Abstract
PURPOSE: Devising methodology to characterize and optimize acquisition schemes for biological tissues, such as the prostate, that produce model-based synthetic diffusion-weighted images and derived model parameters with predictable SNR improvement. METHODS: The averaging effect (AE) in synthetic diffusion-weighted images obtained through fitting of various signal decay models to signals measured over 21 linearly spaced b-values between 0 and 2000s/mm2 is determined with analytic expressions. Similarly, the standard deviation of a retrospective 2-point ADC fit based on the synthetic data is analyzed. Furthermore, acquisition schemes that achieve constant SNR or constant AE for synthetic images over the same b-value range are devised by means of numerical optimization using either a custom iterative method or a standard function optimizer. These acquisition schemes are verified by measurements on a phantom with a non-monoexponential diffusion signal decay combined with a bootstrapping approach to increase the number of data samples. RESULTS: The dependence of AE on model function and parameters is complex. Repeated measurements at specific b-values can boost AE locally, while improvements in ADC uncertainty are particularly pronounced for repetitions of the higher b-value. Optimization of acquisition schemes generally results in discrete b-values, whereby the number of b-values corresponds to the number of model parameters. Results from phantom measurements are in agreement with the theoretical predictions. CONCLUSION: The presented analytical calculations and numerical optimizations can be useful to improve acquisition schemes under various experimental conditions and clinical needs.