Improving image quality of sparse-view lung tumor CT images with U-Net

TL;DR

The benefit of U-Net postprocessing for regular CT screenings of patients with lung metastasis to increase the IQ and diagnostic confidence while reducing the dose is demonstrated and sparse-projection-view streak artifacts reduce the quality and usability of sparse-view CT images.

Abstract

Background: We aimed at improving image quality (IQ) of sparse-view computed tomography (CT) images using a U-Net for lung metastasis detection and determining the best tradeoff between number of views, IQ, and diagnostic confidence. Methods: CT images from 41 subjects aged 62.8 $\pm$ 10.6 years (mean $\pm$ standard deviation), 23 men, 34 with lung metastasis, 7 healthy, were retrospectively selected (2016-2018) and forward projected onto 2,048-view sinograms. 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. A dual-frame U-Net was trained and evaluated for each subsampling level on 8,658 images from 22 diseased subjects. A representative image per scan was selected from 19 subjects (12 diseased, 7 healthy) for a single-blinded multireader study. These slices, for all levels of subsampling, with and without U-Net postprocessing, were presented to three readers. IQ and diagnostic confidence were ranked using predefined scales. Subjective nodule segmentation was evaluated using sensitivity and Dice similarity coefficient (DSC); clustered Wilcoxon signed-rank test was used. Results: The 64-projection sparse-view images resulted in 0.89 sensitivity and 0.81 DSC, while their counterparts, postprocessed with the U-Net, had improved metrics (0.94 sensitivity and 0.85 DSC) (p = 0.400). Fewer views led to insufficient IQ for diagnosis. For increased views, no substantial discrepancies were noted between sparse-view and postprocessed images. Conclusions: Projection views can be reduced from 2,048 to 64 while maintaining IQ and the confidence of the radiologists on a satisfactory level.

Figures (5)

• Figure 1: The architecture of the dual-frame U-Net. The model takes as input the unprocessed sparse-view images and outputs the pure artifact residual image. An example of 16 projection sparse-view input and corresponding residual output is shown. The number of channels is provided above each layer.
• Figure 2: An example computed tomography (CT) image reconstructed with full-view and sparse-view projections, with and without postprocessing by the dual-frame U-Net. The image on the left demonstrates the ground truth full-view image without postprocessing. The top row shows the CT image reconstructed with different sparse-view projections without postprocessing. The bottom row depicts the respective sparse-view images postprocessed by the U-Net model for each projection view. The region of interest (blue box) shows the metastasis (highlighted by the yellow arrow). All images are clipped to the lung window and include an iodined contrast medium. Scale bar in the full-view image = $5 cm$ .
• Figure 3: Mean over image quality (a), diagnostic confidence (b), severity of artifacts (c), and Dice similarity coefficient values (d) for lung nodule segmentations for 19 sparse-view images with (processed) and without postprocessing (sparse) by the dual-frame U-Net, labeled by three readers (n = 57). Scales defined for all labels are given in Tables \ref{['tab:labels']} and \ref{['tab:labels_artifact']}.
• Figure 4: Confusion matrices for sparse-view CT images and their postprocessed counterpart images for all projection views were calculated over 19 subject-wise images presented to three readers (n = 57).
• Figure 5: Examples of metastasis segmentations. A correctly marked nodule, true positive (TP), and two incorrectly segmented regions, namely false negative (FN) and false positive (FP), are shown. FP refers to the case where the perceived metastasis was nonexistent. FN refers to the case where the perceived nodule had no overlap with the ground truth segmentation. The top row shows the overlay of the ground truth segmentation (yellow) and the segmentation marked by the reader (blue) over the full-view image. The bottom row shows the sparse-view image, reconstructed from 16 projection views with or without postprocessing, presented to the readers for marking lung nodules. All slices are clipped to the lung window and include an iodined contrast medium. Scale bar = $5 cm$.