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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

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Diffeomorphic Lung Registration Using Deep CNNs and Reinforced Learning.

Jorge Onieva Onieva¹, Berta Marti-Fuster¹, María Pedrero de la Puente¹

¹Applied Chest Imaging Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

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|June 4, 2020

Summary

This study introduces a novel reinforced learning strategy for medical image registration, improving accuracy in chest CT scan alignment. The new method reduces estimation error compared to existing approaches.

Keywords:

Deep learning Diffeomorphism Lung registration Chest computed tomography Reinforced learning

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Area of Science:

Medical Imaging
Computer Vision
Machine Learning

Background:

Image registration is crucial for analyzing medical scans.
Comparing chest inspiratory and expiratory CT scans requires precise alignment.
Existing methods may face challenges with large datasets and accuracy.

Purpose of the Study:

To develop an accurate and efficient method for registering chest CT scans.
To recover the diffeomorphic elastic displacement vector field (DVF).
To improve upon existing image registration techniques using reinforced learning.

Main Methods:

Utilizing a RegNet-based architecture.
Implementing a reinforced learning strategy for large datasets.
Jointly regressing direct and inverse transformations to recover the DVF.

Main Results:

The proposed method achieved lower estimation error than the standard RegNet approach.
The reinforced learning strategy effectively handled large training datasets.
Accurate recovery of the displacement vector field was demonstrated.

Conclusions:

The reinforced learning approach enhances accuracy in medical image registration.
This method offers a promising solution for aligning serial medical images.
Further applications in respiratory motion analysis are suggested.