  Purpose: To propose a task-driven computational framework for assessing
diffusion MRI experimental designs which, rather than relying on
parameter-estimation metrics, directly measures quantitative task performance.
  Theory: Traditional computational experimental design (CED) methods may be
ill-suited to tasks, such as clinical classification, where outcome does not
depend on parameter-estimation accuracy or precision alone. Current assessment
metrics evaluate experiments' ability to faithfully recover microstructure
parameters rather than their associated task performance. This work proposes a
novel CED assessment method that addresses this shortcoming. For a given
experimental design (protocol, parameter-estimation method, model, etc.),
experiments are simulated start-to-finish and task performance is computed from
receiver operating characteristic (ROC) curves and summary metrics such as area
under the curve (AUC).
  Methods: Two experiments were performed: first a validation of the pipeline's
task performance predictions in two clinical datasets, comparing in-silico
predictions to real-world ROC/AUC; and second, a demonstration of the
pipeline's advantages over traditional CED approaches, using two simulated
clinical classification tasks.
  Results: Our computational method accurately predicts (a) the qualitative
form of ROC curves, (b) the relative performance of different experimental
designs, and (c) the absolute performance (AUC) of each experimental design.
Furthermore, our method is shown to outperform traditional task-agnostic
assessment methods.
  Conclusions: The proposed pipeline produces accurate, quantitative
predictions of real-world task performance. Compared to current approaches,
such task-driven assessment is more likely to identify experimental design that
perform well in practice. It provides the foundation for developing future
task-driven CED frameworks.
