DeepCEST 3T: Robust MRI parameter determination and uncertainty quantification with neural networks—application to CEST imaging of the human brain at 3T

Purpose Calculation of sophisticated MR contrasts often requires complex mathematical modeling. Data evaluation is computationally expensive, vulnerable to artifacts, and often sensitive to fit algorithm parameters. In this work, we investigate whether neural networks can provide not only fast model...

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Veröffentlicht in:	Magnetic resonance in medicine Jg. 84; H. 1; S. 450 - 466
Hauptverfasser:	Glang, Felix, Deshmane, Anagha, Prokudin, Sergey, Martin, Florian, Herz, Kai, Lindig, Tobias, Bender, Benjamin, Scheffler, Klaus, Zaiss, Moritz
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Wiley Subscription Services, Inc 01.07.2020
Schlagworte:	Algorithms APT Brain Brain - diagnostic imaging Brain cancer Brain Neoplasms - diagnostic imaging Brain tumors chemical exchange saturation transfer (CEST) Computational neuroscience deepCEST Humans Image contrast Image Interpretation, Computer-Assisted Image reconstruction Inhomogeneity Magnetic Resonance Imaging Mathematical models Neural networks Neural Networks, Computer Neuroimaging NOE Parameter estimation Parameter robustness Parameter sensitivity Parameter uncertainty probabilistic neural network Robustness (mathematics) Semisolids Tumors Uncertainty uncertainty quantification deepCEST uncertainty quantification probabilistic neural network APT NOE chemical exchange saturation transfer (CEST)
ISSN:	0740-3194, 1522-2594, 1522-2594
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Zusammenfassung:	Purpose Calculation of sophisticated MR contrasts often requires complex mathematical modeling. Data evaluation is computationally expensive, vulnerable to artifacts, and often sensitive to fit algorithm parameters. In this work, we investigate whether neural networks can provide not only fast model fitting results, but also a quality metric for the predicted values, so called uncertainty quantification, investigated here in the context of multi‐pool Lorentzian fitting of CEST MRI spectra at 3T. Methods A deep feed‐forward neural network including a probabilistic output layer allowing for uncertainty quantification was set up to take uncorrected CEST‐spectra as input and predict 3T Lorentzian parameters of a 4‐pool model (water, semisolid MT, amide CEST, NOE CEST), including the B0 inhomogeneity. Networks were trained on data from 3 subjects with and without data augmentation, and applied to untrained data from 1 additional subject and 1 brain tumor patient. Comparison to conventional Lorentzian fitting was performed on different perturbations of input data. Results The deepCEST 3T networks provided fast and accurate predictions of all Lorentzian parameters and were robust to input perturbations because of noise or B0 artifacts. The uncertainty quantification detected fluctuations in input data by increase of the uncertainty intervals. The method generalized to unseen brain tumor patient CEST data. Conclusions The deepCEST 3T neural network provides fast and robust estimation of CEST parameters, enabling online reconstruction of sophisticated CEST contrast images without the typical computational cost. Moreover, the uncertainty quantification indicates if the predictions are trustworthy, enabling confident interpretation of contrast changes.
Bibliographie:	Funding information Max Planck Society; German Research Foundation DFG, Grant/Award Numbers: ZA 814/2‐1; European Union’s Horizon 2020 Research and Innovation Program, Grant/Award Numbers: 667510 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0740-3194 1522-2594 1522-2594
DOI:	10.1002/mrm.28117