Magnetic resonance (MR) imaging is an important computer-aided diagnosis technique with rich pathological information. The factor of physical and physiological constraint seriously affects the applicability of that technique. Thus, computed tomography (CT)-based radiotherapy is more popular on account of its imaging rapidity and environmental simplicity. Therefore, it is of great theoretical and practical significance to design a method that can construct an MR image from the corresponding CT image. In this paper, we treat MR imaging as a machine vision problem and propose a multi-conditional constraint generative adversarial network (GAN) for MR imaging from CT scan data. Considering reversibility of GAN, both generator and reverse generator are designed for MR and CT imaging, respectively, which can constrain each other and improve consistency between features of CT and MR images. In addition, we innovatively treat the real and generated MR image discrimination as object re-identification; cosine error fusing with original GAN loss is designed to enhance verisimilitude and textural features of the MR image. The experimental results with the challenging public CT-MR image dataset show distinct performance improvement over other GANs utilized in medical imaging and demonstrate the effect of our method for medical image modal transformation.

The three gases CO, H 2 , and CH 4 are currently prevalent in various industrial environments, posing a threat to life and property. However, the crosstalk phenomenon between the gas mixtures cannot be avoided by technological improvement in practical measurements. To adapt to field measurement requirements, this paper experimentally investigates the crosstalk characteristics of mixed gases and proposes a measurement method based on the cross-talk characteristics of field-confusable gas array sensors. The experimental results show that compared with the traditional direct measurement method, the proposed method reduces the crosstalk error from 50.1% to 10.3%, which significantly improves the accuracy of the field detection. At the same time, the method is not limited by the sensor category, which is applicable to the field measurement of various types of array sensors.