TY - JOUR
T1 - Diffusion tensor imaging at low SNR
T2 - nonmonotonic behaviors of tensor contrasts
AU - Landman, Bennett A.
AU - Farrell, Jonathan A D
AU - Huang, Hao
AU - Prince, Jerry L.
AU - Mori, Susumu
N1 - Funding Information:
This work was supported by RO1AG20012, U24 RR021382, P41 RR15241 and the Department of Defense Office of Naval Research National Defense Science and Engineering Graduate Fellowship.
PY - 2008/7
Y1 - 2008/7
N2 - Diffusion tensor imaging (DTI) provides measurements of directional diffusivities and has been widely used to characterize changes in the tissue microarchitecture of the brain. DTI is gaining prominence in applications outside of the brain, where resolution, motion and short T2 values often limit the achievable signal-to-noise ratio (SNR). Consequently, it is important to revisit the topic of tensor estimation in low-SNR regimes. A theoretical framework is developed to model noise in DTI, and by using simulations based on this theory, the degree to which the noise, tensor estimation method and acquisition protocol affect tensor-derived quantities, such as fractional anisotropy and apparent diffusion coefficient, is clarified. These results are then validated against clinical data. It is shown that reliability of tensor contrasts depends on the noise level, estimation method, diffusion-weighting scheme and underlying anatomy. The propensity for bias and errors does not monotonically increase with noise. Comparative results are shown in both graphical and tabular forms, so that decisions about suitable acquisition protocols and processing methods can be made on a case-by-case basis without exhaustive experimentation.
AB - Diffusion tensor imaging (DTI) provides measurements of directional diffusivities and has been widely used to characterize changes in the tissue microarchitecture of the brain. DTI is gaining prominence in applications outside of the brain, where resolution, motion and short T2 values often limit the achievable signal-to-noise ratio (SNR). Consequently, it is important to revisit the topic of tensor estimation in low-SNR regimes. A theoretical framework is developed to model noise in DTI, and by using simulations based on this theory, the degree to which the noise, tensor estimation method and acquisition protocol affect tensor-derived quantities, such as fractional anisotropy and apparent diffusion coefficient, is clarified. These results are then validated against clinical data. It is shown that reliability of tensor contrasts depends on the noise level, estimation method, diffusion-weighting scheme and underlying anatomy. The propensity for bias and errors does not monotonically increase with noise. Comparative results are shown in both graphical and tabular forms, so that decisions about suitable acquisition protocols and processing methods can be made on a case-by-case basis without exhaustive experimentation.
KW - DTI
KW - Low SNR
KW - Monte Carlo simulation
KW - Reliability
KW - Tensor estimation
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U2 - 10.1016/j.mri.2008.01.034
DO - 10.1016/j.mri.2008.01.034
M3 - Article
C2 - 18499378
AN - SCOPUS:45849114741
SN - 0730-725X
VL - 26
SP - 790
EP - 800
JO - Magnetic Resonance Imaging
JF - Magnetic Resonance Imaging
IS - 6
ER -