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Magnetic Resonance Imaging Estimation of Longitudinal Relaxation Rate Change (ΔR1) in Dual Gradient Echo Sequences
H Bagher-Ebadian1, S P Nejad-Davarani1, M M Ali1
1Physics Dept., Oakland University, Rochester, MI 48309 and Dept. Nuclear Engineering, Shiraz University, Shiraz; Siamak P. Nejad-Davarani - Dept. Neurology, Henry Ford Hospital and Dept. Biomedical Eng. University of Michigan, Ann Arbor; Meser M. Ali - Dept. Radiology, Henry Ford Hospital, Stephen L. Brown - Dept. Radiation Oncology, Henry Ford Hospital, Malek Makki, - Dept. Diagnostic Imaging, University Children Hospital of Zurich, Zurich, Switzerland, Quan Jiang - Dept. Neurology, Henry Ford Hospital; Douglas. C. Noll - Dept. Biomedical Engineering, University of Michigan, Ann Arbor, James R. Ewing Dept. Neurology, Henry Ford Hospital, Dept. Neurology, Wayne State University., and Dept. of Physics, Oakland University.
Abstract:
Magnetic Resonance Imaging (MRI) estimation of contrast agent concentration in fast pulse sequences such as Dual Gradient Echo (DGE) imaging is challenging. An Adaptive Neural Network (ANN) was trained with a map of contrast agent concentration estimated by Look-Locker (LL) technique (modified version of inversion recovery imaging) as a gold standard. Using a set of features extracted from DGE MRI data, an ANN was trained to create a voxel based estimator of the time trace of CA concentration. The ANN was trained and tested with the DGE and LL information of six Fisher rats using a K-Fold Cross-Validation (KFCV) method with 60 folds and 10500 samples. The Area Under the Receiver Operator Characteristic Curve (AUROC) for 60 folds was used for training, testing and optimization of the ANN. After training and optimization, the optimal ANN (4:7:5:1) produced maps of CA concentration which were highly correlated (r =0.89, P < 0.0001) with the CA concentration estimated by the LL technique. The estimation made by the ANN had an excellent overall performance (AUROC = 0.870).
