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

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.