Fishbaugh James, Gerig Guido
Computer Science and Engineering Department, Tandon School of Engineering, NYU, NY.
Proc IEEE Int Symp Biomed Imaging. 2019 Apr;2019:1488-1491. doi: 10.1109/ISBI.2019.8759583. Epub 2019 Jul 11.
The analysis of medical image time-series is becoming increasingly important as longitudinal imaging studies are maturing and large scale open imaging databases are becoming available. Image regression is widely used for several purposes: as a statistical representation for hypothesis testing, to bring clinical scores and images not acquired at the same time into temporal correspondence, or as a consistency filter to enforce temporal correlation. Geodesic image regression is the most prominent method, but the geodesic constraint limits the flexibility and therefore the application of the model, particularly when the observation time window is large or the anatomical changes are non-monotonic. In this paper, we propose to parameterize diffeomorphic flow by acceleration rather than velocity, as in the geodesic model. This results in a nonparametric image regression model which is completely flexible to capture complex change trajectories, while still constrained to be diffeomorphic and with a guarantee of temporal smoothness. We demonstrate the application of our model on synthetic 2D images as well as real 3D images of the cardiac cycle.
随着纵向成像研究的成熟以及大规模开放成像数据库的出现，医学图像时间序列分析变得越来越重要。图像回归被广泛用于多种目的：作为假设检验的统计表示，使临床评分和不同时间获取的图像在时间上对应，或作为一致性滤波器以增强时间相关性。测地线图像回归是最突出的方法，但测地线约束限制了灵活性，从而限制了该模型的应用，特别是当观察时间窗口较大或解剖学变化是非单调的时候。在本文中，我们提议像在测地线模型中那样，通过加速度而非速度对微分同胚流进行参数化。这产生了一个非参数图像回归模型，该模型完全灵活以捕捉复杂的变化轨迹，同时仍受限于微分同胚且保证时间平滑性。我们展示了我们的模型在合成二维图像以及心动周期的真实三维图像上的应用。
