Purpose: We studied the prevalence of celiac trunk and its anatomical variations on diagnostic computed tomography angiography (CTA) studies and have proposed a new classification to define the celiac artery (CA) variations based on embryology.
Material And Methods: We retrospectively assessed the celiac trunk variations in 1113 patients who came to our department for diagnostic CTA for liver and renal donor workup. The patient data were acquired from the Picture Archiving and Communication System of our institutions. We analysed the celiac trunk's origin and branching pattern, including the superior mesenteric artery (SMA) and inferior phrenic artery (IPA).
Results: We evaluated the CTA studies of 1050 patients. A normal trifurcation pattern, the most common type, was observed in 39% of cases. Variation with CA + left IPA was the most common subtype. Other variations noted in the study and their incidences are listed in the table below. We attempted to propose a new classification based on embryo-logy, which comprises 6 main types and their subtypes. We also analysed previous studies from the literature, including cadaveric, post-mortem, CTA, and digital subtraction angiography studies and compared them with the present study.
Conclusions: Because variations of CA classifications reported to date do not encompass all CA branching pattern variants, we have proposed a new classification that incorporates most of the variants. We reiterate the clinical importance of anatomical variants of CA, IPA, and SMA in surgical and interventional radiology procedures.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9673974 | PMC |
http://dx.doi.org/10.5114/pjr.2022.120525 | DOI Listing |
Sensors (Basel)
December 2024
School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450000, China.
To address the limitations of existing deep learning-based algorithms in detecting surface defects on brake pipe ends, a novel lightweight detection algorithm, FP-YOLOv8, is proposed. This algorithm is developed based on the YOLOv8n framework with the aim of improving accuracy and model lightweight design. First, the C2f_GhostV2 module has been designed to replace the original C2f module.
View Article and Find Full Text PDFSensors (Basel)
December 2024
NUS-ISS, National University of Singapore, Singapore 119615, Singapore.
The attention mechanism is essential to (CNN) vision backbones used for sensing and imaging systems. Conventional attention modules are designed heuristically, relying heavily on empirical tuning. To tackle the challenge of designing attention mechanisms, this paper proposes a novel probabilistic attention mechanism.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Information and Electronic Engineering, International Hellenic University, 57001 Thessaloniki, Greece.
Recent advances in emotion recognition through Artificial Intelligence (AI) have demonstrated potential applications in various fields (e.g., healthcare, advertising, and driving technology), with electroencephalogram (EEG)-based approaches demonstrating superior accuracy compared to facial or vocal methods due to their resistance to intentional manipulation.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.
Coronary artery stenosis detection remains a challenging task due to the complex vascular structure, poor quality of imaging pictures, poor vessel contouring caused by breathing artifacts and stenotic lesions that often appear in a small region of the image. In order to improve the accuracy and efficiency of detection, a new deep-learning technique based on a coronary artery stenosis detection framework (DCA-YOLOv8) is proposed in this paper. The framework consists of a histogram equalization and canny edge detection preprocessing (HEC) enhancement module, a double coordinate attention (DCA) feature extraction module and an output module that combines a newly designed loss function, named adaptive inner-CIoU (AICI).
View Article and Find Full Text PDFSensors (Basel)
December 2024
College of Computer and Information Sciences (CCIS), King Saud University, Riyadh 11543, Saudi Arabia.
One of the most promising applications for electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is motor rehabilitation through motor imagery (MI) tasks. However, current MI training requires physical attendance, while remote MI training can be applied anywhere, facilitating flexible rehabilitation. Providing remote MI training raises challenges to ensuring an accurate recognition of MI tasks by healthcare providers, in addition to managing computation and communication costs.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!