Background Dual-energy computed tomography (DECT) has been widely used due to improved substances identification from additional spectral information. The quality of material-specific image produced by DECT attaches great importance to the elaborated design of the basis material decomposition method. Objective The aim of this work is to develop and validate a data-driven algorithm for the image-based decomposition problem. Methods A deep neural net, consisting of a fully convolutional net (FCN) and a fully connected net, is proposed to solve the material decomposition problem. The former net extracts the feature representation of input reconstructed images, and the latter net calculates the decomposed basic material coefficients from the joint feature vector. The whole model was trained and tested using a modified clinical dataset. Results The proposed FCN delivers image with about 60% smaller bias and 70% lower standard deviation than the competing algorithms, suggesting its better material separation capability. Moreover, FCN still yields excellent performance in case of photon noise. Conclusions Our deep cascaded network features high decomposition accuracies and noise robust property. The experimental results have shown the strong function fitting ability of the deep neural network. Deep learning paradigm could be a promising way to solve the nonlinear problem in DECT.
The DNN model is applicable to the decomposition tasks with different dual energies. Experimental results demonstrated the strong function fitting ability of DNN. Thus, the Deep learning paradigm provides a promising approach to solve the nonlinear problem in DECT.
Ensuring the integrity of remote data is the prerequisite for implementing cloud-edge computing. Traditional data integrity verification schemes make users spend a lot of time regularly checking their data, which is not suitable for large-scale IoT (Internet of Things) data. On the other hand, the introduction of a third-party auditor (TPA) may bring about greater privacy and security issues. We use blockchain to address the problem of TPA. However, implementing dynamic integrity verification with blockchain is a bigger challenge due to the low throughput and poor scalability of blockchain. More importantly, whether there is a security problem with blockchain-based integrity verification is not yet known. In this paper, we propose a scalable blockchain-based integrity verification scheme that implements fully dynamic operations and blockless verification. The scheme builds scalable homomorphic verification tags based on ZSS (Zhang-Safavi-Susilo) short signatures. We exploit smart contract technology to replace TPA for integrity verification tasks, which not only eliminates the risk of privacy leakage but also resists collusion attacks. Furthermore, we formally define a blockchain-based security model and prove that our scheme is secure under the security assumption of cryptographic primitives. Finally, the mathematical analysis of our scheme shows that both the communication complexity and the communication complexity of an audit are
O
c
, in which
c
is the number of challenge blocks. We compare our scheme with other schemes, and the results show that our scheme has the lowest time consumption to complete an audit.
A facile
and efficient approach to the synthesis of 1,2,5-trisubstituted
imidazoles is developed via a multicomponent reaction under metal-free
catalysis. Under Brønsted acid catalysis, the desired products
can be obtained from readily available vinyl azides, aromatic aldehydes,
and aromatic amines without generating any toxic waste. The convenient
operations and high functional group compatibility indicate that this
approach offers an attractive alternative method for the synthesis
of imidazole derivatives.
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