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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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Learned Regularizers and Geometry for Image Denoising
Recent frameworks for image denoising have demonstrated that it can be more productive to recover an image from a smoothed version of some geometric feature of the image rather than denoise the image directly. Improvements can be found both with respect to image quality metrics as well as the preservation of fine details. The challenge in working with this data is that mathematically sound mechanisms developed for handling natural image data do not necessarily apply, and this data itself can be quite ill behaved. In this work we learn both ‘geometric’ or nonlinear higher order features and corresponding regularizers. These approaches show improvement over recent modelbased deep learning (DL) image denoising methods both with respect to image quality metrics as well as the preservation of fine features. Furthermore, the proposed approach for enhancing DL architectures by incorporating geometrically-inspired features is motivated by and has the potential to feed back into mathematically sound models for solving a variety of problems in image processing.
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- Award ID(s):
- 1821342
- PAR ID:
- 10381015
- Date Published:
- Journal Name:
- British Machine Vision Conference
- Page Range / eLocation ID:
- 1-13
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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