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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

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Gradient-based geometry learning for fan-beam CT reconstruction.

Mareike Thies¹, Fabian Wagner¹, Noah Maul^1,2

¹Pattern Recognition Lab, FAU Erlangen-Nürnberg, Germany.

Physics in Medicine and Biology

|October 2, 2023

Summary

This summary is machine-generated.

This study introduces a new method to improve computed tomography (CT) image quality by optimizing the acquisition geometry. The approach enhances image clarity and accuracy, particularly in cases of motion or scanner imperfections.

Keywords:

computed tomography differentiable programming motion compensation projective geometry

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Area of Science:

Medical Imaging
Computational Imaging
Image Reconstruction

Background:

Differentiable computed tomography (CT) reconstruction operators enhance image analysis.
Current methods often fix acquisition geometry, neglecting its impact on reconstruction quality.
Precise acquisition geometry is crucial for high-quality CT reconstruction.

Purpose of the Study:

To extend differentiable fan-beam CT reconstruction to optimize acquisition geometry.
To enable gradient propagation from image space to geometry parameters.
To apply this method for motion compensation and other calibration tasks.

Main Methods:

Analytically derived fan-beam CT reconstruction with respect to acquisition geometry.
Propagated gradient information from image loss functions to geometry parameters.
Applied to rigid motion compensation using a neural network-parameterized cost function.

Main Results:

Improved Structural Similarity Index Measure (SSIM) from 0.848 to 0.946 for motion-affected reconstructions.
Generalization to real fan-beam sinograms (helical trajectory) with SSIM increase from 0.639 to 0.742.
First autofocus-inspired algorithm optimized using analytical gradients.

Conclusions:

The proposed method enables optimization of CT acquisition geometry for improved image reconstruction.
Demonstrated effectiveness in motion compensation and potential for scanner calibration.
Paves the way for hybrid deep learning and analytical gradient techniques in CT.