Quantitative magnetization transfer imaging (qMTI) increases specificity to macromolecular content in tissue by modeling the exchange process between the liquid and the macromolecular pool. However, its use has been limited, in part due to the need to write complicated in-house software for modeling and data analysis. We have developed a free open source software, qMTLab (https://github.com/neuropoly/qMTLab/), that unifies the most widely used qMTI methods and allows one to easily simulate qMTI data, compare the performance of the methods under various experimental conditions, define new acquisition protocols, fit acquired data, and visualize the fitted parameters maps. By providing free software with a simple and easy to use graphical interface, we hope to make qMTI accessible to a greater number of investigators and facilitate the development and optimization of protocols.

We will begin by presenting an introduction on the theory behind qMTI. We will then explain the current acquisition methods and the analytical solutions that have been developed to extract useful physical parameters about this model. Finally, we will present the functionality of the qMTLab software and demonstrate its utility.

Theory

In vivo qMTI methods are based on a model that describes hydrogen nuclei in tissues as composed of two distinct pools: a free pool of highly mobile protons associated with water, and a restricted pool of less mobile protons residing in macromolecules (Figure 1)^1,2. This model is characterised by seven parameters: pool size ratio (F); exchange rate (either k_r or k_f, the two being related by F = k_f/k_r); longitudinal relaxation rates (R_1f, R_1r); transverse relaxation times (T_2f, T_2r) and the equilibrium magnetization (M_0f, with M_0r = F×M_0f). Under certain assumptions, analytical solutions can be derived and a signal equation can be fitted to the data, from which the qMTI parameters are derived.

Three acquisition protocols can be used for qMTI. MT-SPGR is a steady-state acquisition which combines off-resonance pulsed saturation (variable duration and power) of the restricted pool with an SPGR readout (Figure 2a)^3,4. SIR-FSE uses a selective inversion recovery (SIR) sequence with variable inversion times followed by a fast spin echo (FSE) readout (Figure 2b)^5,6. In MT-bSSFP, a steady-state acquisition is performed by applying a series of equally spaced, on-resonance RF pulses of variable duration and flip angles (Figure 2c)⁷.

qMTLab software presentation

The software consists of two parts: 1) a qMTI data simulator, and 2) a qMTI data fitting and visualization interface. The simulation part allows users to generate synthetic qMTI data using the above described protocols, evaluate how well they perform under known ground-truth parameters, determine the most appropriate acquisition parameters, and evaluate how fitting constraints impact the results. The data fitting part provides an interface to import qMTI data, fit models to the data and visualize the resulting parameters maps. An example of the graphical user interface is presented in Figure 5.

Synthetic data

Three modes of simulation are offered (Figure 6 a-c): 1) Single Voxel Curve, to simulate MT data from a single voxel; 2) Sensitivity Analysis, allows systematic variation of one MT parameter, over a defined range and number of points, while keeping the others fixed; and 3) Multi Voxel Distribution, where any parameter combination is allowed to be varied simultaneously for a number of voxels.

Data fitting

The data fitting part of qMTLab (Figure 6d) provides a simple interface to load acquired qMTI data, fit a model and visualize the resulting parameters maps. The user can provide a qMTI data file, select or define the protocol used for acquisition and set the fitting options. The user can also load a mask to constrain the areas to be analyzed, an observed R₁ map for R_1f evaluation, a B₁ map for MT pulse power or excitation flip angle correction and a B₀ map for correction of offset frequencies. Fitting results are displayed as parametric maps with user-controllable color scales.

The ability to map quantitative values such as the rate of magnetization exchange between free and restricted protons or the ratio of the proton pools makes qMTI a valuable tool in characterizing tissue microstructure. By providing free software that gives end users a simple and easy-to-use graphical interface, we hope to make qMTI accessible to a greater number of investigators and facilitate the development and optimization of protocols for research in clinical populations. The open-source nature of qMTLab makes it particularly useful, in that it is subject to continuous improvement and that users can customize it and add functionality to fit individual needs.

The qMTLab software can be downloaded for free at: https://github.com/neuropoly/qMTLab/