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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...
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Body composition analysis by radiological imaging - methods, applications, and prospects.

Nicolas Linder1,2, Timm Denecke1, Harald Busse1

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Summary

Radiological imaging offers quantitative body composition (BC) analysis, crucial for oncological and metabolic conditions. Advanced AI methods are enhancing efficiency and accuracy in BC assessments from CT and MRI scans.

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Area of Science:

  • Radiology and Medical Imaging
  • Biomedical Engineering
  • Quantitative Anatomy

Background:

  • Quantitative assessment of human body composition (BC) is increasingly vital for oncological and metabolic disease management.
  • Radiological imaging techniques provide powerful tools for non-invasive BC analysis.
  • Understanding various BC assessment methods is crucial for clinical application and dissemination.

Purpose of the Study:

  • To review and present diverse radiological methods for quantitative body composition analysis.
  • To provide a radiological readership with a comprehensive overview of BC definitions and applications.
  • To facilitate the adoption and expansion of body composition analysis in clinical practice.

Main Methods:

  • Literature review focusing on radiological cross-sectional imaging techniques.
  • Inclusion of own work and experience with MRI- and CT-based analyses.
  • Search terms included body composition, obesity, sarcopenia, osteopenia, imaging, and radiology.

Main Results:

  • Segmentation of tomographic datasets is a key post-processing method, established in clinical disciplines like bariatric surgery.
  • Validated reference values are essential for accurate radiological measures of tissues like liver fat and muscle.
  • Artificial intelligence, particularly deep learning, enables automated and efficient segmentation for retrospective and prospective BC analysis.

Conclusions:

  • Radiological imaging methods are increasingly utilized for reproducible, quantitative body composition analysis.
  • CT and MRI data from routine examinations can be leveraged for retrospective and prospective BC assessments.
  • Automated analysis methods, including deep learning and radiomics, are poised to become significant in future body composition studies.