Diffusion Imaging

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.

Diffusion-Weighted 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. This is achieved by applying two gradient pulses in short succession and detecting a drop in signal due to protons that have moved from one voxel to another during that period. Diffusion gradients are applied in 3 orthogonal planes and several strengths in order to estimate the overall mean diffusion of protons in each voxel. Clinically, this average diffusivity has proven to be very useful to diagnose acute ischemic stroke, by very early (within a couple of minutes) abnormal attenuated diffusion in ischemic brain.

Diffusion Tensor Imaging

Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that enables the measurement of the directional diffusion of water in tissue and can be used to produce measures of tissue diffusion anisotropy (how directional the diffusion is) such as Fractional Anisotropy (FA). In addition, the principal direction of the diffusion tensor can be used to infer the direction of the major white-matter tracks of the brain (i.e. tractography).

Some clinical applications of DTI include localization of lesions relative to specific tracts. For example, in surgical planning for some types of brain tumors, surgery is aided by knowing the proximity and position of tumor with respect to the primary motor tracts.

DTI also has non-neurological applications and its sensitivity to fiber orientation has been applied to characterize of musculoskeletal structures as well as myocardium.

The classic tensor model utilizes a false assumption of Gaussian diffusion that isn’t biologic and may fail to capture subtle pathological alterations. In particular, DTI derived from low b-values (e.g. <=1000 sec/mm2), which has been a common clinical standard, is affected by non-neural contributions (e.g., extracellular water, CSF partial volume, etc.).

 

Diffusion Kurtosis Imaging (DKI)

By using greater number and higher level non-zero b-value shells, modeling of non-Gaussian diffusion effects can be performed. DKI extends conventional diffusion tensor imaging (DTI) by estimating the kurtosis of the water diffusion probability distribution function. The kurtosis is a dimensionless statistic that quantifies the non-Gaussianity of a distribution. Water diffusion in biological tissues is clearly non-Gaussian due to the effects of cellular microstructure (e.g., cell membranes and organelles). This is particularly true in the brain, where water diffusion is strongly restricted by myelinated axons. Qualitatively, high diffusional kurtosis suggests increased microstructural complexity.

 

An advantage of DKI is that it is relatively simple to implement for human imaging on conventional MRI clinical scanners. DKI protocols differ from DTI protocols requiring at least 3 b-values (as compared to 2 b-values for DTI) and 15 independent diffusion gradient directions (compared to 6 for DTI). Typical protocols for brain have b-values of 0, 1000, 2000 s/mm2 with 30 diffusion directions. Image post-processing requires the use of specialized algorithms.

Tissue Models

Multicompartmental models of tissue can further provide more accurate assessment of water behavior and additional insights into tissue architecture by assuming specific features of the tissue. It is important to note that making such assumptions creates its own limitations and multicompartment models are only valid in appropriate tissues and circumstances.

 

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.

Jelescu IO and Budde MD (2017) Design and Validation of Diffusion MRI Models of White Matter. Front. Phys. 5:61. doi: 10.3389/fphy.2017.00061

Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K. Diffusional kurtosis imaging: the quantification of non-Gaussian water diffusion by means of MRI. Magn Reson Med 2005;53:1432-1440.