scholarly journals Comprehensive framework for accurate diffusion MRI parameter estimation

2012 ◽  
Vol 70 (4) ◽  
pp. 972-984 ◽  
Author(s):  
Jelle Veraart ◽  
Jeny Rajan ◽  
Ronald R. Peeters ◽  
Alexander Leemans ◽  
Stefan Sunaert ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Diffusion Mri ◽  
Comprehensive Framework

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.

scholarly journals Quantifying brain microstructure with diffusion MRI: Theory and parameter estimation

2018 ◽  
Vol 32 (4) ◽  
pp. e3998 ◽  
Author(s):  
Dmitry S. Novikov ◽  
Els Fieremans ◽  
Sune N. Jespersen ◽  
Valerij G. Kiselev
Keyword(s):  
Parameter Estimation ◽  
Diffusion Mri ◽  
Brain Microstructure

Parameter estimation using macroscopic diffusion MRI signal models

2015 ◽  
Vol 60 (8) ◽  
pp. 3389-3413 ◽  
Author(s):  
Hang Tuan Nguyen ◽  
Denis Grebenkov ◽  
Dang Van Nguyen ◽  
Cyril Poupon ◽  
Denis Le Bihan ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Diffusion Mri ◽  
Signal Models

scholarly journals Resolving degeneracy in diffusion MRI biophysical model parameter estimation using double diffusion encoding

2019 ◽  
Vol 82 (1) ◽  
pp. 395-410 ◽  
Author(s):  
Santiago Coelho ◽  
Jose M. Pozo ◽  
Sune N. Jespersen ◽  
Derek K. Jones ◽  
Alejandro F. Frangi
Keyword(s):  
Parameter Estimation ◽  
Diffusion Mri ◽  
Double Diffusion ◽  
Biophysical Model ◽  
Model Parameter ◽  
Model Parameter Estimation

scholarly journals Microstructural parameter estimation in vivo using diffusion MRI and structured prior information

2015 ◽  
Vol 75 (4) ◽  
pp. 1787-1796 ◽  
Author(s):  
Jonathan D. Clayden ◽  
Zoltan Nagy ◽  
Nikolaus Weiskopf ◽  
Daniel C. Alexander ◽  
Chris A. Clark
Keyword(s):  
Parameter Estimation ◽  
Diffusion Mri ◽  
Prior Information ◽  
Microstructural Parameter

Weighted Combination of Prior and Sample Information and Parameter Estimation

Optimization ◽  
1976 ◽  
Vol 7 (5) ◽  
pp. 665-672
Author(s):  
H. Burke ◽  
C. Hennig ◽  
W H. Schmidt
Keyword(s):  
Parameter Estimation ◽  
Sample Information ◽  
Weighted Combination

Sequential accuracy in parameter estimation for population correlation coefficients.

2019 ◽  
Vol 24 (4) ◽  
pp. 492-515 ◽  
Author(s):  
Ken Kelley ◽  
Francis Bilson Darku ◽  
Bhargab Chattopadhyay
Keyword(s):  
Parameter Estimation ◽  
Correlation Coefficients ◽  
Population Correlation
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