How to interpret dMRI metrics

Diffusion MRI (dMRI) acquires diffusion-sensitive MRI data to reveal the microscopic change of biological tissues structure. The dMRI data are processed to provide diffusion metrics, such as diffusivities and anisotropy to summarize the diffusion pattern. Studies have shown that diffusion metrics could reveal neuronal change, cellularity, tissue edema.

The diffusion metrics can be categorized into model-based metrics and model-free metrics. The model-based metrics are calculated by fitting dMRI signals with a model, such as a tensor model, NODDI model, and the model’s parameters will provide the metrics. The model-free metrics are directly calculated from the PDF of diffusion distribution (i.e., histogram of diffusion distribution) without assuming a model.

There are pros and cons concerning each approach. Model-based metrics need fewer diffusion sampling, and the metrics are usually associated with physical or biological references. The downside is the violation of models, which is common in biological studies. Model-free metrics need more diffusion samplings, and the association between metrics and biological findings requires tissue validation. The advantage is that it does not have a model assumption.

The following is a summary of how to interpret results from diffusion metrics.

  Metrics Model/Method Interpretation Changes Explanation
FA fractional anisotropy Diffusion tensor FA is nonspecifically associated with axonal integrity decreases: demyelination (Chang, 2017), inflammation, edema, axonal loss FA is a non-unit fraction derived from the ratio between λ1, λ2, and λ3. It has a value ranging from 0 (isotropic) to 1(totally anisotropic). Myelinated fibers have high FA and low RD (Chang 2017).
AD axial diffusivity* Diffusion tensor AD is nonspecifically associated with axonal density decreases: axonal loss (Budde et al., 2007; Song et al., 2003) AD, denoted by λ parallel, quantifies how fast water diffuses along the axonal fibers. It is estimated by λ1, the first eigenvalue of the tensor.
RD radial diffusivity* Diffusion tensor RD is nonspecifically associated with myelination increases: demyelination (Budde et al., 2007; Song et al., 2002; Song et al., 2005) RD, denoted by λ perpendicular, quantified how fast water diffuses across the axonal bundles. It is estimated by (λ2+λ3)/2, the average of the second and third eigenvalues of the tensor.
MD mean diffusivity* Diffusion tensor MD is associated with edema and cell infiltration increases: vesogenic edema, decreases: cytotoxic edema MD is the diffusivity average from the three eigenvalues of the tensor. It is often regarded as an approximation of the overall diffusivities.
QA quantitative anisotropy Q-space imaging QA is associated with axonal density decreases: axonal loss (Yeh 2019)(Shen 2015) QA is the density of anisotropic diffusion after rmoving the isotropic component (Yeh, 2010). It is less affected to edema, while FA and AD are also sensitive to edema (Yeh, 2013).
ISO isotropy Q-space imaging ISO is associated with edema increases: edema ISO is the isotropic component of the spin distribution function (Yeh. 2010). It represents background isotropic diffusion contributed from CSF or edema, including both restricted and non-restricted isotropic diffusion.
RDI restricted diffusion imaging Q-space imaging RDI is associated with cell infiltration during inflammation (Yeh 2017)(Yeh 2021) increases: cell infiltrations (inflammation or tumor infiltration) RDI quantified the total amount of restricted diffusion regardless of the orientation (Yeh 2017)
NRDI none-restricted diffusion imaging Q-space imaging NRDI is associated with edema (Yeh 2017) increases: edema due to inflammation NRDI quantified the total amount of none-restricted diffusion regardless of the orientation (Yeh 2017)

*The diffusivity (either RD, MD, or AD) calculated in DSI Studio has a unit of 10^-3 mm^2/s.

