Lipid Quantification Tool Development Using Multi-Echo MRI
Lipid quantification is a valuable tool in measuring fatty liver disease. While it is possible to gather quantification information using biopsy, it is less invasive and more comprehensive to perform quantification for an entire organ using magnetic resonance spectroscopy methods. While MRI signal is largely due to the presence of water molecules, it can also be affected by large concentrations of other molecules. Distinct molecules will in turn have distinct magnetic characteristics, which allow their relative concentrations to be calculated based on temporal signals. That is, different types of lipids and molecules of water will precess at different characteristic frequencies. Sampling the signal intensity at different echo times will produce different values depending on the array of species that are present. If a knock-down pulse is used for the water molecules, it becomes easier to isolate and measure the signal due to fat.
A toolkit was developed at Invicro to calculate the relative concentration of fat and water in biological tissue using MR images. A look up table approach was used to isolate the appropriate spectroscopic parameters, as the precession frequency will change slightly due to thermal effects. A series of MR readout images with varying echo times are fed into the algorithm, which performs a regularized constraint optimization process to recover the relative quantity of fat and water for each voxel based on fitting to established precession equations.
The toolkit described here was initially written using MATLAB (MathWorks, Inc., Natick, MA). However, due to the resounding success and robustness of the model, it was rewritten as a plugin for VivoQuant®. The VivoQuant® implementation of the code base is both more versatile and more robust. Using the tools developed at Invicro, we were able to develop, implement, and test an experimental quantification toolkit. The speed at which the model could be run allowed for batch processing to examine the validity of the model under differing experimental conditions.
For more information on the Lipid Quantification Tool, please contact email@example.com.