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Title: Generalizability test of a deep learning-based CT image denoising method
Deep learning (DL) has been increasingly explored in low-dose CT image denoising. DL products have also been submitted to the FDA for premarket clearance. While having the potential to improve image quality over the filtered back projection method (FBP) and produce images quickly, generalizability of DL approaches is a major concern because the performance of a DL network can depend highly on the training data. In this work we take a residual encoder-decoder convolutional neural network (REDCNN)-based CT denoising method as an example. We investigate the effect of the scan parameters associated with the training data on the performance of this DL-based CT denoising method and identifies the scan parameters that may significantly impact its performance generalizability. This abstract particularly examines these three parameters: reconstruction kernel, dose level and slice thickness. Our preliminary results indicate that the DL network may not generalize well between FBP reconstruction kernels, but is insensitive to slice thickness for slice-wise denoising. The results also suggest that training with mixed dose levels improves denoising performance.
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The 6th International Conference on Image Formation in X-Ray Computed Tomography
Sponsoring Org:
National Science Foundation
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