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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Resolution enhancement for lung 4D-CT based on transversal structures by using multiple Gaussian process regression

Shiting Fang¹, Runyue Hu², Xinrui Yuan²

¹School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China; The Sixth affiliated Hospital of Sun Yat-sen University, Guangzhou 510655, China.

Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)

|October 10, 2020

Summary

This study introduces a new multiple Gaussian process regression model to improve the resolution of lung four-dimensional computed tomography (4D-CT) scans. The method enhances image clarity without needing extra hardware or data.

Keywords:

Lung four-dimensional computed tomography (4D-CT)Multiple Gaussian process regression Resolution enhancement

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Area of Science:

Medical Imaging
Computational Biology
Machine Learning

Background:

Four-dimensional computed tomography (4D-CT) is clinically valuable but faces hardware limitations affecting superior-inferior resolution.
Dense sampling in the superior-inferior direction is often impractical for 4D-CT systems.

Purpose of the Study:

To develop a novel multiple Gaussian process regression model for enhancing superior-inferior resolution in lung 4D-CT.
To leverage transversal image structures to improve image quality without additional hardware.

Main Methods:

A multiple Gaussian process regression model was developed, inspired by random forest algorithms.
The model learns from transversal images, using 3x3 patches to predict missing superior-inferior pixels.
Training data consists of central pixels (labels) and their eight neighbors (inputs) from sampled patches.

Main Results:

The proposed method was evaluated using simulated and public datasets.
Experiments demonstrated the remarkable performance of the developed approach in enhancing resolution.

Conclusions:

A novel approach is presented to improve 4D-CT resolution without external data or hardware.
The method generates clear coronal and sagittal images, facilitating easier interpretation.