Description
In recent years, the application of deep learning techniques to medical image analysis tasks and image quality enhancement has proven to be a useful tool. One critical area where deep learning models have shown promising results is for patient motion estimation in CT scans [1],[2].
Deep learning models highly depend on the quality and diversity of the underlying training data, but well-annotated datasets, where the patient motion throughout the whole scan is known, are sparse. This is typically overcome with the generation of synthetic data, where motion-free clinical acquisitions are corrupted with simulated patient motion by altering the relevant components in the projection matrices. In the case of head CT scans, the rigid patient motion can be parameterized by a 6DOF trajectory over all acquisition frames. This is typically done by applying a Gaussian motion or, for more complex patterns, using B-splines. However, these simulated patterns often fall short of mimicking real head motion observed in clinical settings, especially by lacking complex spatiotemporal correlations. To provide more realistic training samples it is necessary to define a real-world constrained parameter space, respecting correlations, time dependencies and anatomical boundaries. This allows for neural networks to generalize better to real-world data.
This thesis aims to perform a conclusive analysis of the parameter space of rigid (6DOF) head motion patterns, obtained from measurements with an in-house optical tracking system integrated in a C-arm CT scanner at Siemens Healthineers in Forchheim. By analyzing the spatiotemporal correlations and constraints in the 6DOF parameter space, lower-dimensional underlying structures might be uncovered. Clustering techniques can be incorporated to further reveal sub-manifolds in the 6DOF space, as well as distinguishing different classes of motion types like breathing, nodding, etc. A Variational Autoencoder (or similar) should be trained with the goal of providing annotated synthetic datasets with realistic motion patterns.
[1] A. Preuhs et al., “Appearance Learning for Image-Based Motion Estimation in Tomography,” in IEEE Transactions on Medical Imaging, vol. 39, no. 11, pp. 3667-3678, Nov. 2020
[2] Chen Z, Li Q, Wu D., “Estimate and compensate head motion in non-contrast head CT scans using partial angle reconstruction and deep learning,” in Medical Physics 2024; 51: 3309–3321