Bias in MRI Measurements of Apparent Diffusion Coefficient and Kurtosis: Implications for Choice of Maximum Diffusion Encoding

Tissue water diffusion is non-Gaussian and the expressions used to calculate diffusion parameters are approximations which introduce systematic errors dependent on the maximum diffusion encoding, diffusion time, etc. This study aimed at characterizing biases in estimates of both apparent diffusion coefficient and kurtosis, and determines their dependence on these parameters. Similar to the approach of several previous studies, Taylor expansion of the diffusion signal was used to calculate biases. Predicted errors were compared with data from one volunteer. Predicted errors agreed well with the measured errors and also the published diffusion tensor imaging measurements. The equations derived predict biases in measured diffusion parameters and explain much of the discrepancy between measurements obtained with different acquisition protocols. The equations may also be used to choose appropriate diffusion encoding for diffusion weighted, tensor, and kurtosis imaging.

[1]  Kevin C. Chan,et al.  B-value dependence of DTI quantitation and sensitivity in detecting neural tissue changes , 2010, NeuroImage.

[2]  John N. Tsitsiklis,et al.  Introduction to Probability , 2002 .

[3]  A. Rosenkrantz,et al.  Minimization of errors in biexponential T2 measurements of the prostate , 2015, Journal of magnetic resonance imaging : JMRI.

[4]  M. Kanematsu,et al.  Diffusion‐weighted imaging of the liver: Optimizing b value for the detection and characterization of benign and malignant hepatic lesions , 2008, Journal of magnetic resonance imaging : JMRI.

[5]  C. Boesch,et al.  Diffusion‐weighted imaging of the parotid gland: Influence of the choice of b‐values on the apparent diffusion coefficient value , 2004, Journal of magnetic resonance imaging : JMRI.

[6]  Joseph A. Helpern,et al.  White matter characterization with diffusional kurtosis imaging , 2011, NeuroImage.

[7]  Derek K. Jones,et al.  “Squashing peanuts and smashing pumpkins”: How noise distorts diffusion‐weighted MR data , 2004, Magnetic resonance in medicine.

[8]  J. Helpern,et al.  Diffusional kurtosis imaging: The quantification of non‐gaussian water diffusion by means of magnetic resonance imaging , 2005, Magnetic resonance in medicine.

[9]  J V Hajnal,et al.  High b-Value Diffusion Tensor Imaging of the Neonatal Brain at 3T , 2008, American Journal of Neuroradiology.

[10]  Charles M. Grinstead,et al.  Introduction to probability , 1999, Statistics for the Behavioural Sciences.

[11]  N. Toschi,et al.  Differences in Gaussian diffusion tensor imaging and non-Gaussian diffusion kurtosis imaging model-based estimates of diffusion tensor invariants in the human brain. , 2016, Medical physics.

[12]  J. Helpern,et al.  MRI quantification of non‐Gaussian water diffusion by kurtosis analysis , 2010, NMR in biomedicine.

[13]  V. Kiselev The Cumulant Expansion: An Overarching Mathematical Framework For Understanding Diffusion NMR , 2010 .

[14]  O. Gonen,et al.  The optimal MR acquisition strategy for exponential decay constants estimation. , 2008, Magnetic resonance imaging.

[15]  G. Johnson,et al.  A model describing diffusion in prostate cancer , 2017, Magnetic resonance in medicine.

[16]  N M deSouza,et al.  Diffusion-weighted magnetic resonance imaging: a potential non-invasive marker of tumour aggressiveness in localized prostate cancer. , 2008, Clinical radiology.

[17]  Stephan E Maier,et al.  Biexponential characterization of prostate tissue water diffusion decay curves over an extended b-factor range. , 2006, Magnetic resonance imaging.

[18]  Derek K. Jones,et al.  Diffusion‐tensor MRI: theory, experimental design and data analysis – a technical review , 2002 .

[19]  Richard A. Jones,et al.  The evolution of the apparent diffusion coefficient in the pediatric brain at low and high diffusion weightings , 2003, Journal of magnetic resonance imaging : JMRI.

[20]  M. F. Falangola,et al.  Preliminary observations of increased diffusional kurtosis in human brain following recent cerebral infarction , 2011, NMR in biomedicine.

[21]  Paul Malcolm,et al.  An improved model for prostate diffusion incorporating the results of Monte Carlo simulations of diffusion in the cellular compartment , 2017, NMR in biomedicine.

[22]  M. Moseley,et al.  Magnetic Resonance in Medicine 51:924–937 (2004) Characterizing Non-Gaussian Diffusion by Using Generalized Diffusion Tensors , 2022 .

[23]  Brian Hansen,et al.  Precision and accuracy of diffusion kurtosis estimation and the influence of b‐value selection , 2017, NMR in biomedicine.

[24]  Daniel C. Alexander,et al.  NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain , 2012, NeuroImage.

[25]  G. Johnson,et al.  Parameter Estimation Error Dependency on the Acquisition Protocol in Diffusion Kurtosis Imaging , 2016, Applied magnetic resonance.

[26]  G. Johnson,et al.  A monte carlo study of restricted diffusion: Implications for diffusion MRI of prostate cancer , 2017, Magnetic resonance in medicine.

[27]  D. Le Bihan,et al.  Quantitative Non-Gaussian Diffusion and Intravoxel Incoherent Motion Magnetic Resonance Imaging: Differentiation of Malignant and Benign Breast Lesions , 2015, Investigative radiology.

[28]  Emilie T. McKinnon,et al.  Comparison of cumulant expansion and q-space imaging estimates for diffusional kurtosis in brain. , 2018, Magnetic resonance imaging.

[29]  J. E. Tanner,et al.  Spin diffusion measurements : spin echoes in the presence of a time-dependent field gradient , 1965 .

[30]  R I Grossman,et al.  Non-Gaussian diffusion MRI of gray matter is associated with cognitive impairment in multiple sclerosis , 2015, Multiple sclerosis.

[31]  R. Gillies,et al.  Changes in water mobility measured by diffusion MRI predict response of metastatic breast cancer to chemotherapy. , 2004, Neoplasia.

[32]  G. L. Bretthorst,et al.  Statistical model for diffusion attenuated MR signal , 2003, Magnetic resonance in medicine.

[33]  Koichi Oshio,et al.  Biexponential apparent diffusion coefficients in prostate cancer. , 2009, Magnetic resonance imaging.

[34]  D. Sodickson,et al.  Intravoxel incoherent motion imaging of tumor microenvironment in locally advanced breast cancer , 2011, Magnetic resonance in medicine.