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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.
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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and the...

Imaging Studies III: Computed Tomography

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X-ray Imaging

X-ray Imaging

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Related Experiment Video

Updated: Jun 21, 2026

Computed Tomography and Optical Imaging of Osteogenesis-angiogenesis Coupling to Assess Integration of Cranial Bone Autografts and Allografts

Computed Tomography and Optical Imaging of Osteogenesis-angiogenesis Coupling to Assess Integration of Cranial Bone Autografts and Allografts

Published on: December 22, 2015

Computational radiology in skeletal radiography.

Ph Peloschek¹, S Nemec, P Widhalm

¹Computational Image Analysis and Radiology Lab, Department of Radiology, Medical University Vienna, Waehringer Guertel 18-20, Vienna, Austria.

European Journal of Radiology

|July 8, 2009

Summary

Computational image analysis in musculoskeletal radiology is advancing rapidly, with algorithms now mature for clinical use. Further research is needed to optimize automation, workflow integration, and demonstrate clinical benefits.

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Area of Science:

Musculoskeletal Radiology
Computational Image Analysis
Medical Imaging

Background:

Significant advancements in computational image analysis have occurred in musculoskeletal radiology.
Algorithms have matured, enabling initial clinical feasibility for various applications.

Purpose of the Study:

To review the current state and potential applications of computational image analysis in musculoskeletal radiology.
To identify key challenges and areas requiring further investigation for clinical integration.

Main Methods:

Review of recent developments in computational image analysis algorithms.
Analysis of current and potential applications in musculoskeletal radiography.
Discussion of fundamental issues related to clinical implementation.

Main Results:

Computational image analysis offers promising applications including joint space measurement, erosion quantification, fracture detection, and alignment angle assessment.
Algorithms are nearing a level of maturity suitable for initial clinical adoption.

Conclusions:

While promising, the optimal balance between automation and operator dependency needs definition.
Integration into clinical workflows and proof of incremental clinical benefit are crucial next steps.