用自回归模型预测放疗中器官运动，更准更适应个体差异。
Patient-Specific Autoregressive Models for Organ Motion Prediction in Radiotherapy
- 将器官运动建模为自回归过程，利用历史相位预测未来运动。
- 在50名患者1300多个CT相位上测试，肺和心脏运动预测精度超现有方法。
- 适合需要精准放疗规划的临床场景，尤其对呼吸相关运动敏感者。
放疗通常持续较长时间，期间患者因呼吸等生理因素导致器官运动。治疗前准确预测和建模该运动对确保精确放疗至关重要。然而，现有预治疗器官运动预测方法主要依赖主成分分析（PCA）进行形变分析，严重依赖配准质量，且难以捕捉运动的周期性时序动态。本文观察到器官运动预测本质上类似自回归过程，该技术广泛应用于自然语言处理中，即根据先前输入预测下一个输出，与预测未来器官运动阶段的目标天然契合。基于此，我们将器官运动预测重构为自回归过程，以更好捕捉患者特异性运动模式。具体而言，治疗前获取每位患者的4D CT扫描，每条序列包含多个3D CT相位，将这些相位输入自回归模型，基于先前相位的运动模式预测未来相位。我们在本机构50名接受放疗患者的实测4D CT数据集以及一个包含20名患者（部分有多次扫描）的公开数据集上评估方法，共涉及超过1300个3D CT相位。结果表明，该方法在预测肺和心脏运动方面超越现有基准，充分验证了其从CT图像中捕捉运动动态的有效性。研究结果凸显该方法在提升放疗预规划中的潜力，实现更精准、自适应的放射治疗。
原文摘要 · Abstract (English)
Radiotherapy often involves a prolonged treatment period. During this time, patients may experience organ motion due to breathing and other physiological factors. Predicting and modeling this motion before treatment is crucial for ensuring precise radiation delivery. However, existing pre-treatment organ motion prediction methods primarily rely on deformation analysis using principal component analysis (PCA), which is highly dependent on registration quality and struggles to capture periodic temporal dynamics for motion modeling.In this paper, we observe that organ motion prediction closely resembles an autoregressive process, a technique widely used in natural language processing (NLP). Autoregressive models predict the next token based on previous inputs, naturally aligning with our objective of predicting future organ motion phases. Building on this insight, we reformulate organ motion prediction as an autoregressive process to better capture patient-specific motion patterns. Specifically, we acquire 4D CT scans for each patient before treatment, with each sequence comprising multiple 3D CT phases. These phases are fed into the autoregressive model to predict future phases based on prior phase motion patterns. We evaluate our method on a real-world test set of 4D CT scans from 50 patients who underwent radiotherapy at our institution and a public dataset containing 4D CT scans from 20 patients (some with multiple scans), totaling over 1,300 3D CT phases. The performance in predicting the motion of the lung and heart surpasses existing benchmarks, demonstrating its effectiveness in capturing motion dynamics from CT images. These results highlight the potential of our method to improve pre-treatment planning in radiotherapy, enabling more precise and adaptive radiation delivery.
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