Sebastian Domsch1, Bettina Mürle2, Sebastian Weingärtner1,3,4, Jascha Zapp1, Frederik Wenz5, Lothar R Schad1. 1. Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Germany. 2. Department of Neuroradiology, Medical Faculty Mannheim, Heidelberg University, Germany. 3. Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota, USA. 4. Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA. 5. Department of Radiation Oncology, Medical Faculty Mannheim, Heidelberg University, Germany.
Abstract
PURPOSE: The oxygen extraction fraction (OEF) is an important biomarker for tissue-viability. MRI enables noninvasive estimation of the OEF based on the blood-oxygenation-level-dependent (BOLD) effect. Quantitative OEF-mapping is commonly applied using least-squares regression (LSR) to an analytical tissue model. However, the LSR method has not yet become clinically established due to the necessity for long acquisition times. Artificial neural networks (ANNs) recently have received increasing interest for robust curve-fitting and might pose an alternative to the conventional LSR method for reduced acquisition times. This study presents in vivo OEF mapping results using the conventional LSR and the proposed ANN method. METHODS: In vivo data of five healthy volunteers and one patient with a primary brain tumor were acquired at 3T using a gradient-echo sampled spin-echo (GESSE) sequence. The ANN was trained with simulated BOLD data. RESULTS: In healthy subjects, the mean OEF was 36 ± 2% (LSR) and 40 ± 1% (ANN). The OEF variance within subjects was reduced from 8% to 6% using the ANN method. In the patient, both methods revealed a distinct OEF hotspot in the tumor area, whereas ANN showed less apparent artifacts in surrounding tissue. CONCLUSION: In clinical scan times, the ANN analysis enables OEF mapping with reduced variance, which could facilitate its integration into clinical protocols. Magn Reson Med 79:890-899, 2018.
PURPOSE: The oxygen extraction fraction (OEF) is an important biomarker for tissue-viability. MRI enables noninvasive estimation of the OEF based on the blood-oxygenation-level-dependent (BOLD) effect. Quantitative OEF-mapping is commonly applied using least-squares regression (LSR) to an analytical tissue model. However, the LSR method has not yet become clinically established due to the necessity for long acquisition times. Artificial neural networks (ANNs) recently have received increasing interest for robust curve-fitting and might pose an alternative to the conventional LSR method for reduced acquisition times. This study presents in vivo OEF mapping results using the conventional LSR and the proposed ANN method. METHODS: In vivo data of five healthy volunteers and one patient with a primary brain tumor were acquired at 3T using a gradient-echo sampled spin-echo (GESSE) sequence. The ANN was trained with simulated BOLD data. RESULTS: In healthy subjects, the mean OEF was 36 ± 2% (LSR) and 40 ± 1% (ANN). The OEF variance within subjects was reduced from 8% to 6% using the ANN method. In the patient, both methods revealed a distinct OEF hotspot in the tumor area, whereas ANN showed less apparent artifacts in surrounding tissue. CONCLUSION: In clinical scan times, the ANN analysis enables OEF mapping with reduced variance, which could facilitate its integration into clinical protocols. Magn Reson Med 79:890-899, 2018.