Super resolution dual-layer CBCT imaging with model-guided deep learning

Objective: This study aims at investigating a novel super resolution CBCT imaging technique with the dual-layer flat panel detector (DL-FPD). Approach: In DL-FPD based CBCT imaging, the low-energy and high-energy projections acquired from the top and bottom detector layers contain intrinsically mismatched spatial information, from which super resolution CBCT images can be generated. To explain, a simple mathematical model is established according to the signal formation procedure in DL-FPD. Next, a dedicated recurrent neural network (RNN), named as suRi-Net, is designed by referring to the above imaging model to retrieve the high resolution dual-energy information. Different phantom experiments are conducted to validate the performance of this newly developed super resolution CBCT imaging method. Main Results: Results show that the proposed suRi-Net can retrieve high spatial resolution information accurately from the low-energy and high-energy projections having lower spatial resolution. Quantitatively, the spatial resolution of the reconstructed CBCT images of the top and bottom detector layers is increased by about 45% and 54%, respectively. Significance: In future, suRi-Net provides a new approach to achieve high spatial resolution dual-energy imaging in DL-FPD based CBCT systems

Automated Classification Model With OTSU and CNN Method for Premature Ventricular Contraction Detection

Premature ventricular contraction (PVC) is one of the most common arrhythmias which can cause palpitation, cardiac arrest, and other symptoms affecting the work and rest activities of a patient. However, patients hardly decipher their own feelings to determine the severity of the disease thus, requiring a professional medical diagnosis. This study proposes a novel method based on image processing and convolutional neural network (CNN) to extract electrocardiography (ECG) curves from scanned ECG images derived from clinical ECG reports, and segment and classify heartbeats in the absence of a digital ECG data. The ECG curve is extracted using a comprehensive algorithm that combines the OTSU algorithm with erosion and dilation. This algorithm can efficiently and accurately separate the ECG curve from the ECG background grid. The performance of the classification model was evaluated and optimized using hundreds of clinical ECG data collected from Fujian Provincial Hospital. Additionally, thousands of clinical ECG reports were scanned to digital images as the test set to confirm the accuracy of the algorithm for practical application. Results showed that the average sensitivity, specificity, positive predictive value, and accuracy of the proposed model on the MIT-BIH dataset were 95.47%, 97.72%, 98.75%, and 98.25%, respectively. The classification average sensitivity, specificity, positive predictive value, and accuracy based on clinical scanned ECG images can reach to 97.24%, 81.6%, 83.8%, and 89.33%, respectively, and the clinical feasibility is high. Overall, the proposed method can extract ECG curves from scanned ECG images efficiently and accurately. Furthermore, it performs well on heartbeat classification of normal (N) and ventricular premature heartbeat

Power Allocation Scheme for Femto-to-Macro Downlink Interference Reduction for Smart Devices in Ambient Intelligence

In this paper, we present an analysis on the characteristics of cross-tier interference in regard to femtocells deployed in LTE cellular networks. We also present a cross-tier SLNR-based water filling (CSWF) power allocation algorithm for the reduction of interference from femtocell to macrocell for smart devices used in ambient intelligence. The results of this study show that CSWF significantly improves the macro UE performance around a femtocell access point (AP) from the SINR and throughput perspective. The CSWF algorithm also provides a relative gain on the throughput of femtocell UEs compared to frequency partitioning. Furthermore, the proposed algorithm has a low complexity and is implemented on the femto-AP side only, therefore not affecting the macro system

Optimized antimicrobial and antiproliferative activities of titanate nanofibers containing silver

Titanate nanofibers containing silver have been demonstrated through the experiments reported herein to have effective antifungal and antiproliferative activities in the presence of UV light. The titanate nanofibers containing silver can be fabricated by means of ion exchange followed by a topochemical process in an environment suitable for reductive reactions. Excellent antibacterial, antifungal, and antiproliferative activities could be demonstrated by both Ag2Ti5O11 · xH2O and Ag/titanate (UV light irradiation) due to their unique structures and compositions, which have photocatalytic activities to generate reactive oxygen species and capabilities to continuously release the silver ions. Therefore these materials have the potential to produce a membrane for the treatment of superficial malignant tumor, esophageal cancer, or cervical carcinoma. They may also hold utility if incorporated into a coating on stents in moderate and advanced stage esophageal carcinoma or for endoscopic retrograde biliary drainage. These approaches may significantly reduce infections, inhibit tumor growth, and importantly, improve quality of life and prolong survival time for patients with tumors