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Supervised Deep Learning for Head Motion Correction in PET.

Tianyi Zeng¹, Jiazhen Zhang¹, Enette Revilla²

¹Department of Radiology & Biomedical Imaging, Yale University, New Haven, CT, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

|December 18, 2023

Summary

This summary is machine-generated.

This study introduces a deep learning algorithm for head motion correction in brain PET scans, eliminating the need for external tracking devices. The DL-HMC method accurately predicts motion, improving image quality and diagnostic reliability.

Keywords:

PET brain data-driven motion correction deep learning image registration supervised learning

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Area of Science:

Medical Imaging
Artificial Intelligence
Neuroscience

Background:

Head movement in brain PET imaging causes artifacts and errors.
Accurate motion correction is vital for quantitative analysis and disease diagnosis.
Current methods often require external tracking devices.

Purpose of the Study:

To develop a deep learning algorithm for continuous, hardware-free head motion prediction in brain PET.
To improve the accuracy of quantitative analysis in dynamic PET scans.

Main Methods:

A novel Deep Learning for Head Motion Correction (DL-HMC) algorithm was developed.
DL-HMC utilizes dynamic PET scans and predicts six rigid motion parameters.
The network was trained using supervised learning with external Polaris Vicra tracking as the gold standard.

Main Results:

DL-HMC demonstrated accurate prediction of rigid motion parameters for brain PET.
Quantitative and qualitative evaluations confirmed the algorithm's performance.
An ablation study validated the effectiveness of DL-HMC's design components.

Conclusions:

The developed DL-HMC algorithm enables accurate head motion prediction in brain PET without external hardware.
This data-driven approach enhances image quality and quantitative accuracy for neurological disease diagnosis.