Technical Note: Phantom-based training framework for convolutional neural network CT noise reduction

Nathan R. Huber, Andrew D. Missert, Hao Gong, Shuai Leng, Lifeng Yu, Cynthia H. McCollough

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Deep artificial neural networks such as convolutional neural networks (CNNs) have been shown to be effective models for reducing noise in CT images while preserving anatomic details. A practical bottleneck for developing CNN-based denoising models is the procurement of training data consisting of paired examples of high-noise and low-noise CT images. Obtaining these paired data are not practical in a clinical setting where the raw projection data is not available. This work outlines a technique to optimize CNN denoising models using methods that are available in a routine clinical setting. Purpose: To demonstrate a phantom-based training framework for CNN noise reduction that can be efficiently implemented on any CT scanner. Methods: The phantom-based training framework uses supervised learning in which training data are synthesized using an image-based noise insertion technique. Ten patient image series were used for training and validation (9:1) and noise-only images obtained from anthropomorphic phantom scans. Phantom noise-only images were superimposed on patient images to imitate low-dose CT images for use in training. A modified U-Net architecture was used with mean-squared-error and feature reconstruction loss. The training framework was tested for clinically indicated whole-body-low-dose CT images, as well as routine abdomen-pelvis exams for which projection data was unavailable. Performance was assessed based on root-mean-square error, structural similarity, line profiles, and visual assessment. Results: When the CNN was tested on five reserved quarter-dose whole-body-low-dose CT images, noise was reduced by 75%, root-mean-square-error reduced by 34%, and structural similarity increased by 60%. Visual analysis and line profiles indicated that the method significantly reduced noise while maintaining spatial resolution of anatomic features. Conclusion: The proposed phantom-based training framework demonstrated strong noise reduction while preserving spatial detail. Because this method is based within the image domain, it can be easily implemented without access to projection data.

Original languageEnglish (US)
JournalMedical physics
DOIs
StateAccepted/In press - 2022

Keywords

  • deep learning
  • noise reduction
  • phantom

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

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