Pulse sequence programming for fast imaging and implementation of complex algorithms for data processing, reconstruction and quantification of cardiac imaging

Résumé

Magnetic Resonance Imaging (MRI) is considered to be one of the most widely used imaging techniques in cardiovascular applications. Computational models are continually optimizing the images acquisition and processing pipelines. However, previous computational models of cardiac images faced limitations such as high dependence on assumptions, insufficient accuracy and lack of enough experimental data. The main objective of this work was to propose new techniques to improve imaging and data processing. 1. Pulse Sequence Programming for Fast Imaging: MRI is one of the imaging techniques widely used for the acquisition of the phase contrast images required for the analysis of hemodynamic data. More specifically, PC-MRI obtains velocity and direction of flowing blood. A phase contrast pulse sequence based on GE and with velocity compensation has been developed which allows accurate manipulation of blood flow patterns in small arteries of small animals. 2. Relaxation Time Mapping: In MRI, relaxation times represent specific tissue properties that can be quantified with the help of specific imaging strategies. While there are basic software tools for specific pulse sequences, until now there is no universal software program available to automate the mapping of relaxation times from various types of images. To simplify the search space for the optimum fit, using the partial linear relationship between signal intensity and fitting parameters, an alternative computational tool to compute relaxation times has been proposed and validated. 3. Cardiac Segmentation and Quantification: Evaluation of ventricular volumes and function is considered to be one of the crucial tasks in the management of patients with different heart diseases. Cardiac segmentation of right and left ventricles is necessary when it is intended to study these heart chambers or to extract local densitometric information. A reliable and robust image processing tool to extract cardiovascular MRI derived measurements of both ventricles has been developed and validated during this project.