Purpose
Glutamate weighted Chemical Exchange Saturation Transfer (GluCEST) MRI is a noninvasive technique for mapping parenchymal glutamate in the brain. Because of the sensitivity to field (B0) inhomogeneity, the total acquisition time is prolonged due to the repeated image acquisitions at several saturation offset frequencies, which can cause practical issues such as increased sensitivity to patient motions. Because GluCEST signal is derived from the small z‐spectrum difference, it often has a low signal‐to‐noise‐ratio (SNR). We proposed a novel deep learning (DL)‐based algorithm armed with wide activation neural network blocks to address both issues.
Methods
B0 correction based on reduced saturation offset acquisitions was performed for the positive and negative sides of the z‐spectrum separately. For each side, a separate deep residual network was trained to learn the nonlinear mapping from few CEST‐weighted images acquired at different ppm values to the one at 3 ppm (where GluCEST peaks) in the same side of the z‐spectrum.
Results
All DL‐based methods outperformed the “traditional” method visually and quantitatively. The wide activation blocks‐based method showed the highest performance in terms of Structural Similarity Index (SSIM) and peak signal‐to‐noise ratio (PSNR), which were 0.84 and 25dB respectively. SNR increases in regions of interest were over 8dB.
Conclusion
We demonstrated that the new DL‐based method can reduce the entire GluCEST imaging time by ˜50% and yield higher SNR than current state‐of‐the‐art.
Magnetic resonance fingerprinting (MRF) is a new technique for simultaneously quantifying multiple MR parameters using one temporally resolved MR scan. But its brute-force dictionary generating and searching (DGS) process causes a huge disk space demand and computational burden, prohibiting it from a practical multiple slice high-definition imaging. The purpose of this paper was to provide a fast and space efficient DGS algorithm for MRF. Based on an empirical analysis of properties of the distance function of the acquired MRF signal and the pre-defined MRF dictionary entries, we proposed a parameter separable MRF DGS method, which breaks the multiplicative computation complexity into an additive one and enabling a resolution scalable multi-resolution DGS process, which was dubbed as MRF ZOOM. The evaluation results showed that MRF ZOOM was hundreds or thousands of times faster than the original bruteforce DGS method. The acceleration was even higher when considering the time difference for generating the dictionary. Using a high precision quantification, MRF can find the right parameter values for a 64x64 imaging slice in 117 secs. Our data also showed that spatial constraints can be used to further speed up MRF ZOOM.3
In complex systems, functional dependency and physical dependency may have a coupling effect. In this paper, the reliability of a k-out-of-n system is analyzed considering loadsharing effect and failure mechanism (FM) propagation. Three types of FMs are considered and an accumulative damage model is proposed to illustrate the system behavior of the k-out-of-n system and the coupling effect between load-sharing effect and FM propagation effect. A combinational algorithm based on Binary decision diagram (BDD) and Monte-Carlo simulation is presented to evaluate the complex system behavior and reliability of the k-out-of-n system. A current stabilizing system that consists of a 3-out-of-6 subsystem with FM propagation effect is presented as a case to illustrate the complex behavior and to verify the applicability of the proposed method. Due to the coupling effect change, the main mechanism and failure mode will be changed, and the system lifetime is shortened. Reasons are analyzed and results show that different sensitivity factors of three different FMs lead to the change of the development rate, thus changing the failure scenario. Neglecting the coupling effect may lead to an incomplete and ineffective measuring and monitoring plan. Design strategies must be adopted to make the FM propagation insensitive to load-sharing effect.
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