用神经场建模脑结构随时间变化，更精准捕捉细微形态演化。
Capturing Longitudinal Changes in Brain Morphology Using Temporally Parameterized Neural Displacement Fields
- 用多层感知机构建时序参数化变形场，实现连续时间的脑影像配准。
- 在4D脑部MRI上验证，能有效捕捉微小但关键的结构变化。
- 新正则化项确保体素轨迹单调变化，结果更符合生物实际。
纵向图像配准可研究脑形态的时序变化，对监测特定结构的生长或萎缩具有重要意义。然而，该任务因数据噪声/伪影及难以量化连续扫描间的微小解剖变化而极具挑战。本文提出一种新型纵向配准方法，利用时序参数化的神经位移场建模结构变化。具体地，采用多层感知机实现隐式神经表示（INR），作为任意时间点上变形场的连续坐标基近似。对于某受试者的一组N次扫描，模型输入三维空间坐标x, y, z与对应时间表示t，学习描述观测和未观测时间点上结构的连续形态。此外，利用INR的解析导数，设计了一种新的正则化函数，强制体素轨迹变化速率单调，显著提升生物学合理性。方法在4D脑部MR注册任务中得到验证，表现出优异性能。
原文摘要 · Abstract (English)
Longitudinal image registration enables studying temporal changes in brain morphology which is useful in applications where monitoring the growth or atrophy of specific structures is important. However this task is challenging due to; noise/artifacts in the data and quantifying small anatomical changes between sequential scans. We propose a novel longitudinal registration method that models structural changes using temporally parameterized neural displacement fields. Specifically, we implement an implicit neural representation (INR) using a multi-layer perceptron that serves as a continuous coordinate-based approximation of the deformation field at any time point. In effect, for any N scans of a particular subject, our model takes as input a 3D spatial coordinate location x, y, z and a corresponding temporal representation t and learns to describe the continuous morphology of structures for both observed and unobserved points in time. Furthermore, we leverage the analytic derivatives of the INR to derive a new regularization function that enforces monotonic rate of change in the trajectory of the voxels, which is shown to provide more biologically plausible patterns. We demonstrate the effectiveness of our method on 4D brain MR registration.
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