Magnetization transfer induced biexponential longitudinal relaxation

Abstract

Longitudinal relaxation of brain water 1 H magnetization in mammalian brain in vivo is typically analyzed on a per-voxel basis using a monoexponential model, thereby assigning a single relaxation time constant to all 1 H magnetization within a given voxel. This approach was tested by obtaining inversion recovery (IR) data from gray matter of rats at 64 exponentially spaced recovery times. Using Bayesian probability for model selection, brain water data were best represented by a biexponential function characterized by fast and slow relaxation components. At 4.7T, the amplitude fraction of the rapidly relaxing component is 3.4% ؎ 0.7% with a rate constant of 44 ؎ 12 s -1 (mean ؎ SD; 174 voxels from four rats). The rate constant of the slow relaxing component is 0.66 ؎ 0. Longitudinal relaxation rate constant (R 1 ϭ 1/T 1 ) measurements of tissue water are key to a variety of MR methods, including dynamic contrast-enhanced techniques (1) and assessment of multiple sclerosis patients for damage to normal-appearing white matter (2). Data from such measurements in brain are typically modeled as a monoexponential magnetization recovery, assuming that the longitudinal relaxation of all water can be represented by a single R 1 . However, in vivo voxel resolution is coarse on the scale of tissue microstructure and water exists in a variety of magnetic environments ("compartments") within a single in vivo voxel. Each compartment potentially provides a unique relaxation environment for water. Consistent with this concept, a variety of tissues display multiexponential T 2 relaxation and each exponential component can be assigned to a unique anatomical compartment (3,4). Further, multiple R 1 components have been described for peripheral nerve (4,5), though not for brain gray matter.Non-monoexponential in vivo relaxation data are generally analyzed by one of two methods. The first method models relaxation data as the sum of a small number of discrete exponential functions, with each exponential component having a unique amplitude and R 1 . In this case, the implicit biophysical picture is that water within a given voxel can be approximated as residing in a few separate magnetic environments that are not in fast exchange. In the limit of slow exchange this would enable measurement of compartment-specific R 1 values. The second method models relaxation data with a continuous distribution of exponential functions. Here the implicit biophysical picture is that water within a given voxel should be approximated as residing in a large number of separate magnetic environments that are not in fast exchange (6,7).Taking advantage of high-field magnets equipped with high-performance gradients, the study reported herein was done using highly time-resolved inversion recovery (IR) spin-echo echo-planar imaging (SEPI) to acquire data sets with 64 to 128 exponentially spaced inversion time (TI) values. Bayesian probability theory (8,9) was employed to select among models composed of sums of from one to four exponentials. The relaxation data...

Magnetic resonance microdynamic imaging reveals distinct tissue microenvironments

“…2bI and II, respectively. Magnetization transfer (MT) between bulk water protons and nonaqueous protons (e.g., protons residing on proteins) was shown to be the source of short T 1 components in the brain (Gochberg and Gore, 2007; Prantner et al, 2008). The third identified short T 1 and T 2 GM peak (Fig.…”

Effects of magnetization transfer on T 1 contrast in human brain white matter

“…Previous studies have recognized the potential contribution of MT to bi-exponential signal recovery after inversion of WP magnetization, and were able to explain experimental IR data with a two-pool model of MT between WP (Gochberg, Kennan et al 1997, Gochberg, Kennan et al 1999, Prantner, Bretthorst et al 2008, Labadie, Lee et al 2014). The RF energy dependence of the recovery observed in the current study further solidifies this notion.…”

“…In pure liquids with only one species of 1 H, M(t) can generally be described by a single exponential function, characterized by time constant T 1 . For the more complex situation of brain tissue, it has been suggested that M(t) can be approximated by using a two-pool model of MT between WP and MP, which leads to bi-exponential behavior (Zimmerman and Britten 1957, Gochberg, Kennan et al 1997, Prantner, Bretthorst et al 2008, Labadie, Lee et al 2014): $$S_{WP}false(tfalse)=1-1\frac{M_{WP}(t)}{M_{WP}(\infty )}=a_1{e}^{-{\lambda }_{1}t}+a_2{e}^{-{\lambda }_{2}t}$$ $$2{\lambda }_{1,2}=R_{1,WP}+R_{1,MP}+k_{MW}+k_{WM}\pm \sqrt{{false(R_{1,WP}-R_{1,WP}+k_{MW}-k_{WM}false)}^2+4k_{MW}{k}_{WM}}$$ $$a_{1,2}=\pm 1\frac{S_{WP}(0)(R_{1,WP}+k_{WM}-{\lambda }_{2,1})-S_{MP}(0)k_{WM}}{{\lambda }_1-{\lambda }_2}$$ $$false(1-ffalse)k_{WM}={fk}_{MW}$$…”

Biexponential longitudinal relaxation in white matter: Characterization and impact on T1 mapping with IR‐FSE and MP2RAGE

“…Although early studies of human WM at 1.5T (20) found that this biexponential T 1 relaxation did not contribute significantly to signal evolution, it is more easily detected and quantified at higher field strengths. Prantner et al (21) tested several hypotheses that could explain the biexponential T 1 behavior observed in rat brains at 4.7T and 11.7T. These mechanisms included sequence or scanner artifact, blood flow, and multiple exchanging or nonexchanging compartments.…”