Multi-Parameter Mapping of R1, PD, MT & R2*
Nikolaus Weiskopf1,2
1Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 2Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany

Synopsis

Multi-parameter mapping (MPM) based on multi-echo spoiled gradient echo acquisitions can provide estimates of the longitudinal relaxation rate (R1), effective transverse relaxation rate (R2*), proton density (PD) and magnetization transfer (MT) saturation. The basic data acquisition scheme is introduced together with the required data analysis and modelling steps. Important implementation aspects, potential pitfalls and limitations are discussed. Different examples are presented of how MPM are used for neuroimaging, including whole-brain and cortical microstructure imaging in aging and trauma of the central nervous system.

Target Audience

Physicists, engineers, scientists or clinicians with training in MRI who are interested in quantitative MRI and multi-contrast assessment of tissue.

Outcome/Objectives

  • Understand basics of multi-parameter mapping (MPM) using multi-echo spoiled gradient echo acquisitions
  • Understand how to estimate R1, PD, MT and R2* from acquired data
  • Avoid pitfalls and understand limitations of MPM
  • Explore examples of how MPM are applied in neuroimaging

Background

Magnetic resonance imaging (MRI) offers different types of exquisite soft tissue contrast. Since the different contrast mechanisms are preferentially sensitive to different underlying tissue microstructural features, a more comprehensive view of the tissue microstructure can be achieved by imaging of multiple MRI contrast parameters. This talk focuses on multi-parameter mapping (MPM) based on multi-echo spoiled gradient echo sequences [1], [2], which yields estimates of the longitudinal relaxation rate (R1), effective transverse relaxation rate (R2*), proton density (PD) and magnetization transfer (MT) saturation.

Data Acquisition

The data acquisition is typically based on acquiring a multi-echo radio-frequency (RF) spoiled gradient echo sequence with PD-, T1- and MT-weighting by choice of an appropriate excitation flip angle and repetition time (TR) combination [1], [3]. MT-weighting is achieved by an additional off-resonance RF saturation pulse (typically 2kHz off-resonant). Images are acquired at typically 6-8 different echo times (TE) for each of the three differently weighted gradient echo sequence acquisitions. Additionally acquired reference data typically include RF transmit (B1+) and receive (B1-) field maps [4]. Since spoiled gradient echo sequences are available on most MRI scanner platforms, MPM protocols can be readily implemented (for examples, see [5] and http://www.hmri.info). The multi-echo spoiled gradient echo sequences have a high data acquisition efficiency and thus enable high spatial resolution scans at short scan times with a high signal-to-noise ratio (SNR).

Data Processing

The signal of the differently weighted gradient echo acquisitions can be modelled by the Ernst equation including an empirical description of the MT saturation [2], [6]. The entire dataset can be described by the unified ESTATICS model integrating all images [3], [7], yielding R1, PD, R2*, MT maps and improving the maps’ SNR. RF transmit field maps are used to minimize transmit field effects on the R1 and MT saturation maps [1]. RF receive field maps are used in addition to data driven methods to compensate for receive sensitivity differences affecting the PD maps [8]–[11]. The data analysis steps are similar to the ones applied to dual flip angle spoiled gradient echo acquisitions for R1 and PD mapping (e.g., DESPOT1, [12]). The estimation of R2* is similar to a log-linear fit of the signal in the image versus the image’s echo time. All map estimation methods for MPM are implemented in the open source hMRI-toolbox (http://www.hmri.info; [3]). A number of other open source toolboxes are available that also cover similar processing steps, e.g. qMRLab [13], QUIT [14], mrQ [10].

Considerations

Although the basic implementation of the data acquisition and processing methods is straightforward, several aspects need to be addressed for optimal data quality. A correction of RF transmit and receive field inhomogeneities is required to minimize biases in R1, PD and MT saturation maps [3], [4], [8]–[11]. Since data are acquired at a finite minimal TE, an extrapolation to zero TE is required to yield accurate PD values [3], [15]. The estimations are based on the Ernst equation assuming perfect spoiling of all transverse coherences. In practice, only an imperfect spoiling can be achieved resulting in deviations from the idealized signal characteristics [16], which can be corrected for in post-processing [17], [18]. MT effects can further impact signal characteristics and bias estimates, but may be addressed by controlled saturation magnetization transfer (CSMT; [19]) balancing MT effects across acquisitions with different excitation flip angles. Flow, off-resonance and chemical shift may further impact the data quality. Precision and SNR of the maps can be optimized based on the error propagation from the underlying weighted images [20]. When optimized the quantitative maps facilitate multi-center and longitudinal studies, since data points are better comparable across sites and time points [1], [21], [22].

Applications

MPM were applied in different areas of neuroimaging. Studies investigated e.g. developmental [23] and aging [24], [25] related changes. Non-invasive mapping of intracortical myelination was another important target of MPM, particularly of high resolution acquisitions with 400-800µm resolution [26]–[28]. In addition to normal controls, different patient cohorts were studied, including e.g. spinal cord injury [29]. Since multi-center approaches play a more important role in clinical studies, different studies have addressed the reproducibility of MPM and related methods [1], [22], [30].

