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Improving image quality of sparse-view lung tumor CT images with U-Net
Six corresponding sparse-view CT data subsets at varying levels of undersampling were reconstructed from sinograms using filtered backprojection with 16, 32, 64, 128, 256, and 512 views.
Improving Automated Hemorrhage Detection in Sparse-view Computed Tomography via Deep Convolutional Neural Network based Artifact Reduction
Purpose: Sparse-view computed tomography (CT) is an effective way to reduce dose by lowering the total number of views acquired, albeit at the expense of image quality, which, in turn, can impact the ability to detect diseases.
Optimizing Convolutional Neural Networks for Chronic Obstructive Pulmonary Disease Detection in Clinical Computed Tomography Imaging
Comparably, with input images manually adjusted to the emphysema window, the DenseNet model predicted COPD with a mean AUC of 0. 86 [0. 82, 0. 89].
WNet: A data-driven dual-domain denoising model for sparse-view computed tomography with a trainable reconstruction layer
We investigate the performance of the network on sparse-view chest CT scans, and we highlight the added benefit of having a trainable reconstruction layer over the more conventional fixed ones.
Per-Pixel Lung Thickness and Lung Capacity Estimation on Chest X-Rays using Convolutional Neural Networks
Additionally, we predicted the lung thicknesses on the synthetic test set, where the mean-absolute error between the total volumes was 0. 19 liter with a positive correlation (r = 0. 99).
