Journal
PHYSICS IN MEDICINE AND BIOLOGY
Volume 68, Issue 20, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1088/1361-6560/acf90e
Keywords
computed tomography; projective geometry; differentiable programming; motion compensation
Ask authors/readers for more resources
In this paper, the authors extend the differentiable formulation of CT fan-beam reconstruction to the acquisition geometry to improve the quality of reconstruction. They demonstrate that by using a trained neural network to parameterize the cost function, the motion compensation algorithm can be optimized. The proposed method also has potential applications in other areas.
Objective. Incorporating computed tomography (CT) reconstruction operators into differentiable pipelines has proven beneficial in many applications. Such approaches usually focus on the projection data and keep the acquisition geometry fixed. However, precise knowledge of the acquisition geometry is essential for high quality reconstruction results. In this paper, the differentiable formulation of fan-beam CT reconstruction is extended to the acquisition geometry. Approach. The CT fan-beam reconstruction is analytically derived with respect to the acquisition geometry. This allows to propagate gradient information from a loss function on the reconstructed image into the geometry parameters. As a proof-of-concept experiment, this idea is applied to rigid motion compensation. The cost function is parameterized by a trained neural network which regresses an image quality metric from the motion-affected reconstruction alone. Main results. The algorithm improves the structural similarity index measure (SSIM) from 0.848 for the initial motion-affected reconstruction to 0.946 after compensation. It also generalizes to real fan-beam sinograms which are rebinned from a helical trajectory where the SSIM increases from 0.639 to 0.742. Significance. Using the proposed method, we are the first to optimize an autofocus-inspired algorithm based on analytical gradients. Next to motion compensation, we see further use cases of our differentiable method for scanner calibration or hybrid techniques employing deep models.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available