Abstract Chemical exchange saturation transfer (CEST) is a versatile technique that enables noninvasive detections of endogenous metabolites present in low concentrations in living tissue. However, CEST imaging suffers from an inherently low signal‐to‐noise ratio (SNR) due to the decreased water signal caused by the transfer of saturated spins. This limitation challenges the accuracy and reliability of quantification in CEST imaging. In this study, a novel spatial–spectral denoising method, called BOOST (suBspace denoising with nOnlocal lOw‐rank constraint and Spectral local‐smooThness regularization), was proposed to enhance the SNR of CEST images and boost quantification accuracy. More precisely, our method initially decomposes the noisy CEST images into a low‐dimensional subspace by leveraging the global spectral low‐rank prior. Subsequently, a spatial nonlocal self‐similarity prior is applied to the subspace‐based images. Simultaneously, the spectral local‐smoothness property of Z ‐spectra is incorporated by imposing a weighted spectral total variation constraint. The efficiency and robustness of BOOST were validated in various scenarios, including numerical simulations and preclinical and clinical conditions, spanning magnetic field strengths from 3.0 to 11.7 T. The results demonstrated that BOOST outperforms state‐of‐the‐art algorithms in terms of noise elimination. As a cost‐effective and widely available post‐processing method, BOOST can be easily integrated into existing CEST protocols, consequently promoting accuracy and reliability in detecting subtle CEST effects.
Parameter retrieval is a typical nonconvex optimization problem in a wide range of research and engineering fields. Classic methods tackle the parameter retrieval problem by feature extraction from the subspace or transform domain. In this paper, we proposed a network-based method to directly solve the nonconvex optimization problem on parameters estimation of complex exponential signals, with no requirement of labeled data. The proposed network has an architecture similar to the Autoencoder network but with the decoder sub-network replaced by a complex exponential signal generator. After training the network to fit the signal parameters to the acquired data, one could obtain the parameters, i.e., frequencies, decay rates, and intensities, and reconstruct the signal. By this work, we show that with a simple application of a lightweight neural network, nonconvex optimization problems like parameter retrieval can be solved efficiently, even without any intricately designed algorithms. We also discuss the robustness of the network-based method by repeated experiments and present the failure cases to indicate the limitations of this method.
When observing high-speed maneuvering targets, the relative motion between the target and the radar will produce linear range walking, range bending, Doppler spread and other phenomena, resulting in the failure to obtain higher SNR gain by prolonging the coherent integration time. A coherent integration algorithm based on Wigner Ville Distribution (WVD) is proposed for target detection with uniform acceleration in any direction. The algorithm first uses the second-order Keystone transform and combines the linear detection algorithm to correct range walking and range bending, then introduces the delay variable and constructs a third-order matrix based on the traditional WVD algorithm to convert the echo signal to the time-frequency domain plane for third-order phase parameter estimation, and compensates the third-order phase coefficient, and then uses the LVD algorithm to estimate and compensate its second-order phase coefficient to achieve the correction of Doppler spread. The algorithm has excellent parameter estimation performance in a strong noise environment and has lower algorithm complexity compared with the traditional full-dimension search algorithm.
Two-dimensional (2D) J-resolved NMR technique offers a natural solution for disentangling complex mixtures that suffer from crowded spectra in 1D NMR. The applicability of classical 2D J-resolved spectroscopy is inevitably limited by phase-twist lineshapes and strong coupling artifacts. Here, a general and robust NMR method is proposed to record 2D absorption-mode J-resolved spectra in rapid acquisition manner. This method can also reduce the impact of strong coupling artifacts, thus achieving full considerations for applications. Intuitively, this method delivers pure chemical shifts along one dimension and orthogonally adds J couplings along the other dimension, free of 45° spectral shearing. It may provide a powerful tool for structural and configurational studies as well as biological analyses.
Wing's morphing leading edge, drooping in a seamless way, has significant potential for noise abatement and drag reduction. Innovative design methods for compliant skin and internal actuating mechanism, respectively, are proposed and validated through a mockup in this paper. For the skin, a collaborative optimization method is presented, which takes all design variables, continuous and discrete, into account simultaneously. Moreover, to overcome the drawback of conventional algorithm, which is insufficient for deformation control in critical regime, weight penalty is imposed on present objective function. On the other hand, an internal kinematic actuating mechanism is designed from an improved concept, of which positions of level-rod hinges are optimized in a larger zone to fit the deflection requirement. The test of mockup validates the above methods, and excellent morphing quality of the compliant skin proves the advancement of the collaborative optimization method. However, the design method of internal actuating mechanism needs further improvement, and the error induced deteriorates the final morphing quality of the mockup.
Subjecting structures to external forces inevitably leads to the generation of vibrations. For high-rise and flexible structures, excessive vibrations can significantly impact their normal operation and structural integrity. To mitigate these undesirable vibrations, structural vibration control is essential. Among various passive control methods, the tuned mass damper (TMD) is widely used for its ability to reduce vibrations through resonance with the structure. Meanwhile, the active mass damper (AMD) can also achieve an excellent control efficiency by exerting active control force on structures. Hybrid control integrates the benefits of multiple control strategies and applies the control forces on the same structure simultaneously. Hybrid mass damper (HMD) combines the passive characteristics of TMD and the active features of AMD, overcoming the limitations associated with using either system in isolation. This paper proposes a novel hybrid control method based on virtual TMD algorithm and optimizes the parameters of HMD by weighting the structural response and stroke of HMD to improve the comprehensive control performance. The effectiveness of this optimization is substantiated in the frequency domain. Additionally, numerical simulations are conducted to compare the optimized HMD with the traditional TMD and the unoptimized HMD, demonstrating both the effectiveness of the optimization and the superior control performance of the optimized HMD. The numerical results indicate that the optimized HMD reduces stroke by 15.6% compared to the unoptimized HMD on the premise that the control effect only loses 2.4%. Overall, the optimized HMD demonstrates superior comprehensive control performance relative to the unoptimized HMD.
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