AI 中文总结
该研究提出嵌入反投影算子的双域U-Net，从无呼吸信号或投影分箱的自由呼吸扫描中重建运动分辨的4D CBCT，在模拟与临床数据上均表现出更优的肿瘤、食道可见性及更少运动伪影，实现了高质量4D重建。
AI 中文摘要
四维锥形束CT（4D CBCT）对胸部肿瘤的图像引导放射治疗至关重要，但其应用受限于较长的扫描时间，这会导致患者接受高剂量辐射，并产生运动或稀疏采样伪影。我们提出一种深度学习方法，用于从常规自由呼吸扫描中重建运动分辨的4D CBCT，无需呼吸信号或显式投影分箱。我们的卷积神经网络（CNN）以自由呼吸的3D CBCT投影为输入，预测最大吸气时的静态体积以及覆盖整个呼吸周期的10个位移向量场（DVF）。该网络对U-Net进行了扩展：编码器作用于滤波后的投影堆栈，解码器作用于体积域，跳跃连接被多个分辨率下不可训练的反投影函数取代，以在域间传递特征。该模型在模拟CBCT扫描上进行训练，并在11名未见过的模拟患者和13例临床自由呼吸扫描上进行评估。临床专家对另外两个模型（60秒和6秒扫描）的3例和2例扫描进行评估，将我们的4D重建的单相与参考3D SART-TV图像的肿瘤和食道可见性进行比较。专家更偏好我们方法的肿瘤可见性（59% vs 36%无偏好，5%参考）和食道可见性（47% vs 42%，11%）。在模拟数据上，图像质量与SART-TV相当（平均均方根误差：-1.19 HU，峰值信噪比：+0.09 dB，结构相似性指数：-0.009），同时实现了4D重建。在临床扫描上，我们的方法表现出比传统重建更清晰的动态结构（如横膈膜）和更少的运动条纹伪影。这种非患者特异性的CNN可从单次自由呼吸扫描预测静态体积和完整的4D呼吸运动模型，无需呼吸替代物或投影分箱，减少了运动伪影，同时增加了运动建模能力。
英文摘要
Four-dimensional cone beam CT (4D CBCT) is important for image-guided radiation therapy of thoracic cancers, but its use is limited by long scan times, causing high patient dose and motion/sparse-sampling artifacts. We propose a deep learning method for motion-resolved 4D CBCT reconstruction from conventional free-breathing scans, without a respiratory signal or explicit projection binning. Our CNN takes free-breathing 3D CBCT projections as input and predicts a static volume at maximum inhalation plus ten displacement vector fields (DVFs) spanning a breathing cycle. The network extends U-Net: the encoder acts on filtered projection stacks, the decoder acts in the volume domain, and skip connections are replaced with non-trainable back-projection functions at multiple resolutions to transfer features between domains. The model is trained on simulated CBCT scans and evaluated on 11 unseen simulated patients and 13 clinical free-breathing scans. Two additional models (60 s and 6 s scans) were evaluated by clinical experts on three and two scans, comparing single phases of our 4D reconstruction to reference 3D SART-TV images for tumor and esophagus visibility. Experts preferred our method for tumor visibility (59% vs. 36% no preference, 5% reference) and esophagus visibility (47% vs. 42%, 11%). On simulated data, image quality matched SART-TV (mean RMSE: -1.19 HU, PSNR: +0.09 dB, SSIM: -0.009) while enabling 4D reconstruction. On clinical scans, our method showed sharper dynamic structures (e.g., diaphragm) and fewer motion streak artifacts than traditional reconstruction. This non-patient-specific CNN predicts static volumes and full 4D respiratory motion models from a single free-breathing scan, without a respiratory surrogate or projection binning, reducing motion artifacts while adding motion-modeling capability.
Comments15 pages, 9 Figures
