Method for analyzing magnetic resonance imaging data "Magnetic resonance fingerprinting MRF"

Author: George Kachlishvili
Keywords: magnetic resonance imaging, data analysis, dictionary
Annotation:

MRF data analysis is a relatively new statistical method of processing and correct interpretation of data. It main and at the same time unique approach is to identify and correctly interpret the so-called hidden parameters using a dictionary on a theoretical basis. The quality of its likelihood is determined by the statistical volume of the date. This means that the same date is recorded repeatedly at different angles, from where a special algorithm calculates average statistical data excluding random fluctuations. The obtained average statistical data is compared with a similar theoretical data from the dictionary, which is used to determine the desired hidden parameters. From the mathematical apparatus FFT, convolution and 〖FFT〗^(-1) are mainly used. From the very beginning, MRF has been designed to modernize and improve the results of magnetic resonance imaging apparatus. For example, there was an attempt at the initial stage to determine Alzheimer's disease. But today it became clear that similar data analysis and algorithms can be very successfully used for other purposes. MRF is often referred to as machine learning.