CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT Denoising

Abstract

Low-dose computed tomography (LDCT) denoising is an important problem in CT research. Compared to the normal dose CT (NDCT), LDCT images are subjected to severe noise and artifacts. Recently in many studies, vision transformers have shown superior feature representation ability over convolutional neural networks (CNNs). However, unlike CNNs, the potential of vision transformers in LDCT denoising was little explored so far. To fill this gap, we propose a Convolution-free Token2Token Dilated Vision Transformer for low-dose CT denoising. The CTformer uses a more powerful token rearrangement to encompass local contextual information and thus avoids convolution. It also dilates and shifts feature maps to capture longer-range interaction. We interpret the CTformer by statically inspecting patterns of its internal attention maps and dynamically tracing the hierarchical attention flow with an explanatory graph. Furthermore, an overlapped inference mechanism is introduced to effectively eliminate the boundary artifacts that are common for encoder-decoder-based denoising models. Experimental results on Mayo LDCT dataset suggest that the CTformer outperforms the state-of-the-art denoising methods with a low computation overhead.

11 pages, 14 figures

References in corpus (3)

• Transformer in Transformer
• EfficientFormer: Vision Transformers at MobileNet Speed
• DuDoTrans: Dual-Domain Transformer Provides More Attention for Sinogram Restoration in Sparse-View CT Reconstruction

Cited by in corpus (6)

• Transformers in Healthcare: A Survey
• LIT-Former: Linking In-plane and Through-plane Transformers for Simultaneous CT Image Denoising and Deblurring
• CT-Mamba: A Hybrid Convolutional State Space Model for Low-Dose CT Denoising
• Spatially Covariant Lesion Segmentation
• Cross-domain Denoising for Low-dose Multi-frame Spiral Computed Tomography
• A Green Learning Approach to LDCT Image Restoration