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.
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.
Advanced Discussion (show/hide)»
If we simply knew the T1 and T2 of all normal and pathological tissues, together with other measurable parameters (like spin density [H], susceptibility, diffusion, etc,), might we be able to diagnose diseases based on unique patterns? This is the idea behind "MR Fingerprinting", an intriguing new development combining novel MR measurement techniques with pattern recognition algorithms and large parameter data bases. See the reference by Ma et al for details.
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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).
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