HFGS: High-Frequency Information Guided Net for Multi-Regions Pseudo-CT Synthesis

Rongchang Zhao, Jianyu Qi, Rui Li, Teng Yang, Jiaxu Li, Jian Zhang, Zijian Zhang

Abstract: Computed tomography (CT) scans are clinically important in radiotherapy planning (RTP) for ROI contour delineation and radiation dose calculation. However, it involves significant radiation exposure, which can bring potential health problems. Nowadays, the synthesis of MR to CT provides an alternative to repetitive CT examinations. Although transformers are widely used for image synthesis, achieving effective multi-regions pseudo-CT synthesis from magnetic resonance (MR) images faces common and unique challenges: 1) The quadratic time complexity problem. While transformer facilitates long-range modeling in image synthesis, efficiently integrating the self-attention mechanism with 3D volume data remains an unresolved challenge. 2) The modal differences between MR and CT are significant, and the complex structural priors present within MR and CT make it difficult for transformer to learn effective mapping functions. To address these issues, we propose a high frequency-information guided net for multi-regions pseudo-CT synthesis (HFGS), efficiently generating multi-regions pseudo-CTs from different MR sequences. Our carefully designed 3D cascaded frequency transformer (CFT) serves as the synthesis module, utilizing element-wise product of frequency domain signals instead of matrix multiplication in spatial domain. This approach provides an efficient self-attention calculation method and improves synthesis efficiency. Additionally, to tackle the challenge of insufficient high-frequency information for high-quality decoding, we have designed a learnable guidance module to capture important structural priors within both the source and target modalities, guiding the synthesis module to produce high-quality pseudo-CTs. Our code will be available at https://github.com/qijianyu277/HFGS.