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	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 Rizzo, R., Dziadosz, M., Kyathanahally, S. P., Shamaei, A., & Kreis, R. (2023). 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, 89(5), 1707-1727. https://doi.org/10.1002/mrm.29561 Detailed Record Published Version
	Reliability of quantification estimates in MR spectroscopy: CNNs vs traditional model fitting Rizzo, R., Dziadosz, M., Kyathanahally, S. P., Reyes, M., & Kreis, R. (2022). Reliability of quantification estimates in MR spectroscopy: CNNs vs traditional model fitting. In L. Wang, Q. Dou, P. T. Fletcher, S. Speidel, & S. Li (Eds.), Lecture notes in computer science: Vol. 13438. Medical image computing and computer assisted intervention - MICCAI 2022. 25th international conference, Singapore, September 18-22, 2022, proceedings, part VIII (pp. 715-724). https://doi.org/10.1007/978-3-031-16452-1_68 Detailed Record Published Version