Dr. Tianling Lyu Profile

Dr. Tianling Lyu

at Zhejiang Lab

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Proceedings Article | 20 November 2024 Paper

Frequency space mamba enhanced bidirectional generative network for dual-source CBCT

Chang Liu, Yikun Zhang, Yiyuan Tao, Tianling Lyu, Yan Xi, Yang Chen

Proceedings Volume 13242, 132420L (2024) https://doi.org/10.1117/12.3033807

KEYWORDS: Cone beam computed tomography, X-ray computed tomography, Data modeling, Computed tomography, Biological imaging, Deep learning

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As DECT becomes widely accepted in the field of diagnostic radiology, there is growing interest in using dual-energy imaging to improve other scenarios. In this context, a new mobile dual-source dual-energy CBCT is being developed for scenarios such as radiotherapy and interventional radiology. The device performs dual-energy measurements by utilizing two X-ray sources mounted side-by-side in the z-axis direction, causing the problem of a mismatch in the fields of view of high-energy and low-energy sources in the z-axis. To solve this problem, this study proposes a method based on deep learning to generate high-energy and low-energy CT images in the missing fields of view. This method can generate high-energy (or low-energy) images from low-energy (or high-energy) images, and then complete the information in the missing fields of view. Furthermore, to enhance the quality of the generated images, a plug-and-play frequency-domain Mamba module is designed to extract frequency-domain features in the latent space, and then the redundant feature maps are filtered out through the designed frequency channel filtering module so that the model can pay more attention to learn and extract the effective features. Experimental results on the simulated data show that the proposed method can effectively generate the missing low- and high-energy CT images, and the SSIM, PSNR, and MAE are up to 99.3%, 48.1dB, and 6.3HU, respectively. Moreover, the generated images could maintain good continuity in the z-axis, which means that our method can effectively ensure the consistency in the fields of view of dual sources. In addition, our model can be further fine-tuned online using the paired dual-energy data in the overlap fields of view when dealing with data from unseen patients, constructing the patient-specific model to ensure the robustness against different samples.

Proceedings Article | 20 November 2024 Paper

DPD-Net: a dual-domain progressive diffusion network for joint limited-angle and metal artifact removal

Yijie Shi, Tianling Lyu, Xusheng Zhang, Xinyun Zhong, Yang Yang, Pinzheng Zhang, Zhan Wu, Yang Chen

Proceedings Volume 13242, 132420M (2024) https://doi.org/10.1117/12.3034419

KEYWORDS: Metals, Diffusion, Education and training, X-ray computed tomography, Image processing, Data modeling, Computed tomography, Image analysis, Ablation

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Computed Tomography (CT) is a high-precision medical imaging technique that utilizes X-rays and computer reconstruction to provide detailed three-dimensional images of human anatomy. It is used for clinical diagnosis and treatment. Non-ideal scanning conditions often occur, including the presence of metal implants in the human body and limited-angle scanning. These non-ideal conditions result in serious metal artifacts and limited-angle artifacts. To address the above challenge, in this paper, we propose a novel deep dual-domain progressive diffusion network, namely DPD-Net, to jointly suppress metal artifact and limited-angle artifact for the first time. DPD-Net leverages the advantage of dual-domain strategy for limited-angle artifact suppression in image-domain and metal trace inpainting in sinogram-domain simultaneously. To sufficiently solve dual-artifact problem, the dual-domain generative diffusion models are designed for data distribution learning. The proposed DPD-Net is trained and evaluated on a publicly available dataset. Extensive experimental results validate that the proposed method outperforms the state-of-the-art competing methods.

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