用神经隐式表示建模患者影像时空变化，实现治疗全程解剖结构动态预测。
ST-NeRP: Spatial-Temporal Neural Representation Learning with Prior Embedding for Patient-specific Imaging Study
- 基于隐式神经表征，将基线影像编码为先验嵌入，再学习时空连续形变函数。
- 在胸腹腔4D CT和纵向CT数据上，实现对多个时间点的形变场精准预测。
- 适合需长期随访的肿瘤治疗监测，尤其适用于动态解剖结构追踪。
治疗过程中及之后，影像常用于监测疾病进展和评估疗效。然而，从一系列患者特异性图像序列中可靠捕捉并预测空间-时间解剖变化仍具挑战。为此，我们提出一种基于先验嵌入的空间-时间神经表征学习方法（ST-NeRP）。该方法首先利用隐式神经表征（INR）网络将参考时间点的影像编码为先验嵌入；随后，通过另一组INR网络学习一个空间-时间连续的形变函数，该函数基于完整的患者特异性图像序列进行训练，可预测任意目标时间点的形变场。ST-NeRP在多种序列影像数据上验证有效，包括胸部与腹部的4D CT及纵向CT数据集。结果表明，该模型在追踪患者治疗过程中解剖结构动态变化方面具有显著潜力。
原文摘要 · Abstract (English)
During and after a course of therapy, imaging is routinely used to monitor the disease progression and assess the treatment responses. Despite of its significance, reliably capturing and predicting the spatial-temporal anatomic changes from a sequence of patient-specific image series presents a considerable challenge. Thus, the development of a computational framework becomes highly desirable for a multitude of practical applications. In this context, we propose a strategy of Spatial-Temporal Neural Representation learning with Prior embedding (ST-NeRP) for patient-specific imaging study. Our strategy involves leveraging an Implicit Neural Representation (INR) network to encode the image at the reference time point into a prior embedding. Subsequently, a spatial-temporally continuous deformation function is learned through another INR network. This network is trained using the whole patient-specific image sequence, enabling the prediction of deformation fields at various target time points. The efficacy of the ST-NeRP model is demonstrated through its application to diverse sequential image series, including 4D CT and longitudinal CT datasets within thoracic and abdominal imaging. The proposed ST-NeRP model exhibits substantial potential in enabling the monitoring of anatomical changes within a patient throughout the therapeutic journey.
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