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CTformer: convolution-free Token2Token dilated vision transformer for low-dose CT denoising.

Dayang Wang1, Fenglei Fan2, Zhan Wu3

  • 1Department of Electrical and Computer Engineering, University of Massachusetts, Lowell, MA, United States of America.

Physics in Medicine and Biology
|February 28, 2023
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Summary

This study introduces the Convolution-free Token2Token Dilated Vision Transformer (CTformer) for low-dose computed tomography (LDCT) denoising. The CTformer effectively reduces noise and artifacts in LDCT images with high performance and low computational cost.

Keywords:
deep learninginterpretabilitylow-dose CT denoisingmedical imagingtransformer models

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence

Background:

  • Low-dose computed tomography (LDCT) images suffer from significant noise and artifacts.
  • Vision transformers show promise in image processing tasks, but their application in LDCT denoising is underexplored.

Purpose of the Study:

  • To explore the effectiveness of vision transformers for low-dose computed tomography (LDCT) denoising.
  • To propose a novel transformer-based model for LDCT denoising.

Main Methods:

  • A Convolution-free Token2Token Dilated Vision Transformer (CTformer) was developed, utilizing token rearrangement for local context and dilated/shifted feature maps for long-range interactions.
  • An overlapped inference mechanism was employed to mitigate boundary artifacts.
  • Model interpretability was assessed through attention map analysis and attention flow tracing.

Main Results:

  • The CTformer demonstrated superior performance compared to state-of-the-art denoising methods on the Mayo dataset.
  • The model achieved excellent denoising results with low computational overhead.

Conclusions:

  • The proposed CTformer model offers effective low-dose computed tomography (LDCT) denoising capabilities.
  • Its low computational cost and interpretability make it a promising tool for clinical applications.