Truly reproducible uniform estimation of the ADC with multi‐b diffusion data— Application in prostate diffusion imaging

Purpose The ADC is a well‐established parameter for clinical diagnostic applications, but lacks reproducibility because it is also influenced by the choice diffusion weighting level. A framework is evaluated that is based on multi‐b measurement over a wider range of diffusion‐weighting levels and higher order tissue diffusion modeling with retrospective, fully reproducible ADC calculation. Methods Averaging effect from curve fitting for various model functions at 20 linearly spaced b‐values was determined by means of simulations and theoretical calculations. Simulation and patient multi‐b image data were used to compare the new approach for diffusion‐weighted image and ADC map reconstruction with and without Rician bias correction to an active clinical trial protocol probing three non‐zero b‐values. Results Averaging effect at a certain b‐value varies for model function and maximum b‐value used. Images and ADC maps from the novel procedure are on‐par with the clinical protocol. Higher order modeling and Rician bias correction is feasible, but comes at the cost of longer computation times. Conclusions Application of the new framework makes higher order modeling more feasible in a clinical setting while still providing patient images and reproducible ADC maps of adequate quality.