstorfer, K., Huang, Y., & Maier, A. (2022). Deep Learning-based Extended Field of View Computed Tomography Image Reconstruction: Influence of Network Design on Image Estimation Outside the Scan Field of View. Biomedical Physics and Engineering Express. https://doi.org/10.1088/2057-1976/ac47fc
MLA:
Singh Khural, Bhupinder, et al. "Deep Learning-based Extended Field of View Computed Tomography Image Reconstruction: Influence of Network Design on Image Estimation Outside the Scan Field of View." Biomedical Physics and Engineering Express (2022).
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