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https://www.selleckchem.com/products/bay-11-7082-bay-11-7821.html Significantly lower PSw and Ew were observed in NAWM compared to WM (ΔPSw -11.9 mL/100 g/min, p less then .05; ΔEw -4.3%, p less then .01). Significantly lower Ew was observed in NAGM compared to GM (ΔEw -12.1%, p less then .01). Significantly lower PSw and CBF were observed in non-GBCA contrast enhancing lesions compared to NAWM (ΔPSw = -11.5 mL/100 g/min, p less then .05; ΔCBF = -8.1 mL/100 g/min, p less then .05). Ew was significantly higher in non-GBCA enhancing chronic MS lesions compared to NAWM (ΔEw = 1.6%, p less then .05). The lower BBB water exchange in chronic MS lesions is consistent with previously reported observations and may demonstrate metabolic changes associated with MS. PURPOSE To enable fast reconstruction of undersampled motion-compensated whole-heart 3D coronary magnetic resonance angiography (CMRA) by learning a multi-scale variational neural network (MS-VNN) which allows the acquisition of high-quality 1.2 × 1.2 × 1.2 mm isotropic volumes in a short and predictable scan time. METHODS Eighteen healthy subjects and one patient underwent free-breathing 3D CMRA acquisition with variable density spiral-like Cartesian sampling, combined with 2D image navigators for translational motion estimation/compensation. The proposed MS-VNN learns two sets of kernels and activation functions for the magnitude and phase images of the complex-valued data. For the magnitude, a multi-scale approach is applied to better capture the small calibre of the coronaries. Ten subjects were considered for training and validation. Prospectively undersampled motion-compensated data with 5-fold and 9-fold accelerations, from the remaining 9 subjects, were used to evaluate the framework. The proposed approach was compared to Wavelet-based compressed-sensing (CS), conventional VNN, and to an additional fully-sampled (FS) scan. RESULTS The average acquisition time (ms) was 411 for 5-fold, 234 for 9-fold accelerat

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