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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...

Imaging Studies III: Computed Tomography

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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

Imaging Studies I: CT and MRI

Imaging Studies I: CT and MRI

Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
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Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...

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Updated: May 17, 2026

Hybrid µCT-FMT imaging and image analysis

Hybrid µCT-FMT imaging and image analysis

Published on: June 4, 2015

Image analysis and modeling in medical image computing. Recent developments and advances.

H Handels¹, T M Deserno, H-P Meinzer

¹University of Lübeck, Institute of Medical Informatics, Ratzeburger Allee 160, 23538 Lübeck, Germany. handels@imi.uni-luebeck.de

Methods of Information in Medicine

|October 12, 2012

Summary

This summary is machine-generated.

Model-based image analysis enhances medical diagnostics and image-guided therapy by improving automation, accuracy, and reproducibility. These advanced methods offer new perspectives for patient risk analysis and understanding complex biological mechanisms.

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Area of Science:

Medical image computing
Model-based image analysis
Quantitative medical imaging

Background:

Medical image computing is crucial for diagnostics and image-guided therapy.
Current methods require enhanced automation, accuracy, reproducibility, and robustness for clinical routine.
Model-based approaches offer significant improvements in these areas.

Discussion:

The focus theme presents recent advances in modeling and model-based image analysis.
Models integrated into image analysis improve algorithm performance.
Model-based techniques enable prediction of organ changes and patient risk analysis.

Key Insights:

Model-based image analysis improves segmentation quality, accuracy, and reproducibility.
Complex models are applied across diverse medical imaging modalities (radiographic, CT, MR, DTI, microscopic).
These methods demonstrate high potential and broad applicability in medical image computing.

Outlook:

Model-based image computing provides deeper insights into complex biological mechanisms.
These methods are vital for advancing medical diagnostics and patient treatment.
Continued development promises further integration into clinical practice.