Optimizing CT Scan Geometries With and Without Gradients
This work addresses motion compensation in CT imaging for medical diagnostics, presenting an incremental improvement in optimization speed.
The paper tackled the problem of compensating for rigid patient motion in CT scans by optimizing projection geometries, showing that gradient-based algorithms converge substantially faster than gradient-free ones while maintaining comparable capture range and robustness.
In computed tomography (CT), the projection geometry used for data acquisition needs to be known precisely to obtain a clear reconstructed image. Rigid patient motion is a cause for misalignment between measured data and employed geometry. Commonly, such motion is compensated by solving an optimization problem that, e.g., maximizes the quality of the reconstructed image with respect to the projection geometry. So far, gradient-free optimization algorithms have been utilized to find the solution for this problem. Here, we show that gradient-based optimization algorithms are a possible alternative and compare the performance to their gradient-free counterparts on a benchmark motion compensation problem. Gradient-based algorithms converge substantially faster while being comparable to gradient-free algorithms in terms of capture range and robustness to the number of free parameters. Hence, gradient-based optimization is a viable alternative for the given type of problems.