标题： 用于全身磁共振成像到计算机断层扫描模态转换的三维小波潜在扩散模型 显示英文标题

标题： 3D Wavelet Latent Diffusion Model for Whole-Body MR-to-CT Modality Translation

摘要： 磁共振（MR）成像在现代临床诊断中起着至关重要的作用。它越来越多地被整合到先进的治疗流程中，例如混合正电子发射断层扫描/磁共振（PET/MR）成像和仅磁共振放疗。这些集成方法在准确估计辐射衰减方面至关重要，这通常通过从MR扫描合成计算机断层扫描（CT）图像以生成衰减图来实现。然而，现有的全身成像MR到CT合成方法通常存在生成的CT图像与输入MR图像之间空间对齐不良，以及图像质量不足，无法可靠地用于下游临床任务。在本文中，我们提出了一种新颖的三维小波潜在扩散模型（3D-WLDM），该模型通过在学习的潜在空间中执行模态转换来解决这些限制。通过将小波残差模块纳入编码器-解码器架构，我们增强了图像和潜在空间中细尺度特征的捕捉和重建。为了在扩散过程中保持解剖完整性，我们分离了结构特性和模态特定特征，并将结构部分固定以防止变形。我们还在扩散模型中引入了双跳跃连接注意力机制，使生成的高分辨率CT图像在骨结构和软组织对比度方面有更优的表现。 显示英文摘要

摘要： Magnetic Resonance (MR) imaging plays an essential role in contemporary clinical diagnostics. It is increasingly integrated into advanced therapeutic workflows, such as hybrid Positron Emission Tomography/Magnetic Resonance (PET/MR) imaging and MR-only radiation therapy. These integrated approaches are critically dependent on accurate estimation of radiation attenuation, which is typically facilitated by synthesizing Computed Tomography (CT) images from MR scans to generate attenuation maps. However, existing MR-to-CT synthesis methods for whole-body imaging often suffer from poor spatial alignment between the generated CT and input MR images, and insufficient image quality for reliable use in downstream clinical tasks. In this paper, we present a novel 3D Wavelet Latent Diffusion Model (3D-WLDM) that addresses these limitations by performing modality translation in a learned latent space. By incorporating a Wavelet Residual Module into the encoder-decoder architecture, we enhance the capture and reconstruction of fine-scale features across image and latent spaces. To preserve anatomical integrity during the diffusion process, we disentangle structural and modality-specific characteristics and anchor the structural component to prevent warping. We also introduce a Dual Skip Connection Attention mechanism within the diffusion model, enabling the generation of high-resolution CT images with improved representation of bony structures and soft-tissue contrast.