Magnetization Transfer & T1 Contrast: Mechanisms, Sensing & Quantifying
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

Magnetic resonance imaging (MRI) yields exquisite soft tissue contrast. This lecture focuses on longitudinal relaxation and magnetization transfer (MT) as contrast mechanisms. For both contrast mechanisms the basic theoretical description and definitions are introduced. It is discussed how they are affected by microstructural characteristics, particularly macromolecular content and myelination in the brain. Different acquisition and analysis methods are described for sensing and quantifying the longitudinal relaxation time (T1) and parameters of MT. Examples of the use of T1 and MT mapping in neuroimaging with a focus on myelin mapping are explored.

Target Audience

Physicists, engineers or scientists with training in MRI who are interested in quantitative MRI.

Outcome/Objectives

  • Describe T1 relaxation and magnetization transfer (MT) effects
  • Develop an understanding of how T1 and MT contrasts relate to tissue characteristics
  • Understand basic pulse sequences for measuring T1 and MT
  • Understand basic data processing steps for estimating T1 and MT from the acquired data
  • Explore examples of the application of T1 and MT mapping

Longitudinal Relaxation

Magnetic resonance imaging (MRI) yields different types of exquisite soft tissue contrast. This lecture focuses on longitudinal relaxation and magnetization transfer (MT) as contrast mechanisms [1]. Both contrast mechanisms are sensitive to macromolecular content and myelination, which makes them popular for anatomical imaging of the brain, e.g., differentiating between highly myelinated white matter and less myelinated gray matter.
The phenomenological Bloch equations [2] include longitudinal relaxation and its associated relaxation time T1 to describe the recovery of the longitudinal magnetization component aligned with the main field. The structure of biological tissue is highly complex, leading to further refinements of relaxation theory in tissue [4]–[6]. Although the relaxation mechanisms in tissue are not fully understood, it is known that relaxivity is increased due to water-binding sites at the protein-water interface where significant relaxation occurs [4]. Such sites are found in macromolecules in the brain, particularly in myelin [7].
The fundamental experiment for estimating the relaxation time T1 consists of tracking the recovery of the longitudinal magnetization after an RF inversion pulse [8]. In practice several measurements are performed varying the inversion time (TI) between the inversion pulse and the readout. T1 can be obtained by an exponential fit of the recovery curve. However, the experiments are time consuming due to the many measurements and the need to achieve equilibrium conditions between consecutive experiments. Thus, alternative faster techniques were developed such as the Look-Locker method [9], [10], inversion recovery preparations combined with fast readouts (MP2RAGE, [11]) and variable flip angle (VFA) technique [12], [13]. The Look-Locker shortens the total measurement time by repeated sampling during one single T1 relaxation process. The VFA method acquires multiple spoiled gradient echo images with different excitation flip angles. The different measurement techniques suffer from different pitfalls but generally inhomogeneities in the RF transmit field affect their accuracy [14]–[16].

Magnetization Transfer

Recognizing the higher complexity of tissue leads to models of two or multiple pools of spin ensembles in different molecular environments, which exchange magnetization [17]–[19]. Henkelman et al. [20] proposed a model consisting of two exchanging pools: a liquid/free water pool and a macromolecular/bound water pool. The formal modelling provides a strong theoretical foundation for understanding the MT phenomenon and important information about relaxation rates, exchange rates and pool sizes. However, semi-empirical approaches and reduced models were also developed and are used widely due to their easier implementation and shorter acquisition times [21], [22].
A variety of data acquisition approaches were developed for measuring MT effects [1]. However, on clinical MRI systems using off-resonance pulses for saturating the macromolecular pool and measuring the consequent MT effect is the most widely used approach. The widely used magnetization transfer ratio (MTR; [22]) is defined as the relative reduction of signal intensity comparing an image (typically spoiled gradient echo) acquired with the RF saturation pulse to an image without the additional saturation pulse. MTR depends on the specific implementation of the pulse sequence, such as the MT saturation pulse characteristics, repetition time and others. In addition, subject specific factors can impact MTR, such as local T1 and RF transmit field values [23]. The MT saturation [21], which is based on three spoiled gradient echo acquisitions, addresses several of these shortcomings while maintaining rather short acquisition times compared to acquisitions required for comprehensive fitting of two pool models. The different MT mapping approaches have different limitations but variations in the static magnetic field B0 and RF transmit field generally impact them.
Both T1 and different MT metrics are sensitive to the macromolecular content and myelin [24], [25]. Thus, they are used for anatomical imaging [11], [26], [27] and microstructure imaging in e.g. development [28], [29], aging [30], CNS injury [31] and neurodegeneration [32].

Conclusion

The longitudinal relaxation time (T1) and magnetization transfer (MT) parameters depend on features of the tissue microstructure, particularly myelination in the brain. The pronounced contrasts are widely exploited in neuroimaging, although the exact details of the relation to the microstructure are still under investigation. Several acquisition techniques and modelling approaches were developed with different strengths and challenges, offering a broad range of options to tailor the approach to requirements of the planned neuroimaging study.

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.

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