Background: Stereotactic computer-aided surgery has provided the surgeon with a means to navigate more safely through diseased or surgically altered sinus anatomy. Accurate registration is vital to successful image-guided surgery. This study compared the accuracy and performance of three registration methods: fiducial, anatomic landmarks, and surface registration.
Methods: Ten fixed cadaveric heads underwent endoscopic computed tomography scan followed by middle meatal antrostomy and sphenoidotomy. Each registration method was performed, and the time required and mean registration error were recorded. Five anatomic sites were then identified and compared with the preoperative computed tomography images. The true distances between the known anatomic sites and the crosshair locations on the images were measured.
Results: Statistically significant differences were noted for mean registration error and time for registration. The mean +/-SEM time for registration for the fiducial, surface, and landmark methods were 5 minutes 24 seconds +/-27 seconds, 1 minute 1 second +/-5 seconds, and 11 minutes 46 seconds +/-45 seconds, respectively. The mean +/-SEM registration error for the fiducial, surface, and landmark methods were 0.48 +/- 0.21 mm, 1.05 +/- 0.06 mm, and 3.1 +/- 0.25 mm, respectively. When the true accuracy of the three registration methods were compared, no significant difference was found between fiducial and surface registration. However, fiducial registration was significantly more accurate than landmark registration at all points. When compared with landmark registration, surface registration was statistically more accurate at all anatomic sites except for the sella turcica and optic nerve.
Conclusion: When the true accuracies of these methods were compared in fixed cadaveric specimens,fiducial and surface registration were statistically similar but were found to be significantly more accurate than landmark registration. Furthermore, when time of registration, accuracy, and ease of use were considered, surface registration was found superior.
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Sensors (Basel)
December 2024
Department of Electrical and Computer Engineering, University of Missouri, Columbia, MO 65211, USA.
Multi-modal systems extract information about the environment using specialized sensors that are optimized based on the wavelength of the phenomenology and material interactions. To maximize the entropy, complementary systems operating in regions of non-overlapping wavelengths are optimal. VIS-IR (Visible-Infrared) systems have been at the forefront of multi-modal fusion research and are used extensively to represent information in all-day all-weather applications.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, China.
Point cloud registration is pivotal across various applications, yet traditional methods rely on unordered point clouds, leading to significant challenges in terms of computational complexity and feature richness. These methods often use k-nearest neighbors (KNN) or neighborhood ball queries to access local neighborhood information, which is not only computationally intensive but also confines the analysis within the object's boundary, making it difficult to determine if points are precisely on the boundary using local features alone. This indicates a lack of sufficient local feature richness.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.
In this paper, we propose a Proof-of-Location (PoL)-based location verification scheme for mitigating Sybil attacks in vehicular ad hoc networks (VANETs). For this purpose, we employ smart contracts for storing the location information of the vehicles. This smart contract is maintained by Road Side Units (RSUs) and acts as a ground truth for verifying the position information of the neighboring vehicles.
View Article and Find Full Text PDFSensors (Basel)
December 2024
College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China.
This paper proposes a registration approach rooted in point cloud clustering and segmentation, named Clustering and Segmentation Normal Distribution Transform (CSNDT), with the aim of improving the scope and efficiency of point cloud registration. Traditional Normal Distribution Transform (NDT) algorithms face challenges during their initialization phase, leading to the loss of local feature information and erroneous mapping. To address these limitations, this paper proposes a method of adaptive cell partitioning.
View Article and Find Full Text PDFNutrients
December 2024
Research Unit for Sport and Physical Activity, Faculty of Sport Sciences and Physical Education, University of Coimbra, 3040-248 Coimbra, Portugal.
Background/objectives: The increasing popularity of acute supplementation among young athletes is concerning, given the limited scientific evidence to guide recommendations specific to this group. Therefore, the aim of this systematic review was to synthesize the available scientific evidence on the acute effects of supplementation in young athletes to understand the impact on physical and cognitive performance.
Methods: Following pre-registration on INPLASY (INPLASY202310017) and according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, systematic searches of three electronic databases (Web of Science, PubMed, and Scopus) were conducted by independent researchers from inception until July 2024.
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