The Diffusion tensor imaging or diffusion-weighted magnetic resonance imaging (DW-MRI) represents the diffusion behavior of water molecules in biological tissue as an imaging method based on classic MRT. It is mainly used in brain examinations. Similar to classic MRI, the procedure is non-invasive and does not require the use of ionizing radiation.
What is Diffusion Tensor Imaging?
Diffusion-weighted magnetic resonance imaging is a method of magnetic resonance imaging (MRT) that measures the diffusion movements of water molecules in body tissue.
In clinical practice, it is mainly used to examine the brain, because the diffusion behavior of the water allows conclusions to be drawn about some diseases of the central nervous system. With the help of diffusion-weighted magnetic resonance tomography or diffusion tensor imaging, information about the course of the large nerve fiber bundles can also be obtained. In the frequently used diffusion tensor imaging (DTI), a variant of DW-MRI, the directional dependence of the diffusion is also recorded.
The DTI calculates a tensor per unit volume, which is used to describe the three-dimensional diffusion behavior. However, due to the huge amount of data required, these measurements are significantly more time-consuming than classic MRI. The data can only be interpreted using various visualization techniques. Today, diffusion tensor imaging that emerged in the 1980s is supported by all new MRI devices.
Function, effect & goals
Like conventional magnetic resonance imaging, diffusion-weighted magnetic resonance imaging is based on the fact that protons have a spin with a magnetic moment. The spin can align itself either parallel or anti-parallel to an external magnetic field.
The anti-parallel alignment has a higher energetic state than the parallel alignment. When an external magnetic field is applied, an equilibrium is established in favor of the low-energy protons. If a high-frequency field is switched on across this field, the magnetic moments flip over in the direction of the xy plane depending on the strength and duration of the pulse. This condition is known as nuclear magnetic resonance. When the high-frequency field is switched off again, the nuclear spins align themselves again in the direction of the static magnetic field with a time delay that depends on the chemical environment of the proton.
The signal is registered via the voltage generated in the measuring coil. In diffusion-weighted magnetic resonance tomography, a gradient field is applied during the measurement, which changes the field strength of the static magnetic field in a predetermined direction. This causes the hydrogen nuclei to go out of phase and the signal disappears. If the direction of rotation of the cores is reversed by a new high-frequency pulse, they come back into phase and the signal occurs again.
However, the intensity of the second signal is weaker because some nuclei are no longer in phase. This loss of intensity of the signal describes the diffusion of the water. The weaker the second signal, the more nuclei have diffused in the direction of the gradient field and the lower the diffusion resistance. The resistance to diffusion is in turn dependent on the internal structure of the nerve cells. With the help of the measured data, the structure of the examined tissue can be calculated and illustrated.
Diffusion-weighted magnetic resonance imaging is often used in stroke diagnostics. The failure of the sodium-potassium pumps in the event of a stroke severely restricts the diffusion movements. With DW-MRI this is immediately visible, while with conventional MRI the changes can often only be registered after several hours. Another area of application relates to the planning of operations in brain surgery.
Diffusion tensor imaging determines the course of the nerve pathways. This must be taken into account when planning the operation. The recordings can also show whether a tumor has already penetrated the nerve tract. This method can also be used to assess the question of whether an operation has any prospects at all. Many neurological and psychiatric diseases, such as Alzheimer's disease, epilepsy, multiple sclerosis, schizophrenia or HIV encephalopathy, are now the subject of research in diffusion tensor imaging. The question is which brain regions are affected by which diseases. Diffusion tensor imaging is also increasingly used as a research tool for cognitive science studies.
Risks, side effects & dangers
Despite its good results in the diagnosis of strokes, in the preparation of brain operations and as a research tool in many clinical studies, diffusion-weighted magnetic resonance tomography still has its application limits.
In some cases, the process is not yet fully developed and requires intensive research and development work to improve it. The measurements of the diffusion-weighted magnetic resonance tomography often only offer a limited image quality because the diffusion movement is only expressed by an attenuation of the measured signal. Little progress has been made even with a higher spatial resolution, since with smaller volume elements the signal attenuation disappears in the noise of the measuring apparatus. In addition, a large number of individual measurements is necessary.
The measurement data must be reworked in the computer in order to be able to correct some disturbances. So far, there are still problems to represent a complex diffusion behavior satisfactorily. According to the current state of the art, the diffusion within a voxel can only be correctly recorded in one direction. Methods are being tested that can simultaneously make diffusion-weighted recordings in different directions. These are processes that require high angular resolution.
The methods for evaluating and processing the data also still need to be optimized. In previous studies, for example, the data obtained from diffusion-weighted magnetic resonance imaging for larger groups of test subjects were compared with one another. Due to the different anatomical structures of different individuals, however, this can lead to misleading study results. That is why new methods for statistical analysis must be developed.
