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Toward an Affine Feature-Based Registration Method for Ground Glass Lung Nodule Tracking.

Yehuda Kfir Ben Zikri¹, María Helguera¹, Nathan D Cahill²

¹Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA.

Vipimage 2019 : Proceedings of the VII ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing, October 16-18, 2019, Porto, Portugal. Vipimage (Conference) (2019 : Porto, Portugal)

|July 24, 2020

Summary

This study introduces a new method for tracking lung nodule changes using registered CT scans. The technique improves accuracy in assessing tumor progression or regression, aiding clinical decisions.

Keywords:

Affine feature-based image registration Background tissue deformation correction Lung nodule tracking Quantitative nodule tracking

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Area of Science:

Medical Imaging
Radiology
Computational Biology

Background:

Accurate lung nodule progression assessment is vital for disease management and therapy response evaluation.
Computed tomography (CT) is standard for lung nodule tracking, but segmentation errors due to irregular boundaries complicate quantification.
Existing methods struggle with background lung tissue deformation, impacting nodule assessment accuracy.

Purpose of the Study:

To develop and evaluate a feature-based affine image registration framework for accurate lung nodule progression assessment.
To improve the quantification of lung nodule changes by accounting for thoracic CT image deformation.
To enhance clinical confidence in evaluating tumor progression or regression.

Main Methods:

Developed a feature-based affine image registration framework to align serial thoracic CT images.
Utilized digital subtraction images post-registration to assess nodule changes.
Evaluated the method on twelve de-identified patient datasets.

Main Results:

Achieved registration accuracy better than 1.5mm compared to non-rigid registration techniques.
Demonstrated the framework's ability to account for background lung tissue deformation.
Clinical assessment using registered subtraction images showed consistency and increased confidence over visual analysis.

Conclusions:

The proposed feature-based affine registration framework offers a robust and accurate method for lung nodule progression assessment.
This technique enhances the reliability of quantifying tumor changes from thoracic CT images.
The method shows significant clinical potential for improving nodule tracking and patient management.