Peak velocities measurement by MRI using 2-D flow and 4-D flow

Cardiovascular diseases (CVD) has become a more common disease in the 21st century, especially in the developed countries. As such, there is an increasing demand for Cardiac Magnetic Resonance Imaging (CMR) for diagnosing, assessing and monitoring of CVD. CMR does not only provide the morphological...

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Bibliographic Details
Main Author: Tan, Valarine Li Yin
Other Authors: Mohammed Yakoob Siyal
Format: Final Year Project
Language:English
Published: 2019
Subjects:
Online Access:http://hdl.handle.net/10356/78298
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Institution: Nanyang Technological University
Language: English
Description
Summary:Cardiovascular diseases (CVD) has become a more common disease in the 21st century, especially in the developed countries. As such, there is an increasing demand for Cardiac Magnetic Resonance Imaging (CMR) for diagnosing, assessing and monitoring of CVD. CMR does not only provide the morphological information of the anatomy but it is also capable of providing important functional information such as the blood flow dynamics, which is also known as hemodynamic of the heart. However, one major disadvantage of using CMR to quantify blood flow is that it requires long post image acquisition processing time. To quantify blood flow from the MR images, extensive post-acquisition data analysis is needed and therefore it is a time consuming and tedious analysis. However, this disadvantage and limitation should not deter people from using CMR. There are many other advantages that MRI has over the other imaging modality including the ability of imaging soft tissues and it does not involve the use of ionising radiation. To overcome the limitation, a tool can be built to improve the procedure and time needed for post-processing. This report describes a new graphic user interface (GUI) designed and built to simplify the process of post-processing of the MR images. It provides a tool to extract and process velocity information from phase contrast (PC) MR images, without the need for time-consuming post data acquisition analysis. Analysis of MR images acquired from healthy volunteers was also performed using the GUI as part of the process to test and determine the accuracy of the GUI. The analysis and results will also be discussed in the later part of this report.