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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: Jul 1, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Systematic Review on Learning-based Spectral CT.

Alexandre Bousse¹, Venkata Sai Sundar Kandarpa¹, Simon Rit²

¹LaTIM, Inserm UMR 1101, Université de Bretagne Occidentale, 29238 Brest, France.

IEEE Transactions on Radiation and Plasma Medical Sciences

|March 13, 2024

Summary

This summary is machine-generated.

Spectral computed tomography (CT) offers advanced imaging over conventional CT. Machine learning techniques are emerging to overcome spectral CT artifacts and improve clinical applications.

Keywords:

Artificial Intelligence (AI)Deep Learning Dual-energy CT (DECT)Machine Learning Photon-counting CT (PCCT)

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Area of Science:

Medical imaging
Radiology
Computed Tomography

Background:

Spectral computed tomography (CT) represents an advancement over conventional single-energy CT.
Key forms include dual-energy CT (DECT) and photon-counting CT (PCCT).
These techniques enhance image quality, enable material decomposition, and allow feature quantification.

Purpose of the Study:

To review state-of-the-art data-driven techniques for spectral CT.
To address challenges and artifacts inherent in spectral CT data and images.
To highlight machine learning applications in overcoming spectral CT limitations.

Main Methods:

Review of machine learning techniques applied to spectral CT.
Analysis of data-driven approaches for artifact reduction.
Examination of methods for material decomposition and feature quantification.

Main Results:

Machine learning shows significant promise in mitigating spectral CT artifacts.
Data-driven techniques enhance image quality and diagnostic accuracy.
ML enables improved material decomposition and quantitative analysis.

Conclusions:

Machine learning is crucial for overcoming spectral CT challenges.
Advanced techniques are vital for broader clinical adoption of spectral CT.
Future research should focus on robust ML algorithms for spectral CT.