Skip to article frontmatterSkip to article content

Data Fitting

MP2RAGE

NeuroPoly Lab, Polytechnique Montreal, Quebec, Canada

Dictionary-based techniques such as MP2RAGE do not typically use conventional minimization algorithms (e.g. Levenberg-Marquardt to fit signal equations to observed data. Instead, the MP2RAGE technique uses pre-calculated signal values for a wide range of parameter values (e.g. T1), and then interpolation is done within this dictionary of values to estimate the T1 value that matches the observed signal. This approach results in rapid post-processing times because the dictionaries can be simulated/generated prior to scanning and interpolating between these values is much faster than most fitting algorithms. This means that the quantitative image can be produced and displayed directly on the MRI scanner console rather than needing to be fitted offline.

Loading...

Figure 2.14:T1 lookup table as a function of T1 and SMP2RAGE value. Inversion times used to acquire this magnitude image dataset were 800 ms and 2700 ms, the flip angles were 4° and 5° (respectively), TRMP2RAGE = 6000 ms, and TR = 6.7 ms. The code that was used were shared open sourced by the authors of the original MP2RAGE paper Marques, 2017.

To produce T1 maps with good accuracy and precision using dictionary-based interpolation methods, it is important that the signal curves are unique for each parameter value. MP2RAGE can produce good T1 maps by using a dictionary with only dimensions (T1, SMP2RAGE), since SMP2RAGE is unique for each T1 value for a given protocol Marques et al., 2010. However, as was noted above, SMP2RAGE is also sensitive to B1 because of θ1\theta_{1} and θ2\theta_{2} in Equations 2.13, 2.14, 2.15, and 2.16. The B1-sensitivity can be reduced substantially with careful MP2RAGE protocol optimization Marques et al., 2010, and further improved by including B1 as one of the dictionary dimensions [T1, B1, SMP2RAGE] (Figure 2.14). This requires an additional acquisition of a B1 map Marques & Gruetter, 2013, which lengthens the scan time.

Loading...

Figure 2.15:Example MP2RAGE dataset of a healthy adult brain at 7T and T1 map. Inversion times used to acquire this magnitude image dataset were 800 ms and 2700 ms, the flip angles were 4° and 5° (respectively), TRMP2RAGE = 6000 ms, and TR = 6.7 ms. The dataset and code that was used were shared open sourced by the authors of the original MP2RAGE paper Marques, 2017.

The MP2RAGE pulse sequence is increasingly being distributed by MRI vendors, thus typically a data fitting package is also available to reconstruct the T1 maps online. Alternatively, several open source packages to create T1 maps from MP2RAGE data are available online Marques, 2017Hollander, 2017, and for new users these are recommended—as opposed to programming one from scratch—as there are many potential pitfalls (e.g. adjusting the equations to handle partial Fourier or parallel imaging acceleration).

References
  1. Marques, J. (2017). MP2RAGE related scripts. https://github.com/JosePMarques/MP2RAGE-related-scripts
  2. Marques, J. P., Kober, T., Krueger, G., van der Zwaag, W., Van de Moortele, P.-F., & Gruetter, R. (2010). MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. Neuroimage, 49(2), 1271–1281.
  3. Marques, J. P., & Gruetter, R. (2013). New Developments and Applications of the MP2RAGE Sequence - Focusing the Contrast and High Spatial Resolution R1 Mapping. PLoS One, 8(7), e69294.
  4. de Hollander, G. (2017). PyMP2RAGE. https://github.com/Gilles86/pymp2rage