用超声生成类CT图像，帮医生实时定位，减少辐射暴露。
3D Ultrasound-Derived Pseudo-CT Synthesis Using a Transformer-Augmented Residual Network for Real-Time Operator Guidance
- 用Transformer增强的3D残差网络，从超声图生成类CT图像。
- 在TRUSTED数据集上PSNR和SSIM均优于现有方法。
- 适合需实时引导的超声检查，尤其关注减少不必要的CT扫描。
计算机断层扫描（CT）对临床诊断和影像引导手术至关重要，但会暴露患者于电离辐射。超声（US）无辐射且普及率高，但高度依赖操作者，缺乏组织定量特征，常导致诊断不确定和不必要的CT检查。本文提出一种3D超声衍生伪CT（UD-pCT）框架，从超声图像生成类CT解剖参考体积，不追求精确的亨氏单位。利用TRUSTED数据集中配对的3D肾部超声与CT体积，通过基于地标的关键模态配准流程实现空间对齐，为监督训练提供高质量成对输入。所提瓶颈变压器残差U-Net3D（BT-ResUNet3D）模型采用3D残差编码器-解码器生成器，结合变压器瓶颈，有效建模细粒度局部解剖结构及长程体积分量依赖关系；3D条件补丁生成对抗网络判别器则强化合成伪CT体积的局部结构真实性。定量评估显示，该方法在结构保真度和感知图像质量方面优于现有基线模型。生成的UD-pCT体积可为操作者提供实时解剖参考，有望降低采集变异性和不必要的CT使用。本研究局限在于配对数据集相对较小，可能影响模型泛化能力。
原文摘要 · Abstract (English)
Computed tomography (CT) is indispensable for clinical diagnosis and image-guided interventions but exposes patients to ionizing radiation, motivating the development of safer imaging alternatives. Ultrasound (US) is non-ionizing and widely accessible; however, it is highly operator dependent and lacks quantitative tissue characterization, often leading to diagnostic uncertainty and unnecessary CT examinations. This work presents a 3D ultrasound-derived pseudo-CT (UD-pCT) framework that generates CT-like anatomical reference volumes inferred from US, without aiming to reproduce physically accurate Hounsfield Units. Paired 3D kidney US and CT volumes from the TRUSTED dataset are first spatially aligned using a landmark-based multimodal registration pipeline, creating high-quality paired inputs for supervised training of an adversarial framework. The proposed Bottleneck Transformer Residual U-Net3D (BT-ResUNet3D) model employs a 3D residual encoder-decoder generator augmented with a transformer bottleneck, enabling effective modeling of fine-grained local anatomical structures as well as long-range volumetric dependencies, while a 3D Conditional PatchGAN discriminator enforces local structural realism in the synthesized pseudo-CT volumes. Quantitative evaluation using PSNR and SSIM demonstrates that the proposed method outperforms established baselines in structural fidelity and perceptual image quality. The UD-pCT volumes provide real-time anatomical reference for operator guidance, potentially reducing acquisition variability and unnecessary CT use. A limitation of this study is the relatively small paired dataset, which may limit the generalizability of the proposed model.
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