Purpose: To evaluate the feasibility of using single-source dual-energy CT (SS DECT) to quantify and differentiate calcium carbonate (CA) and non-calcium carbonate (NCA) components of pancreatic duct stones (PDS) with mixed composition.

Materials And Methods: A total of 12 PDS harvested from general surgery department in our hospital were analyzed with micro-CT as a reference standard for CA and NCA composition. These stones were placed in a TOS water phantom of 35 cm diameter to simulate standard adult body size. High- and low-energy image sets were acquired from SS DECT scans with high/low tube potential pairs of 80 kVp/140 kVp. All the image sets were imported into an in-house software for further post-processing. CT number ratio (CTR), defined as the ratio of the CT number at 80 kVp to 140 kVp was calculated for each pixel of the images. Threshold was preset between 1.00 and 1.25 to classify CA and NCA components. Pixels in PDS with CTR higher than the threshold were classified as CA, and those with CTR lower than the threshold were classified as NCA. The percentages of CA and NCA for each stone were determined by calculating the number of CA and NCA pixels. Finally, the minimal, maximal and root-mean-square errors (RMSE) of composition measured by SS DECT under each threshold were calculated by referring to the composition data from micro-CT. The optimal threshold was determined with the minimal RMSE. A paired t test was used to compare the stone composition determined by DECT with micro-CT.

Results: The optimal CTR threshold was 1.16, with RMSE of 6.0%. The minimum and maximum absolute errors were 0.22% and 11.35%, respectively. Paired t test showed no significant difference between DECT and micro-CT for characterizing CA and NCA composition (p = 0.414).

Conclusion: SS DECT is a potential approach for quantifying and differentiating CA and NCA components in PDS with mixed composition.

Download full-text PDF

Source
http://dx.doi.org/10.1007/s00261-018-1837-0DOI Listing

Publication Analysis

Top Keywords

nca components
12
pancreatic duct
8
duct stones
8
single-source dual-energy
8
nca
8
pds mixed
8
nca composition
8
image sets
8
threshold classified
8
paired test
8

Similar Publications

A novel method of cognitive overload assessment based on a fusion feature selection using EEG signals.

J Neural Eng

December 2024

Information science, National digital switching center, Kexuedadao No.62, Zhengzhou, Henan, China, Zhengzhou, Henan, 450000, CHINA.

Cognitive overload, as an overload state of cognitive workload, negatively impacts individuals' task performance and mental health. Cognitive overload assessment models based on Electroencephalography (EEG) can effectively prevent the occurrence of overload through early warning, thereby enhancing task execution efficiency and safeguarding individuals' mental health. Although existing EEG-based cognitive load assessment methods have achieved significant research outcomes, evaluating cognitive overload remains an ongoing challenge.

View Article and Find Full Text PDF

A Novel Non-Contact Multi-User Online Indoor Positioning Strategy Based on Channel State Information.

Sensors (Basel)

October 2024

School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK.

The IEEE 802.11bf-based wireless fidelity (WiFi) indoor positioning system has gained significant attention recently. It is important to recognize that multi-user online positioning occurs in real wireless environments.

View Article and Find Full Text PDF

This paper seeks to explore the influence of success factors, specifically motivation and course quality, on MOOC retention intention. Going beyond a mere examination of these motivational and quality factors, the study investigates students' motivation, considering needs, interests, course system, content, and service quality. Methodologically, a questionnaire survey was conducted, collecting data from 311 students enrolled in online courses.

View Article and Find Full Text PDF

We report a design for a synergistic lithium (Li) metal hosting layer for high-loading Li(Ni,Co,Al)O (NCA) (≥5 mA h cm)||Li-metal full cells in carbonate electrolytes. Based on density functional theory calculations, the hosting layer was designed as a three-dimensional silver/carbon composite nanofiber (Ag/CNF) network with high Li affinity and a platinum (Pt)-coated polypropylene separator with low Li affinity. This design enabled the tailoring of horizontal Li deposition on the Ag/CNF hosting layer.

View Article and Find Full Text PDF

Purpose: Using computer-aided design (CAD) systems, this research endeavors to enhance breast cancer segmentation by addressing data insufficiency and data complexity during model training. As perceived by computer vision models, the inherent symmetry and complexity of mammography images make segmentation difficult. The objective is to optimize the precision and effectiveness of medical imaging.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!