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Management of Respiratory Motion Artefacts in 18F-fluorodeoxyglucose Positron Emission Tomography using an Amplitude-Based Optimal Respiratory Gating Algorithm

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Methods for abdominal respiratory motion tracking.

Dominik Spinczyk¹, Adam Karwan, Marcin Copik

¹Faculty of Biomedical Engineering, Silesian University of Technology , Gliwice , Poland , and.

Computer Aided Surgery : Official Journal of the International Society for Computer Aided Surgery

|April 12, 2014

Summary

This study compares non-invasive methods for tracking patient breathing motions using surface registration. A point cloud approach with a time-of-flight sensor demonstrated effectiveness in capturing abdominal surface changes during respiration.

Keywords:

Non-rigid iterative closest point point cloud respiratory motion tracking time-of-flight

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Area of Science:

Medical Imaging
Biomedical Engineering
Computer Vision

Background:

Accurate tracking of patient respiratory motion is crucial for medical imaging and radiation therapy.
Existing non-invasive surface registration methods have limitations in precision and applicability.

Purpose of the Study:

To evaluate and compare different non-invasive surface registration techniques for tracking abdominal and thoracic breathing motions.
To assess the efficacy of a point cloud approach using a time-of-flight (ToF) sensor.

Main Methods:

Comparison of marker tracking, chessboard projection, and ToF sensor-based point cloud registration.
Extension of the non-rigid Iterative Closest Point (ICP) algorithm using respiratory phase data and markers.
Evaluation of four correspondence data retrieval variants based on surface distance, correspondence distance, and marker error.

Main Results:

The point cloud approach using a ToF sensor was evaluated for its ability to track abdominal surface changes.
Performance metrics including surface distance, correspondence distance, and marker error were measured across different registration methods.
The study identified optimal methods for computing correspondence measurements, considering the number of correspondences and assignment error.

Conclusions:

Non-invasive surface registration methods, particularly the point cloud approach, show promise for accurately tracking respiratory motion.
The developed methods provide a basis for improving motion management in clinical applications.
Further refinement of the non-rigid ICP algorithm and correspondence computation can enhance registration accuracy.