Metal objects implanted in the bodies of patients usually generate severe streaking artifacts in reconstructed images of X-ray computed tomography, which degrade the image quality and affect the diagnosis of disease. Therefore, it is essential to reduce these artifacts to meet the clinical demands. In this work, we propose a Gaussian diffusion sinogram inpainting metal artifact reduction algorithm based on prior images to reduce these artifacts for fan-beam computed tomography reconstruction. In this algorithm, prior information that originated from a tissue-classified prior image is used for the inpainting of metal-corrupted projections, and it is incorporated into a Gaussian diffusion function. The prior knowledge is particularly designed to locate the diffusion position and improve the sparsity of the subtraction sinogram, which is obtained by subtracting the prior sinogram of the metal regions from the original sinogram. The sinogram inpainting algorithm is implemented through an approach of diffusing prior energy and is then solved by gradient descent. The performance of the proposed metal artifact reduction algorithm is compared with two conventional metal artifact reduction algorithms, namely the interpolation metal artifact reduction algorithm and normalized metal artifact reduction algorithm. The experimental datasets used included both simulated and clinical datasets. By evaluating the results subjectively, the proposed metal artifact reduction algorithm causes fewer secondary artifacts than the two conventional metal artifact reduction algorithms, which lead to severe secondary artifacts resulting from impertinent interpolation and normalization. Additionally, the objective evaluation shows the proposed approach has the smallest normalized mean absolute deviation and the highest signal-to-noise ratio, indicating that the proposed method has produced the image with the best quality. No matter for the simulated datasets or the clinical datasets, the proposed algorithm has reduced the metal artifacts apparently.

T ₁–T₂ dual modal magnetic resonance imaging (MRI) has attracted considerable interest because it offers complementary diagnostic information, leading to more precise diagnosis. To date, a number of nanostructures have been reported as T₁–T₂ dual modal MR contrast agents (CAs). However, hybrids of nanocubes with both iron and gadolinium (Gd) elements as T₁–T₂ dual modal CAs have not been reported. Herein, we report the synthesis of novel core/shell Fe₃O₄/Gd₂O₃ nanocubes as T₁–T₂ dual-modal CAs and their application for enhanced T₁–T₂ MR imaging of rat livers. A relaxivity study at 1.5 T indicated that our Fe₃O₄/Gd₂O₃ nanocubes have an r₁ value of 45.24 mM⁻¹ s⁻¹ and an r₂ value of 186.51 mM⁻¹ s⁻¹, which were about two folds of those of Gd₂O₃ nanoparticles and Fe₃O₄ nanocubes, respectively. In vivo MR imaging of rats showed both T₁-positive and T₂-negative contrast enhancements in the livers. We envision that our Fe₃O₄/Gd₂O₃ nanocubes could be applied as T₁–T₂ dual modal MR CAs for a wide range of theranostic applications in the near future.

Support vector machine (SVM)‐based multivariate pattern analysis (MVPA) has delivered promising performance in decoding specific task states based on functional magnetic resonance imaging (fMRI) of the human brain. Conventionally, the SVM‐MVPA requires careful feature selection/extraction according to expert knowledge. In this study, we propose a deep neural network (DNN) for directly decoding multiple brain task states from fMRI signals of the brain without any burden for feature handcrafts. We trained and tested the DNN classifier using task fMRI data from the Human Connectome Project's S1200 dataset (N = 1,034). In tests to verify its performance, the proposed classification method identified seven tasks with an average accuracy of 93.7%. We also showed the general applicability of the DNN for transfer learning to small datasets (N = 43), a situation encountered in typical neuroscience research. The proposed method achieved an average accuracy of 89.0 and 94.7% on a working memory task and a motor classification task, respectively, higher than the accuracy of 69.2 and 68.6% obtained by the SVM‐MVPA. A network visualization analysis showed that the DNN automatically detected features from areas of the brain related to each task. Without incurring the burden of handcrafting the features, the proposed deep decoding method can classify brain task states highly accurately, and is a powerful tool for fMRI researchers.

Misuse of antibiotics has recently been considered a global issue because of its harmful effects on human health. Since conventional methods have numerous limitations, it is necessary to develop fast, simple, sensitive, and reproducible methods for the detection of antibiotics. Among numerous recently developed methods, aptasensors are fascinating because of their good specificity, sensitivity and selectivity. These kinds of biosensors combining aptamer with colorimetric applications of gold nanoparticles to recognize small molecules are becoming more popular owing to their advantageous features, for example, low cost, ease of use, on-site analysis ability using naked eye and no prerequisite for modern equipment. In this review, we have highlighted the recent advances and working principle of gold nanoparticles based colorimetric aptasensors as promising methods for antibiotics detection in different food and environmental samples (2011–2020). Furthermore, possible advantages and disadvantages have also been summarized for these methods. Finally, the recent challenges, outlook, and promising future perspectives for developing novel aptasensors are also considered.