Quantification of MR spectra by deep learning in an idealized setting: Investigation of forms of input, network architectures, optimization by ensembles of networks, and training bias

0567321 - ÚPT 2024 RIV US eng J - Článek v odborném periodiku
Rizzo, R. - Dziadosz, M. - Kyathanahally, S. P. - Shamaei, Amirmohammad - Kreis, R.
Quantification of MR spectra by deep learning in an idealized setting: Investigation of forms of input, network architectures, optimization by ensembles of networks, and training bias.
Magnetic Resonance in Medicine. Roč. 89, č. 5 (2023), s. 1707-1727. ISSN 0740-3194. E-ISSN 1522-2594
GRANT EU: European Commission(XE) 813120 - INSPiRE-MED
Institucionální podpora: RVO:68081731
Klíčová slova: active learning * bias * deep learning * ensemble of networks * model fitting * magnetic resonance spectroscopy * quantification
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impakt faktor: 3.3, rok: 2022
Způsob publikování: Open access
https://onlinelibrary.wiley.com/doi/10.1002/mrm.29561

Purpose: The aims of this work are (1) to explore deep learning (DL) architectures, spectroscopic input types, and learning designs toward optimal quantification in MR spectroscopy of simulated pathological spectra, and (2) to demonstrate accuracy and precision of DL predictions in view of inherent bias toward the training distribution. Methods: Simulated 1D spectra and 2D spectrograms that mimic an extensive range of pathological in vivo conditions are used to train and test 24 different DL architectures. Active learning through altered training and testing data distributions is probed to optimize quantification performance. Ensembles of networks are explored to improve DL robustness and reduce the variance of estimates. A set of scores compares performances of DL predictions and traditional model fitting (MF). Results: Ensembles of heterogeneous networks that combine 1D frequency-domain and 2D time-frequency domain spectrograms as input perform best. Dataset augmentation with active learning can improve performance, but gains are limited. MF is more accurate, although DL appears to be more precise at low SNR. However, this overall improved precision originates from a strong bias for cases with high uncertainty toward the dataset the network has been trained with, tending toward its average value. Conclusion: MF mostly performs better compared to the faster DL approach. Potential intrinsic biases on training sets are dangerous in a clinical context that requires the algorithm to be unbiased to outliers (i.e., pathological data). Active learning and ensemble of networks are good strategies to improve prediction performances. However, data quality (sufficient SNR) has proven as a bottleneck for adequate unbiased performance-like in the case of MF.
Trvalý link: https://hdl.handle.net/11104/0338584