A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction (1610.09736v3)

Abstract: Due to the potential risk of inducing cancers, radiation dose of X-ray CT should be reduced for routine patient scanning. However, in low-dose X-ray CT, severe artifacts usually occur due to photon starvation, beamhardening, etc, which decrease the reliability of diagnosis. Thus, high quality reconstruction from low-dose X-ray CT data has become one of the important research topics in CT community. Conventional model-based denoising approaches are, however, computationally very expensive, and image domain denoising approaches hardly deal with CT specific noise patterns. To address these issues, we propose an algorithm using a deep convolutional neural network (CNN), which is applied to wavelet transform coefficients of low-dose CT images. Specifically, by using a directional wavelet transform for extracting directional component of artifacts and exploiting the intra- and inter-band correlations, our deep network can effectively suppress CT specific noises. Moreover, our CNN is designed to have various types of residual learning architecture for faster network training and better denoising. Experimental results confirm that the proposed algorithm effectively removes complex noise patterns of CT images, originated from the reduced X-ray dose. In addition, we show that wavelet domain CNN is efficient in removing the noises from low-dose CT compared to an image domain CNN. Our results were rigorously evaluated by several radiologists and won the second place award in 2016 AAPM Low-Dose CT Grand Challenge. To the best of our knowledge, this work is the first deep learning architecture for low-dose CT reconstruction that has been rigorously evaluated and proven for its efficacy.

• The paper presents a novel deep CNN using directional wavelets to enhance low-dose CT image quality and reduce noise.
• It employs a 24-layer residual network with bypass connections that improves PSNR and NRMSE metrics over conventional methods.
• Experimental validation, including radiologist assessments and competitive performance, confirms its superior efficiency and accuracy.

Deep Convolutional Neural Network using Directional Wavelets for Low-dose X-ray CT Reconstruction

This paper presents a novel deep learning algorithm aimed at enhancing the quality of low-dose X-ray computed tomography (CT) scans by leveraging a deep convolutional neural network (CNN) in conjunction with directional wavelets. The proposed methodology is motivated by the increasing importance of minimizing radiation exposure during routine CT scans, without compromising diagnostic accuracy.

Methodology

The proposed algorithm utilizes a deep CNN applied to the wavelet transform coefficients of low-dose CT images. Specifically, a directional wavelet transform is employed to capture the directional components of noise and artifacts, allowing the network to exploit intra- and inter-band correlations effectively. This approach addresses CT-specific noise patterns that conventional image-domain de-noising algorithms struggle to manage.

The CNN architecture itself is designed with a residual learning framework, incorporating both external low-frequency band bypass paths and internal bypass connections to enhance training efficiency and performance. The network consists of 24 convolution layers, batch normalization, and rectified linear unit (ReLU) layers, organized into six modules with a concatenation layer to ensure effective gradient back-propagation.

Results

Experimental validation of the proposed algorithm was performed using data from the 2016 Low-Dose CT Grand Challenge. The paper's results demonstrate that the proposed method significantly reduces noise in low-dose CT images, preserving essential diagnostic details. Quantitative metrics such as Peak Signal-to-Noise Ratio (PSNR) and Normalized Root Mean Square Error (NRMSE) were used to evaluate performance, showing marked improvements over conventional methods.

Radiologists from the Mayo Clinic rigorously evaluated the denoised images, which led to the algorithm winning second place in the competition. The research also confirms that the proposed wavelet-domain CNN outperforms traditional image-domain CNNs and model-based iterative reconstruction (MBIR) methods, especially in terms of computational efficiency and noise suppression.

Implications and Future Directions

The significant advantage of the proposed network over traditional MBIR methods lies in its ability to scale with larger datasets. While MBIR algorithms typically train only a few parameters, the deep learning approach benefits from training on extensive datasets, thereby capturing complex noise characteristics more effectively. This capability offers considerable potential for integrating organ, protocol, and hardware-specific noise characteristics, paving the way for more personalized and accurate diagnostic imaging.

Future developments could include adapting the network for varying dose levels using transfer learning techniques, enabling it to handle different noise distributions without extensive re-training. Additionally, exploration of projection domain de-noising could further improve the algorithm's versatility, although challenges related to data cross-correlations need to be addressed.

The paper suggests that the deep-learning approach may offer new pathways for low-dose CT research, potentially setting a new standard in the field by providing a balance between image quality and computational efficiency. Future work could also focus on refining the texture representation to align more closely with radiologists' expectations and improving network architectures for even faster processing times.

Overall, this paper contributes significantly to advancements in low-dose CT reconstruction, providing a feasible and efficient alternative to conventional de-noising and MBIR methods. By leveraging the power of deep learning and wavelet transforms, it opens up new possibilities for improving diagnostic accuracy while minimizing patient exposure.