Hformer: highly efficient vision transformer for low-dose CT denoising

Shi Yu Zhang, Zhao Xuan Wang, Hai Bo Yang, Yi Lun Chen, Yang Li, Quan Pan, Hong Kai Wang, Cheng Xin Zhao

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

In this paper, we propose Hformer, a novel supervised learning model for low-dose computer tomography (LDCT) denoising. Hformer combines the strengths of convolutional neural networks for local feature extraction and transformer models for global feature capture. The performance of Hformer was verified and evaluated based on the AAPM-Mayo Clinic LDCT Grand Challenge Dataset. Compared with the former representative state-of-the-art (SOTA) model designs under different architectures, Hformer achieved optimal metrics without requiring a large number of learning parameters, with metrics of 33.4405 PSNR, 8.6956 RMSE, and 0.9163 SSIM. The experiments demonstrated designed Hformer is a SOTA model for noise suppression, structure preservation, and lesion detection.

Original languageEnglish
Article number61
JournalNuclear Science and Techniques
Volume34
Issue number4
DOIs
StatePublished - Apr 2023

Keywords

  • Auto-encoder
  • Convolutional neural networks
  • Deep learning
  • Image denoising
  • Low-dose CT
  • Medical image
  • Residual network
  • Self-attention

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