Conclusion

MPM yields high resolution R1, R2*, PD and MT saturation maps in clinically feasible times (1mm isotropic in < 20 mins). They can be implemented on most scanner platforms, since multi-echo spoiled gradient echo sequences are widely available and analysis tools are publicly available (e.g., http://www.hmri.info). Thus, MPM has been widely used in basic and clinical neuroscience.

Acknowledgements

NW received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013) / ERC grant agreement n° 616905; from the European Union's Horizon 2020 research and innovation programme under the grant agreement No 681094; from the BMBF (01EW1711A & B) in the framework of ERA-NET NEURON.

References

[1] N. Weiskopf et al., “Quantitative multi-parameter mapping of R1, PD*, MT, and R2* at 3T: a multi-center validation,” Front. Brain Imaging Methods, vol. 7, p. 95, 2013, doi: 10.3389/fnins.2013.00095.

[2] G. Helms, H. Dathe, K. Kallenberg, and P. Dechent, “High-resolution maps of magnetization transfer with inherent correction for RF inhomogeneity and T1 relaxation obtained from 3D FLASH MRI,” Magn. Reson. Med. Off. J. Soc. Magn. Reson. Med. Soc. Magn. Reson. Med., vol. 60, no. 6, pp. 1396–1407, Dec. 2008, doi: 10.1002/mrm.21732.

[3] K. Tabelow et al., “hMRI - A toolbox for quantitative MRI in neuroscience and clinical research,” NeuroImage, Jan. 2019, doi: 10.1016/j.neuroimage.2019.01.029.

[4] A. Lutti, C. Hutton, J. Finsterbusch, G. Helms, and N. Weiskopf, “Optimization and validation of methods for mapping of the radiofrequency transmit field at 3T,” Magn. Reson. Med. Off. J. Soc. Magn. Reson. Med. Soc. Magn. Reson. Med., vol. 64, no. 1, pp. 229–238, Jul. 2010, doi: 10.1002/mrm.22421.

[5] M. F. Callaghan et al., “Example dataset for the hMRI toolbox,” Data Brief, vol. 25, p. 104132, Aug. 2019, doi: 10.1016/j.dib.2019.104132.

[6] G. Helms, H. Dathe, and P. Dechent, “Quantitative FLASH MRI at 3T using a rational approximation of the Ernst equation,” Magn Reson., vol. 59, no. 3, pp. 667–672, 2008.

[7] N. Weiskopf, M. F. Callaghan, O. Josephs, A. Lutti, and S. Mohammadi, “Estimating the apparent transverse relaxation time (R2(*)) from images with different contrasts (ESTATICS) reduces motion artifacts,” Front. Neurosci., vol. 8, p. 278, 2014, doi: 10.3389/fnins.2014.00278.

[8] D. Papp, M. F. Callaghan, H. Meyer, C. Buckley, and N. Weiskopf, “Correction of inter-scan motion artifacts in quantitative R1 mapping by accounting for receive coil sensitivity effects,” Magn. Reson. Med., vol. 76, no. 5, pp. 1478–1485, 2016, doi: 10.1002/mrm.26058.

[9] S. Lorio et al., “Flexible proton density (PD) mapping using multi-contrast variable flip angle (VFA) data,” NeuroImage, vol. 186, pp. 464–475, 01 2019, doi: 10.1016/j.neuroimage.2018.11.023.

[10] A. Mezer, A. Rokem, S. Berman, T. Hastie, and B. A. Wandell, “Evaluating quantitative proton-density-mapping methods,” Hum. Brain Mapp., vol. 37, no. 10, pp. 3623–3635, 2016, doi: 10.1002/hbm.23264.

[11] S. Volz, U. Nöth, A. Jurcoane, U. Ziemann, E. Hattingen, and R. Deichmann, “Quantitative proton density mapping: correcting the receiver sensitivity bias via pseudo proton densities,” NeuroImage, vol. 63, no. 1, pp. 540–552, Oct. 2012, doi: 10.1016/j.neuroimage.2012.06.076.

[12] S. C. Deoni, T. M. Peters, and B. K. Rutt, “High-resolution T1 and T2 mapping of the brain in a clinically acceptable time with DESPOT1 and DESPOT2,” Magn Reson., vol. 53, no. 1, pp. 237–241, Jan. 2005, doi: 10.1002/mrm.20314 [doi].

[13] J.-F. Cabana et al., “Quantitative magnetization transfer imaging made easy with qMTLab: Software for data simulation, analysis, and visualization,” Concepts Magn. Reson. Part A, vol. 44A, no. 5, pp. 263–277, 2015, doi: 10.1002/cmr.a.21357.

[14] T. Wood, “QUIT: QUantitative Imaging Tools,” J. Open Source Softw., vol. 3, no. 26, p. 656, Jun. 2018, doi: 10.21105/joss.00656.

