Analysis and Clinical Applications of Backpropagation Neural Network in Catheterization Ventriculography and GSPECT
Date Issued
2004
Date
2004
Author(s)
Shiau, Yu-Chien
DOI
en-US
Abstract
Cardiac motion analysis is very important for the clinical diagnosis and disease evaluation of heart disease. There have been several image processing techniques for motion detection and motion analysis. Among those techniques, artificial neural network has been an important new approach in recent years.
We propose a new approach and a novel algorithm, which uses a three-layer backpropagation neural network for cardiac motion analysis of catheterization ventriculography, myocardial perfusion gated single photon emission computed tomography (GSPECT), blood-pool GSPECT, and radionuclide ventriculography. The backpropagation neural network is trained by phantom images or sample images, which simulate different kinds of dynamic cardiac images. After the training is completed, the neural network can perform motion detection on patients’ dynamic cardiac images. The results of motion detection are displayed in the formats of motion vector fields and Doppler color display, which are superimposed on the original cardiac images. The physician specialists interpret cardiac wall motion on patients’ original images, motion vector fields, and Doppler color display. Receiver operating characteristic (ROC) curve analysis is used to test the statistical differences among the accuracy of the image interpretations.
There is no significant statistical differences among the areas under ROC curves for the image interpretations made on original dynamic cardiac images, motion vector fields, and Doppler color displays.
Cardiac wall motion detection and analysis by backpropagation neural network is useful for dynamic cardiac images. It is highly suggestive that the algorithm can be also useful for cardiac cine MRI, cardiac multidetector CT, and cardiac ultrasonography.
Subjects
心臟影像
類神經網路
運動分析
neural network
heart image
motion analysis
Type
thesis