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Related Concept Videos

Imaging Studies III: Computed Tomography01:27

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...
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Related Experiment Video

Updated: Jan 8, 2026

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
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A novel pipeline using statistical parametric mapping for assessing differences in three- and four-dimensional

Taylor P Trentadue1, Andrew R Thoreson2, Thor E Andreassen2

  • 1Assistive and Restorative Technology Laboratory, Mayo Clinic, Rochester, MN, USA; Mayo Clinic Medical Scientist Training Program, Mayo Clinic, Rochester, MN, USA; Mayo Clinic Graduate Program in Biomedical Engineering and Physiology, Mayo Clinic, Rochester, MN, USA.

Journal of Biomechanics
|December 20, 2025
PubMed
Summary

This study introduces a novel pipeline using statistical parametric mapping (SPM) to analyze four-dimensional computed tomography (4DCT) joint mechanics. The method quantifies dynamic joint motion, revealing how wrist position affects distal radioulnar joint (DRUJ) mechanics.

Keywords:
4DCTDistal radioulnar jointDynamic imagingJoint biomechanicsStatistical shape modelingWrist biomechanics

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Area of Science:

  • Biomechanics
  • Medical Imaging
  • Computational Anatomy

Background:

  • Four-dimensional computed tomography (4DCT) enables quantification of joint mechanics during dynamic tasks.
  • Analyzing complex arthrokinematic data across participants and motion cycles requires advanced analytical techniques.
  • Statistical parametric mapping (SPM) is under-explored for 4DCT applications despite its utility in biomechanical and imaging datasets.

Purpose of the Study:

  • To develop and validate a computational pipeline for analyzing 4DCT-derived arthrokinematics.
  • To explore the relationship between wrist position and distal radioulnar joint (DRUJ) interosseous proximities.
  • To demonstrate the pipeline's applicability to both static and dynamic joint motion analysis.

Main Methods:

  • A statistical shape model was used to create a canonical joint template.
  • Non-linear morphing predicted participant-specific joint surfaces from the template.
  • SPM regression and generalized linear models analyzed relationships between wrist position, injury status, and interosseous proximities.

Main Results:

  • The pipeline successfully quantified position-related differences in DRUJ interosseous proximities.
  • Increased pronation was significantly associated with increased proximities at the sigmoid notch volar margin.
  • The analysis demonstrated the pipeline's capability for 3D and 4D computed tomography-derived arthrokinematics.

Conclusions:

  • The proposed pipeline effectively quantifies dynamic joint arthrokinematics using 4DCT data.
  • This method allows for the analysis of relationships between joint motion and continuous or categorical variables.
  • The pipeline is adaptable for studying various joints, tasks, conditions, and injury states.