Hformer: highly efficient vision transformer for low-dose CT denoising
Shiyu Zhang, Zhao-Xuan Wang, Haibo Yang, Yi-Lun Chen, Yang Li, Quan Pan, Hongkai Wang, Chengxin Zhao
Abstract
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.