Efficient Metal Artifact Reduction Transformer for Dental Cone Beam Computed Tomography Images

Core Concepts

An efficient Transformer-based model, MARformer, is proposed to effectively reduce metal artifacts in dental cone beam computed tomography (CBCT) images by exploiting global correlation information while maintaining computational efficiency.

Abstract

The paper presents MARformer, an efficient Transformer-based model for metal artifact reduction (MAR) in dental cone beam computed tomography (CBCT) images. The key components of MARformer are: Dimension-Reduced Self-Attention (DRSA) module: This module reduces the computational complexity of the standard multi-head self-attention in Transformers by computing the similarity matrix along the channel dimension instead of the spatial dimension. This allows MARformer to exploit global correlation information effectively while being more efficient. Patch-wise Perceptive Feed Forward Network (P2FFN): This module is introduced after each DRSA module to perceive local image information and aid in fine-grained restoration of the CBCT images. The proposed MARformer is evaluated on a large-scale dataset containing synthetic and real-world metal-artifact degraded dental CBCT images. Experimental results show that MARformer outperforms previous MAR methods and other image restoration Transformers in terms of both quantitative metrics (PSNR, SSIM) and visual quality, while being more computationally efficient. The authors also conduct ablation studies to analyze the impact of the DRSA and P2FFN modules on the performance of MARformer. The results demonstrate the effectiveness of the proposed components in achieving efficient and high-quality metal artifact reduction for dental CBCT images.

Stats

The input MA images have an average PSNR of 25.72 dB and SSIM of 0.7207. MARformer-L achieves a PSNR of 43.11 dB and SSIM of 0.9789 on the test set, outperforming previous MAR methods and other Transformer-based restoration models. MARformer-T, the lightweight version, achieves similar performance to Uformer-T but with only 0.40M parameters and 12.82G FLOPs, compared to 5.24M parameters and 25.39G FLOPs for Uformer-T.

Quotes

"Our MARformer-L well recovers the teeth shapes and obtains higher PSNR and SSIM results than the other comparison methods." "Experiments on a large-scale dataset containing CBCT images with synthetic and real-world metal artifacts demonstrate that, our MARformer outperforms the state-of-the-art MAR methods and two image restoration Transformers on both objective metrics and visual quality."

Key Insights Distilled From

MARformer: An Efficient Metal Artifact Reduction Transformer for Dental CBCT Images

by Yuxuan Shi,J... at arxiv.org 04-19-2024

https://arxiv.org/pdf/2311.09590.pdf

Deeper Inquiries

How can the proposed MARformer be extended to handle other types of medical imaging modalities beyond dental CBCT

The proposed MARformer can be extended to handle other types of medical imaging modalities beyond dental CBCT by adapting the architecture and training process to suit the specific characteristics of different imaging modalities. For instance, for MRI images, which are known to have different noise patterns and artifacts compared to CT images, the MARformer can be modified to incorporate domain-specific knowledge and preprocessing steps. Additionally, the training data for the MARformer can be augmented with a diverse range of medical imaging datasets to ensure robust performance across various modalities. By fine-tuning the model on different types of medical images and optimizing the hyperparameters accordingly, the MARformer can be effectively applied to tasks such as MRI artifact reduction, ultrasound image enhancement, and more.

What are the potential challenges in applying Transformer-based models for metal artifact reduction in clinical settings, and how can they be addressed

One potential challenge in applying Transformer-based models for metal artifact reduction in clinical settings is the computational complexity and resource requirements of these models. Transformers are known to be computationally intensive, which can be a limitation in real-time clinical applications where speed and efficiency are crucial. To address this challenge, model optimization techniques such as quantization, pruning, and distillation can be employed to reduce the model size and inference time without compromising performance. Additionally, leveraging hardware accelerators like GPUs or TPUs can help speed up the inference process. Another challenge is the interpretability of Transformer models, which is crucial in medical imaging applications. Techniques such as attention visualization and saliency mapping can be used to explain the model's decisions and enhance trust in the clinical setting.

What other applications in the medical imaging domain could benefit from the efficient and effective Transformer architecture developed in this work

The efficient and effective Transformer architecture developed in this work can benefit various applications in the medical imaging domain beyond metal artifact reduction. One potential application is image segmentation, where Transformers can be used to accurately delineate anatomical structures and lesions in medical images. The self-attention mechanism in Transformers enables capturing long-range dependencies, making them well-suited for tasks like organ segmentation and tumor detection. Additionally, the Transformer architecture can be applied to image registration, where aligning images from different modalities or time points is essential for treatment planning and monitoring. By leveraging the global context information captured by Transformers, accurate and robust image registration can be achieved, leading to improved clinical outcomes.