Change of metrics in neurological disorders

↓: decrease ↑: increase -: no change

Condition Example FA AD RD MD QA ISO RDI NRDI
Acute axonal injury with inflammation stroke ( < 3 month), TBI (< 3 month), MS relapse, tumor mass effect ↑ or - - ↑ (at locations with cell infiltrations) ↑ (at edema location)
Axonal loss without inflammation ALS, Huntingtons Diseases, TBI (> 6 month), stroke ( > 6 month) - - - -

References

  1. Budde MD, Xie M, Cross AH, Song SK. Axial diffusivity is the primary correlate of axonal injury in the experimental autoimmune encephalomyelitis spinal cord: a quantitative pixelwise analysis. J Neurosci. 2009;29(9):2805-13.
  2. Budde MD, Kim JH, Liang HF, Schmidt RE, Russell JH, Cross AH, et al. Toward accurate diagnosis of white matter pathology using diffusion tensor imaging. Magn Reson Med. 2007;57(4):688-95.
  3. Sun SW, Liang HF, Trinkaus K, Cross AH, Armstrong RC, Song SK. Noninvasive detection of cuprizone induced axonal damage and demyelination in the mouse corpus callosum. Magn Reson Med. 2006;55(2):302-8.
  4. Song SK, Yoshino J, Le TQ, Lin SJ, Sun SW, Cross AH, et al. Demyelination increases radial diffusivity in corpus callosum of mouse brain. Neuroimage. 2005;26(1):132-40.
  5. Song SK, Sun SW, Ju WK, Lin SJ, Cross AH, Neufeld AH. Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia. Neuroimage. 2003;20(3):1714-22.
  6. Song SK, Sun SW, Ramsbottom MJ, Chang C, Russell J, Cross AH. Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. Neuroimage. 2002;17(3):1429-36.
  7. Kono K, Inoue Y, Nakayama K, Shakudo M, Morino M, Ohata K, et al. The role of diffusion-weighted imaging in patients with brain tumors. AJNR Am J Neuroradiol. 2001;22(6):1081-8.
  8. Gauvain KM, McKinstry RC, Mukherjee P, Perry A, Neil JJ, Kaufman BA, et al. Evaluating pediatric brain tumor cellularity with diffusion-tensor imaging. AJR Am J Roentgenol. 2001;177(2):449-54.
  9. Sugahara T, Korogi Y, Kochi M, Ikushima I, Shigematu Y, Hirai T, et al. Usefulness of diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas. J Magn Reson Imaging. 1999;9(1):53-60.
  10. Chang EH, Argyelan M, Aggarwal M, Chandon TS, Karlsgodt KH, Mori S, et al. The role of myelination in measures of white matter integrity: Combination of diffusion tensor imaging and two-photon microscopy of CLARITY intact brains. Neuroimage. 2017;147:253-61.
  11. Yeh FC, Irimia A, Bastos DCA, Golby AJ. Tractography methods and findings in brain tumors and traumatic brain injury. Neuroimage. 2021;245:118651.
  12. Yeh FC, Zaydan IM, Suski VR, Lacomis D, Richardson RM, Maroon JC, et al. Differential tractography as a track-based biomarker for neuronal injury. Neuroimage. 2019;202:116131.
  13. Garic D, Yeh FC, Graziano P, Dick AS. In vivo restricted diffusion imaging (RDI) is sensitive to differences in axonal density in typical children and adults. Brain Struct Funct. 2021;226(8):2689-705.
  14. Yeh FC, Liu L, Hitchens TK, Wu YL. Mapping immune cell infiltration using restricted diffusion MRI. Magn Reson Med. 2017;77(2):603-12.
  15. Shen CY, Tyan YS, Kuo LW, Wu CW, Weng JC. Quantitative Evaluation of Rabbit Brain Injury after Cerebral Hemisphere Radiation Exposure Using Generalized q-Sampling Imaging. PLoS One. 2015;10(7):e0133001.

Difference between model-based and model-free

Here we focus on the differences between DTI and GQI, although other model-based and model-free methods exist.

The DTI model does not consider restricted diffusion and thus FA, AD, MD are sensitive to a wide range of biological changes including edema, inflammation, or just a superimposing crossing fiber (see Yeh F-C et al. PLoS ONE 8(11): e80713.2013). More advanced methods aim to provide more specific metrics. GQI separates isotropic diffusion and anisotropic diffusion, thus minimizing the partial volume of free water. The anisotropic diffusion is further quantified per each principal diffusion direction, thus minimizing the partial volume of crossing fibers. A neurosurgery study has shown that QA is robust against peritumoral edema and contributes to more reliable tractography (Zhang, et al. Neurosurgery, 73(6), 1044-1053. 2013). A phantom study has shown that QA is more robust to the free water effect and partial volume of crossing fibers. (Yeh, PloS one 8.11, 2013).