Predicting T1 and T2
How can you predict the T1 and T2 values of different tissues?
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At present there is no comprehensive theory that allows one to reliably predict the exact T1 and T2 values of different tissues. Sophisticated theories have been developed which adequately explain the relaxation properties of simple solutions and homogeneous preparations of macromolecules. However, tissues are extraordinarily complex, containing a wide variety of different molecules and possessing structure at both the microscopic and macroscopic levels. Even water is not simple to analyze in tissues; it may exist in multiple states, ranging from free (unbound) to partially structured to fully bound.
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The relaxation properties of most tissues can be explained as in terms of 2- or 3-compartment models, after various assumptions are made regarding exchange rates, water fractions, and the like. One of the oldest and best known of these, first proposed in 1957, is the fast exchange model of Zimmerman and Brittin. A more modern multicompartment model is illustrated in the figure right, which shows different contributions to T1 and T2. It should be kept in mind that all such models are only crude approximations of "reality", and it is possible to construct many equivalent models.
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To predict the relative T1 and T2 values of different tissues and to explain their general appearance at MR imaging, it must first be decided whether the observed signal comes principally from lipid or water protons. For most tissues, water protons generate most (if not all) of the MR signal. In fact, both T1 and T2 values correlate most powerfully and simply with the bulk water content of a tissue. As a rule, the "squishier" or "juicier" a tissue, the longer will be its T1 and T2 values. For example, this principle may be used to predict (and remember) that the T1 and T2 values of the renal medulla (where the urine collects) are longer than those of the renal cortex; the spleen (with more blood) has longer T1 and T2 than the liver, and so forth. Although this "squishiness" concept should be considered purely a mnemonic device and one for which exceptions can easily be found, it has nevertheless served me well over the last several years. In fact, using this crude concept as a starting point, many years ago I was able to show (after rigorous, double blind, radiologic-pathologic correlation, of course) why some meningiomas are dark and others are bright on MR images.
Although in most tissues bulk water content is a strong predictor of T1 and T2, in other tissues the contribution of aliphatic lipid protons to the MR signal must be considered. These nonpolar storage fats have short T1 values, but relatively long T2 values (as they are intermediate in size, their motions are close to the Larmor frequency and there are few static contributions to allow T2 dephasing.) In adipose tissue, for example, nearly all the MR signal arises from such lipid protons. In other tissues, such as bone marrow, liver, and skeletal muscle, fat and water protons each make significant contributions to the total signal and net relaxation times. Here careful measurements will actually reveal multicomponent contributions to both T1 and T2.
Although in most tissues bulk water content is a strong predictor of T1 and T2, in other tissues the contribution of aliphatic lipid protons to the MR signal must be considered. These nonpolar storage fats have short T1 values, but relatively long T2 values (as they are intermediate in size, their motions are close to the Larmor frequency and there are few static contributions to allow T2 dephasing.) In adipose tissue, for example, nearly all the MR signal arises from such lipid protons. In other tissues, such as bone marrow, liver, and skeletal muscle, fat and water protons each make significant contributions to the total signal and net relaxation times. Here careful measurements will actually reveal multicomponent contributions to both T1 and T2.
References
Bryant RG, Korb J-P. Nuclear magnetic resonance and spin relaxation in biological systems. Mag Reson Imaging 2005; 23:167-173.
Halle B. Water in biological systems: the NMR picture. In: Bellissent-Funel M-C (ed.). Hydration processes in biology : theoretical and experimental approaches. IOS Press, Clifton, VA. 1999: 233-249.
Koenig SH. Molecular basis of magnetic relaxation of water protons of tissue. Acad Radiol 1996;3:597-606.
Ma D, Gulani V, Seiberlich N, et al. Magnetic resonance fingerprinting. Nature 2013; 495:187-193. (See Advanced Discussion for comments about this landmark paper and their followup review below).
Mehta BB, Coppo S, McGivney DF, et al. Magnetic resonance fingerprinting: a technical review. Magn Reson Med 2019; 81:25-46.
Sobol WT, Cameron IG, Inch WR, Pintar MM. Modeling of proton spin relaxation in muscle tissue using nuclear magnetic resonance spine grouping and exchange analysis. Biophys J 1986;50:181-191.
Zimmerman JR, Brittin WE. Nuclear magnetic resonance studies in multiple phase systems: lifetime of a water molecule in an adsorbing phase on silica gel. J Phys Chem 1957; 61:1328-1333.
Bryant RG, Korb J-P. Nuclear magnetic resonance and spin relaxation in biological systems. Mag Reson Imaging 2005; 23:167-173.
Halle B. Water in biological systems: the NMR picture. In: Bellissent-Funel M-C (ed.). Hydration processes in biology : theoretical and experimental approaches. IOS Press, Clifton, VA. 1999: 233-249.
Koenig SH. Molecular basis of magnetic relaxation of water protons of tissue. Acad Radiol 1996;3:597-606.
Ma D, Gulani V, Seiberlich N, et al. Magnetic resonance fingerprinting. Nature 2013; 495:187-193. (See Advanced Discussion for comments about this landmark paper and their followup review below).
Mehta BB, Coppo S, McGivney DF, et al. Magnetic resonance fingerprinting: a technical review. Magn Reson Med 2019; 81:25-46.
Sobol WT, Cameron IG, Inch WR, Pintar MM. Modeling of proton spin relaxation in muscle tissue using nuclear magnetic resonance spine grouping and exchange analysis. Biophys J 1986;50:181-191.
Zimmerman JR, Brittin WE. Nuclear magnetic resonance studies in multiple phase systems: lifetime of a water molecule in an adsorbing phase on silica gel. J Phys Chem 1957; 61:1328-1333.