Diffusion MRI is a magnetic resonance imaging (MRI) method that produces in vivo images of biological tissues weighted with the local microstructural characteristics of water diffusion.
More precisely, the image-intensities at each position are attenuated, depending on the strength (b-value) and direction of the so-called magnetic diffusion gradient, as well as on the local microstructure in which the water molecules diffuse. The more attenuated the image is at a given position, the more diffusion there is in the direction of the diffusion gradient. In order to measure the tissue’s complete diffusion profile, one needs to repeat the MR scans, applying different directions (and possibly strengths) of the diffusion gradient for each scan.
Traditionally, in diffusion-weighted imaging (DWI), three gradient-directions are applied, sufficient to estimate the trace of the diffusion tensor or ‘average diffusivity’, a putative measure of edema. Clinically, trace-weighted images have proven to be very useful to diagnose vascular strokes in the brain, by early detection (within a couple of minutes) of the hypoxic edema.
More extended diffusion tensor imaging (DTI) scans derive neural tract directional information from the data using 3D or multidimensional vector algorithms based on three, six, or more gradient directions, sufficient to compute the diffusion tensor. The diffusion model is a rather simple model of the diffusion process, assuming homogeneity and linearity of the diffusion within each image-voxel. From the diffusion tensor diffusion anisotropy measures, such as the Fractional Anisotropy (FA), can be computed. Moreover, the principal direction of the diffusion tensor can be used to infer the white-matter connectivity of the brain (i.e. tractography; trying to see which part of the brain is connected to which other part).
Recently, more advanced models of the diffusion process have been proposed that aim to overcome the weaknesses of the diffusion tensor model. Amongst others, these include q-space imaging and generalized diffusion tensor imaging.
Diffusion-weighted imaging is an MRI method that produces in vivo magnetic resonance images of biological tissues weighted with the local characteristics of water diffusion.
In diffusion-weighted images, instead of a homogeneous magnetic field, the homogeneity is varied linearly by a pulsed field gradient. Since precession is proportional to the magnet strength, the protons begin to precess at different rates, resulting in dispersion of the phase and signal loss. Another gradient pulse is applied in the same direction but with opposite magnitude to refocus or rephase the spins. The refocusing will not be perfect for protons that have moved during the time interval between the pulses, and the signal measured by the MRI machine is reduced.
Diffusion Tensor Imaging
Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that enables the measurement of the restricted diffusion of water in tissue in order to produce neural tract images instead of using this data solely for the purpose of assigning contrast or colors to pixels in a cross sectional image. The idea of using diffusion data to aid in the production of images of neural tracts was first proposed by Aaron Filler & colleagues in March of 1991, and several months later the first DTI image showing neural tracts curving through the brain was produced.
The principal application is in the imaging of white matter where the location, orientation, and anisotropy of the tracts can be measured. The architecture of the axons in parallel bundles, and their myelin sheaths, facilitate the diffusion of the water molecules preferentially along their main direction. Such preferentially oriented diffusion is called anisotropic diffusion.
Diffusion tensor imaging data can be used to perform tractography within white matter. Fiber tracking algorithms can be used to track a fiber along its whole length (e.g. the corticospinal tract, through which the motor information transit from the motor cortex to the spinal cord and the peripheral nerves).
Some clinical applications of DTI are in the tract-specific localization of white matter lesions such as trauma and in defining the severity of diffuse traumatic brain injury. The localization of tumors in relation to the white matter tracts (infiltration, deflection), has been one the most important initial applications. In surgical planning for some types of brain tumors, surgery is aided by knowing the proximity and relative position of the corticospinal tract and a tumor.
The use of DTI for the assessment of white matter in development, pathology and degeneration has been the focus of over 2,500 research publications since 2005. It promises to be very helpful in distinguishing Alzheimer’s disease from other types of dementia. Applications in brain research cover e.g. connectionistic investigation of neural networks in vivo.
DTI also has applications in the characterization of skeletal and cardiac muscle. The sensitivity to fiber orientation also appears to be helpful in the arena of sports medicine where it greatly aids imaging of structure and injury in muscles and tendons.
Diffusion Imaging Publication References
Ciccarelli, O. Catani, M. Johansen-Berg, H. Clark, C. Thompson A. “Diffusion-based tractography in neurological disorders: concepts, applications, and future developments.” Lancet Neurology. Aug 2008; 7(8):715-27.
Colagrande, S. Belli, G. Politi, Letterio S. Mannelli, L. Pasquinelli, F. Villari, N. “The influence of diffusion- and relaxation-related factors on signal intensity: an introductive guide to magnetic resonance diffusion-weighted imaging studies.” Journal of Computer Assisted Tomography. May-Jun 2008;32(3):463-74.
Mukherjee, P. Chung, SW. Berman, JI. Hess, CP. Henry, RG. “ Diffusion tensor MR imaging and fiber tractography: technical considerations.” AJNR: American Journal of Neuroradiology. May 2008;29(5): 843-52.
Mukherjee, P. Berman JI. Chung, SW. Hess, CP. Henry, RG. “Diffusion Tensor MR imaging and fiber tractography: theoretic underpinnings.” ANJR: American Journal of Neuroradiology. Apr 2008;29(4): 632-41.
Hess, Christopher P. Mukherjee, P. “Visualizing white matter pathways in the living human brain: diffusion tensor imaging and beyond.” Neuroimaging Clinics of North America. Nov 2007;17(4): 407-26, vii.
Roberts, TPL. Schwartz, ES. “Principles and implementation of diffusion-weighted and diffusion tensor imaging.” Pediatric Radiology. Aug 2007;37(8):739-48.
Rollins, NK. “Clinical applications of diffusion tensor imaging and tractography in children.” Pediatric Radiology. Aug 2007;37(8):769-80.
A.A.K. Abdel Razek, A.Y. Kandeel, N. Soliman, H.M. El-shenshawy, Y. Kamec, N. Nada and A. Denewar. Role of Diffusion-Weighted Echo-Planar MR Imaging in Differentiation of Residual or Recurrent Head and Neck Tumors and Posttreatment Changes. American Journal of Neuroradiology. Jun-Jul 2007; 28:1146-1152.
Gupta, RK. Hasan, KM. Mishra, AM. Jha,D. Husain,M. Prasad, KN. Narayana, PA. “High fractional anisotropy in brain abscesses versus other cystic intracranial lesions.” AJNR American Journal Neuroradiology. May 2005; 26(5):1107-14.
Hein PA, Eskey CJ, Dunn JF, Hug EB. Diffusion-weighted imaging in the follow-up of treated high-grade gliomas: tumor recurrence versus radiation injury. AJNR Am J Neuroradiology. 2004;25:201–209.
Howe FA, Filler AG, Bell BA, Griffiths JR. “Magnetic resonance neurography”. Magn Reson Med. Dec 1992; 28 (2): 328–38.