End-to-end deep learning for interior tomography with low-dose x-ray CT.

Objective.There are several x-ray computed tomography (CT) scanning strategies used to reduce radiation dose, such as (1) sparse-view CT, (2) low-dose CT and (3) region-of-interest (ROI) CT (called interior tomography). To further reduce the dose, sparse-view and/or low-dose CT settings can be applied together with interior tomography. Interior tomography has various advantages in terms of reducing the number of detectors and decreasing the x-ray radiation dose. However, a large patient or a small field-of-view (FOV) detector can cause truncated projections, and then the reconstructed images suffer from severe cupping artifacts. In addition, although low-dose CT can reduce the radiation exposure dose, analytic reconstruction algorithms produce image noise. Recently, many researchers have utilized image-domain deep learning (DL) approaches to remove each artifact and demonstrated impressive performances, and the theory of deep convolutional framelets supports the reason for the performance improvement.Approach.In this paper, we found that it is difficult to solve coupled artifacts using an image-domain convolutional neural network (CNN) based on deep convolutional framelets.Significance.To address the coupled problem, we decouple it into two sub-problems: (i) image-domain noise reduction inside the truncated projection to solve low-dose CT problem and (ii) extrapolation of the projection outside the truncated projection to solve the ROI CT problem. The decoupled sub-problems are solved directly with a novel proposed end-to-end learning method using dual-domain CNNs.Main results.We demonstrate that the proposed method outperforms the conventional image-domain DL methods, and a projection-domain CNN shows better performance than the image-domain CNNs commonly used by many researchers.

Characteristics of prescriptions and costs for acute upper respiratory tract infections in Chinese outpatient pediatric patients: a nationwide cross-sectional study.

BACKGROUND: To understand the characteristics of prescriptions and costs in pediatric patients with acute upper respiratory infections (AURI) is important for the regulation of outpatient care and reimbursement policy. This study aims to provide evidence on these issues that was in short supply. METHODS: We conducted a retrospective cross-sectional study based on data from National Engineering Laboratory of Application Technology in Medical Big Data. All outpatient pediatric patients aged 0-14 years with an uncomplicated AURI from 1 January 2015 to 31 December 2017 in 138 hospitals across the country were included. We reported characteristics of patients, the average number of medications prescribed per encounter, the categories of medication used and their percentages, the cost per visit and prescription costs of drugs. For these measurements, discrepancies among diverse groups of age, regions, insurance types, and AURI categories were compared. Kruskal-Wallis nonparametric test and Student-Newman-Keuls test were performed to identify differences among subgroups. A multinomial logistic regression was conducted to examine the independent effects of those factors on the prescribing behavior. RESULTS: A total of 1,002,687 clinical records with 2,682,118 prescriptions were collected and analyzed. The average number of drugs prescribed per encounter was 2.8. The most frequently prescribed medication was Chinese traditional patent medicines (CTPM) (36.5% of overall prescriptions) followed by antibiotics (18.1%). It showed a preference of CPTM over conventional medicines. The median cost per visit was 17.91 USD. The median drug cost per visit was 13.84 USD. The expenditures of antibiotics and CTPM per visit (6.05 USD and 5.87 USD) were among the three highest categories of drugs. The percentage of out-of-pocket patients reached 65.9%. Disparities were showed among subgroups of different ages, regions, and insurance types. CONCLUSIONS: The high volume of CPTM usage is the typical feature in outpatient care of AURI pediatric patients in China. The rational and cost-effective use of CPTM and antibiotics still faces challenges. The reimbursement for child AURI cases needs to be enhanced.