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Neural networks for parameter estimation in microstructural MRI: Application to a diffusion-relaxation model of white
João P de Almeida Martins1, Markus Nilsson2, Björn Lampinen3
1Department of Clinical Sciences, Radiology, Lund University, Lund, Sweden; Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway.
Neuroimage
|September 25, 2021
Summary
Machine learning accelerates white matter microstructure analysis using artificial neural networks for relaxation-diffusion MRI data. While effective, networks still face challenges with data degeneracy, highlighting the need for optimized acquisition protocols.
Area of Science:
- Neuroimaging
- Biophysics
- Computational Neuroscience
Background:
- White matter microstructure analysis relies on biophysical models interpreting relaxation-diffusion MRI data.
- Complex models offer detailed insights but face challenges in parameter estimation due to local minima and degenerate fitting landscapes.
- Machine learning (ML) approaches promise accelerated and robust parameter estimation for these models.
Purpose of the Study:
- To investigate the application of artificial neural networks (ANNs) for accelerated parameter estimation in a complex relaxation-diffusion model of white matter.
- To develop and apply strategies for assessing the accuracy and sensitivity of ML-based fitting networks.
- To explore the impact of acquisition protocols on the performance of ML fitting algorithms.
Main Methods:
- Utilized ANNs to accelerate parameter estimation for a novel relaxation-diffusion model of white matter microstructure.
- Developed and employed strategies to evaluate the accuracy and sensitivity of function fitting networks.
- Tested ML-based fitting pipelines on data acquired using both optimized and sub-optimal relaxation-diffusion-correlation protocols.
Main Results:
- ANNs significantly accelerated parameter estimation, providing accurate results with optimized acquisition protocols.
- ML fitting approaches demonstrated reduced susceptibility to sub-optimal protocols compared to traditional least-squares solvers.
- Degeneracy issues persisted, indicating that ML methods cannot entirely substitute for careful acquisition protocol design.
Conclusions:
- ANNs offer a powerful tool for accelerating white matter microstructure analysis using relaxation-diffusion MRI.
- Optimized acquisition protocols remain crucial for mitigating degeneracy issues and ensuring reliable parameter estimation.
- Future research should focus on further integrating ML with optimized acquisition strategies for robust neuroimaging analysis.