[15] H. Neeb, V. Ermer, T. Stocker, and N. J. Shah, “Fast quantitative mapping of absolute water content with full brain coverage,” NeuroImage, vol. 42, no. 3, pp. 1094–1109, Sep. 2008, doi: 10.1016/j.neuroimage.2008.03.060.

[16] V. L. Yarnykh, “Optimal radiofrequency and gradient spoiling for improved accuracy of T1 and B1 measurements using fast steady-state techniques,” Magn. Reson. Med. Off. J. Soc. Magn. Reson. Med. Soc. Magn. Reson. Med., vol. 63, no. 6, pp. 1610–1626, Jun. 2010, doi: 10.1002/mrm.22394.

[17] C. Preibisch and R. Deichmann, “Influence of RF spoiling on the stability and accuracy of T1 mapping based on spoiled FLASH with varying flip angles,” Magn Reson., vol. 61, no. 1, pp. 125–135, Jan. 2009.

[18] S. Baudrexel, U. Nöth, J.-R. Schüre, and R. Deichmann, “T1 mapping with the variable flip angle technique: A simple correction for insufficient spoiling of transverse magnetization,” Magn. Reson. Med., vol. 79, no. 6, pp. 3082–3092, 2018, doi: 10.1002/mrm.26979.

[19] “Fast quantitative MRI using controlled saturation magnetization transfer - A.G. Teixeira - 2019 - Magnetic Resonance in Medicine - Wiley Online Library.” [Online]. Available: https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.27442. [Accessed: 23-Feb-2020].

[20] G. Helms, H. Dathe, N. Weiskopf, and P. Dechent, “Identification of signal bias in the variable flip angle method by linear display of the algebraic ernst equation,” Magn. Reson. Med. Off. J. Soc. Magn. Reson. Med. Soc. Magn. Reson. Med., Mar. 2011, doi: 10.1002/mrm.22849.

[21] R.-M. Gracien et al., “How stable is quantitative MRI? - Assessment of intra- and inter-scanner-model reproducibility using identical acquisition sequences and data analysis programs,” NeuroImage, vol. 207, p. 116364, Feb. 2020, doi: 10.1016/j.neuroimage.2019.116364.

[22] Y. Lee, M. F. Callaghan, J. Acosta-Cabronero, A. Lutti, and Z. Nagy, “Establishing intra- and inter-vendor reproducibility of T1 relaxation time measurements with 3T MRI,” Magn. Reson. Med., vol. 81, no. 1, pp. 454–465, 2019, doi: 10.1002/mrm.27421.

[23] K. J. Whitaker et al., “Adolescence is associated with genomically patterned consolidation of the hubs of the human brain connectome,” Proc. Natl. Acad. Sci. U. S. A., vol. 113, no. 32, pp. 9105–9110, 09 2016, doi: 10.1073/pnas.1601745113.

[24] M. F. Callaghan et al., “Widespread age-related differences in the human brain microstructure revealed by quantitative magnetic resonance imaging,” Neurobiol. Aging, vol. 35, no. 8, pp. 1862–1872, Aug. 2014, doi: 10.1016/j.neurobiolaging.2014.02.008.

[25] B. Draganski et al., “Regional specificity of MRI contrast parameter changes in normal ageing revealed by voxel-based quantification (VBQ),” NeuroImage, vol. 55, no. 4, pp. 1423–1434, Apr. 2011, doi: 10.1016/j.neuroimage.2011.01.052.

[26] F. Dick, A. Tierney, A. Lutti, O. Josephs, M. I. Sereno, and N. Weiskopf, “In vivo functional and myeloarchitectonic mapping of human primary auditory areas,” J. Neurosci., vol. 32, pp. 16095–105, 2012.

[27] M. I. Sereno, A. Lutti, N. Weiskopf, and F. Dick, “Mapping the human cortical surface by combining quantitative T1 with retinotopy,” Cereb. Cortex N. Y. N 1991, vol. 23, no. 9, pp. 2261–2268, Sep. 2013, doi: 10.1093/cercor/bhs213.

[28] R. Trampel, P.-L. Bazin, K. Pine, and N. Weiskopf, “In-vivo magnetic resonance imaging (MRI) of laminae in the human cortex,” NeuroImage, Sep. 2017, doi: 10.1016/j.neuroimage.2017.09.037.

[29] P. Freund et al., “MRI investigation of the sensorimotor cortex and the corticospinal tract after acute spinal cord injury: a prospective longitudinal study,” Lancet Neurol., vol. 12, no. 9, pp. 873–881, Sep. 2013, doi: 10.1016/S1474-4422(13)70146-7.

[30] S. C. L. Deoni, S. C. R. Williams, P. Jezzard, J. Suckling, D. G. M. Murphy, and D. K. Jones, “Standardized structural magnetic resonance imaging in multicentre studies using quantitative T1 and T2 imaging at 1.5 T,” NeuroImage, vol. 40, no. 2, pp. 662–671, Apr. 2008, doi: 10.1016/j.neuroimage.2007.11.052.

Proc. Intl. Soc. Mag. Reson. Med. 28 (2020)