Multi-energy computed tomography (MECT) is able to acquire simultaneous multi-energy measurements from one scan. In addition, it allows material differentiation and quantification effectively. However, due to the limited energy bin width, the number of photons detected in an energy-specific channel is smaller than that in traditional CT, which results in image quality degradation. To address this issue, in this work, we develop a statistical iterative reconstruction algorithm to acquire high-quality MECT images and high-accuracy material-specific images. Specifically, this algorithm fully incorporates redundant self-similarities within nonlocal regions in the MECT image at one bin and rich spectral similarities among MECT images at all bins. For simplicity, the presented algorithm is referred to as ‘MECT-NSS’. Moreover, an efficient optimization algorithm is developed to solve the MECT-NSS objective function. Then, a comprehensive evaluation of parameter selection for the MECT-NSS algorithm is conducted. In the experiment, the datasets include images from three phantoms and one patient to validate and evaluate the MECT-NSS reconstruction performance. The qualitative and quantitative results demonstrate that the presented MECT-NSS can successfully yield better MECT image quality and more accurate material estimation than the competing algorithms.

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With the widespread application of the Internet of Things (IoT) technology, it is expected to become a new data management model. But IoT devices have limited resource storage and computing power, they are highly vulnerable to hackers and leak sensitive information to third parties. The traditional attribute-based searchable encryption schemes usually require an intelligent terminal to perform complex calculations while accessing data, therefore IoT devices may not be able to withstand excessive calculation burden. In this paper, we present a keyword searchable attribute-based encryption scheme with equality test (KS-ABESwET) in the IoT by combining the notions of attribute-based searchable encryption (ABSE) with equality test. The proposed scheme adopts a keyword search algorithm based on the inverted index and equality test mechanism. If the keyword token match index is successful, the cloud server sends all ciphertexts that meet the conditions to the data user. Then, data user classifies the ciphertexts by equality test mechanism, which is executed by the authorized cloud server to determine whether the two ciphertexts encrypted by different access policies contain the same plaintext without decrypting. In this way, data user does not need to decrypt all ciphertexts, which decreases storage resource consumption of IoT devices and simplifies the complex operations generated by the traditional ABSE schemes. Using outsourcing technology, most calculations in the scheme are outsourced to the server, and IoT devices only perform a few calculations, which reduces greatly the computing and storage burden. Based on the decisional q − 1 assumption and decisional Diffie-Hellman (DDH) assumption, the proposed scheme proves that has chosen-plaintext security and chosen-keyword security. Moreover, through comparative analysis and experimental simulation, our scheme is effective and suitable for IoT environment.INDEX TERMS Internet of Things, keyword searchable, attribute-based encryption, equality test. I. INTRODUCTION A. MOTIVATIONIoT technology proposes the interconnection between different devices, such as smart phones, infinite sensors, RFID etc, achieves data collection, transmission and storage [1]. These IoT devices are widely used in companies, factories, and everyday life, thus data security and privacy protection are crucial. In particular, although IoT devices are diverse in variety, their storage and computing capabilities are limited, it is necessary to reduce their computation burden. In the The associate editor coordinating the review of this manuscript and approving it for publication was Tony Thomas.