Gradient-based geometry learning for fan-beam CT reconstruction

Thies M, Wagner F, Maul N, Folle L, Meier M, Rohleder M, Schneider LS, Pfaff L, Gu M, Utz J, Denzinger F, Manhart M, Maier A (2023)

Publication Language: English

Publication Type: Journal article, Original article

Publication year: 2023

Journal

Book Volume: 68

Article Number: 205004

Journal Issue: 20

URI: https://iopscience.iop.org/article/10.1088/1361-6560/acf90e

DOI: 10.1088/1361-6560/acf90e

Open Access Link: https://iopscience.iop.org/article/10.1088/1361-6560/acf90e

Abstract

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 with CRIS profile

Related research project(s)

Involved external institutions

Siemens AG, Healthcare Sector Germany (DE)

How to cite

APA:

Thies, M., Wagner, F., Maul, N., Folle, L., Meier, M., Rohleder, M.,... Maier, A. (2023). Gradient-based geometry learning for fan-beam CT reconstruction. Physics in Medicine and Biology, 68(20). https://dx.doi.org/10.1088/1361-6560/acf90e

MLA:

Thies, Mareike, et al. "Gradient-based geometry learning for fan-beam CT reconstruction." Physics in Medicine and Biology 68.20 (2023).

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