针对截断CBCT数据，专注重建特定结构的深度学习方法
Task-Specific Data Preparation for Deep Learning to Reconstruct Structures of Interest from Severely Truncated CBCT Data
- 设计任务导向的数据准备，让网络聚焦关注结构
- 相比传统训练，新方法可100%准确重建所有肋骨
- 适合需要精准重建特定解剖结构的临床场景
锥形束计算机断层扫描（CBCT）广泛应用于介入手术和放射治疗。由于平板探测器尺寸有限，部分解剖结构可能超出视野（FOV），限制了其临床应用。尽管已有深度学习方法用于扩展多层CT的视野，但在移动式CBCT系统中，投影数据严重截断，网络难以恢复视野外所有结构。在某些应用中，仅需关注特定结构（如肝肺肿瘤穿刺路径规划中的肋骨）。本文提出一种任务特定的数据准备方法，自动引导网络聚焦于感兴趣结构而非全部结构。初步实验表明，采用常规训练的Pix2pixGAN在严重截断的CBCT数据上易产生假阳性和假阴性肋骨重建，而使用该方法训练的Pix2pixGAN能可靠重建全部肋骨。该方法有望拓展CBCT的临床应用范围。
原文摘要 · Abstract (English)
Cone-beam computed tomography (CBCT) is widely used in interventional surgeries and radiation oncology. Due to the limited size of flat-panel detectors, anatomical structures might be missing outside the limited field-of-view (FOV), which restricts the clinical applications of CBCT systems. Recently, deep learning methods have been proposed to extend the FOV for multi-slice CT systems. However, in mobile CBCT system with a smaller FOV size, projection data is severely truncated and it is challenging for a network to restore all missing structures outside the FOV. In some applications, only certain structures outside the FOV are of interest, e.g., ribs in needle path planning for liver/lung cancer diagnosis. Therefore, a task-specific data preparation method is proposed in this work, which automatically let the network focus on structures of interest instead of all the structures. Our preliminary experiment shows that Pix2pixGAN with a conventional training has the risk to reconstruct false positive and false negative rib structures from severely truncated CBCT data, whereas Pix2pixGAN with the proposed task-specific training can reconstruct all the ribs reliably. The proposed method is promising to empower CBCT with more clinical applications.
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