Structure of Optimal Samples in Continuous Nonlinear Experimental Design for Parameter Estimation

2017 ◽  
pp. 81-91
Author(s):  
H. C. La ◽  
H. G. Bock ◽  
J. P. Schlöder
Keyword(s):  
Parameter Estimation ◽  
Experimental Design ◽  
Nonlinear Experimental Design

Optimal experimental design for the parameter estimation of conduction heated foods

2001 ◽  
Vol 48 (2) ◽  
pp. 109-119 ◽  
Author(s):  
H.B. Nahor ◽  
N. Scheerlinck ◽  
R. Verniest ◽  
J. De Baerdemaeker ◽  
B.M. Nicolaı̈
Keyword(s):  
Parameter Estimation ◽  
Experimental Design ◽  
Optimal Experimental Design

scholarly journals Task-driven assessment of experimental designs in diffusion MRI: A computational framework

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258442
Author(s):  
Sean C. Epstein ◽  
Timothy J. P. Bray ◽  
Margaret A. Hall-Craggs ◽  
Hui Zhang
Keyword(s):  
Parameter Estimation ◽  
Experimental Design ◽  
Task Performance ◽  
Real World ◽  
Diffusion Mri ◽  
Estimation Method ◽  
Roc Curves ◽  
Experimental Designs ◽  
Clinical Classification ◽  
Computational Framework

This paper proposes a task-driven computational framework for assessing diffusion MRI experimental designs which, rather than relying on parameter-estimation metrics, directly measures quantitative task performance. Traditional computational experimental design (CED) methods may be ill-suited to experimental 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 microstructural parameters rather than their task performance. The method we propose addresses this shortcoming. For a given MRI 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 associated summary metrics (e.g. area under the curve (AUC)). Two experiments were performed: first, a validation of the pipeline’s task performance predictions against clinical results, 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. Comparison with clinical datasets validates our method’s predictions of (a) the qualitative form of ROC curves, (b) the relative task performance of different experimental designs, and (c) the absolute performance (AUC) of each experimental design. Furthermore, we show that our method outperforms traditional task-agnostic assessment methods, enabling improved, more useful experimental design. Our pipeline produces accurate, quantitative predictions of real-world task performance. Compared to current approaches, such task-driven assessment is more likely to identify experimental designs that perform well in practice. Our method is not limited to diffusion MRI; the pipeline generalises to any task-based quantitative MRI application, and provides the foundation for developing future task-driven end-to end CED frameworks.

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