A unified optimization approach for diffusion tensor imaging technique.
An optimization approach for diffusion tensor imaging (DTI) technique is proposed, aiming to improve the estimates of tensors, fractional anisotropy (FA), and fiber directions. With the simulated annealing algorithm, the proposed approach simultaneously optimizes imaging parameters (gradient duration/separation, read-out time, and TE), b-values, and diffusion gradient directions either with or without incorporating prior knowledge of tensor fields. In addition, the method through which tensors are estimated, least squares in our study, was also considered in the optimization procedures. Monte-Carlo simulations were performed for three different scenarios of prior fiber distributions including fibers orientated in 1 (CONE1) and 3 (CONE3) cone areas (50 tensors orderly oriented within a diverging angle of 20 degrees in each cone) and a uniform fiber distribution (UNIF). In addition, three imaging acquisition schemes together with different signal-to-noise ratios were tested, including M/N=1/6, 2/12, and 5/30 for each prior fiber distribution where M and N were the number of b=0 and b>0 images, respectively. Our results show that the optimal b-value ranges between 0.7 and 1.0 x 10(9) s/m(2) for UNIF. However, the optimal b-value ranges become both higher and wider for CONE1 and CONE3 than that of UNIF. In addition, the biases and standard deviations (SD) of tensors, and SD of FA are substantially reduced and the accuracy of fiber directional estimates is improved using the proposed approach particularly in CONE1 when compared with the conventional approaches. Together, the proposed unified optimization approach may offer a direct and simultaneous means to optimize DTI experiments.
Gao
Wei
W
Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Zhu
Hongtu
H
Lin
Weili
W
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D009413:Nerve Fibers, Myelinated / Q000648:ultrastructure*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity