Using an artificial neural network for fast mapping of the oxygen extraction fraction with combined QSM and quantitative BOLD
Coated Materials, Biocompatible
Microfluidic Analytical Techniques
Neoplastic Stem Cells
© 2019 International Society for Magnetic Resonance in Medicine Purpose: To apply an artificial neural network (ANN) for fast and robust quantification of the oxygen extraction fraction (OEF) from a combined QSM and quantitative BOLD analysis of gradient echo data and to compare the ANN to a traditional quasi-Newton (QN) method for numerical optimization. Methods: Random combinations of OEF, deoxygenated blood volume (v), R2, and nonblood magnetic susceptibility ((χnb)) with each parameter following a Gaussian distribution that represented physiological gray matter and white matter values were used to simulate quantitative BOLD signals and QSM values. An ANN was trained with the simulated data with added Gaussian noise. The ANN was applied to multigradient echo brain data of 7 healthy subjects, and the reconstructed parameters and maps were compared to QN results using Student t test and Bland-Altman analysis. Results: Intersubject means and SDs of gray matter were OEF =43.5 ± 0.8%,R2 =13.5 ± 0.3 Hz, v =3.4 ± 0.1%, χnb=−25 ± 5 ppb for ANN; and OEF = 43.8 ± 5.2%, R2 =12.2 ± 0.8 Hz, v =4.2 ± 0.6%, χnb=−39 ± 7 ppb for QN, with a significant difference ((P<0.05) for R2, v, and χnb). For white matter, they were OEF = 47.5 ± 1.1%, R 2 =17.1 ± 0.4 Hz, v =2.5 ± 0.2%, χnb=−38 ± 5 ppb for ANN; and OEF 42.3 ± 5.6%, R2 =16.7 ± 0.7 Hz, v =2.9 ± 0.3%, χnb=−45 ± 9 ppb for QN, with a significant difference (P<0.05) for OEF and v. ANN revealed more gray–white matter contrast but less intersubject variation in OEF than QN. In contrast to QN, the ANN reconstruction did not need an additional sequence for parameter initialization and took approximately 1 s rather than roughly 1 h. Conclusion: ANNs allow faster and, with regard to initialization, more robust reconstruction of OEF maps with lower intersubject variation than QN approaches.
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