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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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Related Experiment Video

Updated: Sep 19, 2025

DUCT: Double Resin Casting followed by Micro-Computed Tomography for 3D Liver Analysis

DUCT: Double Resin Casting followed by Micro-Computed Tomography for 3D Liver Analysis

Published on: September 28, 2021

Latent space reconstruction for missing data problems in CT.

Anton Kabelac^1,2, Elias Eulig^1,2, Joscha Maier¹

¹Division of X-Ray Imaging and Computed Tomography, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Medical Physics

|June 4, 2025

Summary

This summary is machine-generated.

Latent space reconstruction (LSR) effectively corrects computed tomography (CT) artifacts from missing or corrupt data. This deep learning method improves image quality for truncation and metal artifacts, enhancing diagnostic value.

Keywords:

computed tomography deep learning missing data

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Last Updated: Sep 19, 2025

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Area of Science:

Medical Imaging
Artificial Intelligence
Image Reconstruction

Background:

Computed tomography (CT) image reconstruction is often degraded by artifacts, reducing diagnostic accuracy.
Common artifacts stem from missing or corrupted projection data, including truncation, metal, and limited-angle acquisitions.

Purpose of the Study:

Introduce latent space reconstruction (LSR), a novel deep learning framework for correcting various CT artifacts.
Address artifacts caused by missing or corrupted projection data.

Main Methods:

Train a generative neural network on artifact-free CT images.
Iteratively identify a latent space point matching compromised projection data.
Utilize forward-projection to inpaint corrupted regions in raw data.

Main Results:

LSR effectively corrects truncation artifacts, suppressing them within the field of measurement (FOM) and extending FOM quality.
LSR reduces metal artifacts, improving visualization of surrounding tissues and anatomical details.
Demonstrated artifact suppression and improved image quality for both truncation and metal artifacts.

Conclusions:

LSR proves effective for correcting metal and truncation artifacts in CT imaging.
The framework's versatility supports application to diverse artifact types caused by data corruption or loss.