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WE-E-213CD-03: Inverse-Consistent Symmetric Registration of Inner Colon Surfaces Derived from Prone and Supine CT

H Roth^1,2, J McClelland^1,2, M Modat^1,2

¹Centre for Medical Image Computing.

Medical Physics

|May 19, 2017

Summary

A new symmetric registration method for CT colonography improves accuracy by reducing directional bias between prone and supine colon surface images. This enhances colorectal cancer detection and computer-aided diagnosis.

Keywords:

Cancer Computed tomography Image registration Surface morphology

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Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

Area of Science:

Medical Imaging
Computational Anatomy

Background:

Accurate registration of prone and supine colon surfaces from CT colonography is crucial for detecting colorectal cancer and polyps.
Asymmetric non-rigid registration methods can introduce directional bias, leading to anatomical inaccuracies and potential reader errors.

Purpose of the Study:

To reduce directional bias and enhance the robustness of colon surface registration.
To adapt a cylindrical registration algorithm for symmetric and inverse-consistent application.

Main Methods:

Mapped prone and supine colon surfaces to regular cylinders for simplified registration.
Implemented a symmetric B-spline image registration in cylindrical space, simultaneously computing prone-to-supine and supine-to-prone transformations.
Enforced inverse consistency using a weighted penalty term in the optimization function.

Main Results:

Reduced mean inverse-consistency error by 32% (from 4.8mm to 3.2mm).
Improved mean registration error from 8.2mm to 7.3mm across validation datasets.
Demonstrated effectiveness in patient cases with significant variations in luminal distension and surface morphology.

Conclusions:

A symmetric formulation significantly improves the quality of prone and supine surface registration.
Utilizing information from both registration directions reduces bias and promotes convergence to a more accurate solution.
Enhanced registration accuracy facilitates CT colonography interpretation and holds potential for improving computer-aided detection methods for colorectal cancer.