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This paper proposes a task-driven computational framework for assessingdiffusion MRI experimental designs which, rather than relying onparameter-estimation metrics, directly measures quantitative task performance.Traditional computational experimental design (CED) methods may be ill-suitedto experimental tasks, such as clinical classification, where outcome does notdepend on parameter-estimation accuracy or precision alone. Current assessmentmetrics evaluate experiments' ability to faithfully recover microstructuralparameters rather than their task performance. The method we propose addressesthis shortcoming. For a given MRI experimental design, experiments aresimulated start-to-finish and task performance is computed from ROC curves andassociated summary metrics (e.g. AUC). Two experiments were performed: first, avalidation of the pipeline's task performance predictions against clinicalresults, comparing in-silico predictions to real-world ROC/AUC; and second, ademonstration of the pipeline's advantages over traditional CED approaches,using two simulated clinical classification tasks. Comparison with clinicaldatasets validates our method's predictions of (a) the qualitative form of ROCcurves, (b) the relative task performance of different experimental designs,and (c) the absolute performance of each experimental design. Furthermore, weshow that our method outperforms traditional task-agnostic assessment methods,enabling improved, more useful experimental design. Compared to currentapproaches, such task-driven assessment is more likely to identify experimentaldesigns that perform well in practice. Our method is not limited to diffusionMRI; the pipeline generalises to any task-based quantitative MRI application,and provides the foundation for developing future task-driven end-to end CEDframeworks.

Task-driven assessment of experimental designs in diffusion MRI: a computational framework