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Deformation of Member under Multiple Loadings

Deformation of Member under Multiple Loadings

When a rod is made of different materials or has various cross-sections, it must be divided into parts that meet the necessary conditions for determining the deformation. These parts are each characterized by their internal force, cross-sectional area, length, and modulus of elasticity. These parameters are then used to compute the deformation of the entire rod.
In the case of a member with a variable cross-section, the strain is not constant but depends on the position. The deformation of an...

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Related Experiment Video

Updated: Feb 25, 2026

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

Inverse consistency error for validating deformable image registration: an explorative study on computational

Gianfranco Loi¹, Marco Fusella², Stefania Zara³

¹Azienda Ospedaliero-Universitaria Maggiore della Carità, Novara, Italy.

Physics and Imaging in Radiation Oncology

|February 24, 2026

Summary

Inverse consistency error (ICE) offers automated, voxel-wise assessment of deformable image registration (DIR) accuracy. This method complements contour-based metrics and aids in radiotherapy workflows.

Keywords:

Deformable image registration Inverse consistency error Quality assurance Registration uncertainty Voxel wise validation

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Last Updated: Feb 25, 2026

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Area of Science:

Medical Imaging
Radiotherapy Physics
Computational Anatomy

Background:

Deformable image registration (DIR) validation traditionally relies on contour-based methods.
Automated voxel-wise metrics are needed for more robust DIR accuracy assessment.

Purpose of the Study:

To evaluate inverse consistency error (ICE) as an automated voxel-wise metric for DIR accuracy.
To compare ICE with existing metrics using digital phantoms and clinical data.

Main Methods:

Generated synthetic ground-truth displacement vector fields (DVFs) using digital phantoms.
Performed DIR using the ANACONDA algorithm.
Compared ICE with ground-truth registration error (GTRE), target registration error (TRE), and mean distance to agreement (MDA).

Main Results:

ICE showed negligible values for ground-truth DVFs, confirming invertibility.
ICE correlated strongly with GTRE (R=0.85) and moderately with TRE (R=0.68) in head-and-neck phantoms.
ICE identified high-uncertainty regions missed by contour-based metrics, despite underestimating large homogeneous deformations.

Conclusions:

ICE provides automated, voxel-wise quantification of DIR uncertainty from clinical DVFs.
ICE complements contour-based metrics for patient-specific quality assurance.
ICE can improve dose mapping in adaptive and re-irradiation radiotherapy.