Purpose: Insertion point identification is a major challenge for percutaneous interventions. Planning in 2D slice image data is time-consuming and inefficient. Automated path planning can help to overcome these challenges. However, the setup of the intervention room is difficult to consider. In addition, transferring the insertion point to the skin is often prone to error. Therefore, a visualization for an automated path planning was implemented.
Methods: A condition-based automated path planning was calculated with path length, distance to risk structures and insertion angle. The results were displayed on a phantom using projector-based augmented reality (AR) with an access point selection using the insertion needle. Two variants of the insertion visualization and three target displays were evaluated in a user study.
Results: A visualization of insertion points with a representation of the path quality resulted in a choice of safer paths, compared with no insertion point display or no coding of the path quality. A representation of the target was preferred in the final survey, but did not perform better. A target display separate from the insertion point visualization reduced interferences between visualizations.
Conclusion: A projector-based AR visualization of automated path planning results supports insertion point identification for percutaneous interventions. A display of the path quality enables the choice of safe access paths especially for unexperienced users. Further research is needed to identify clinical benefits and applicability.
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http://dx.doi.org/10.1007/s11548-022-02690-4 | DOI Listing |
Int J Comput Assist Radiol Surg
January 2025
Department of Medical Biophysics, University of Toronto, Toronto, Canada.
Sci Rep
January 2025
Xiamen Topstar Co., Ltd., Xiamen, 361000, Fujian, China.
Automated guided vehicles play a crucial role in transportation and industrial environments. This paper presents a proposed Bio Particle Swarm Optimization (BPSO) algorithm for global path planning. The BPSO algorithm modifies the equation to update the particles' velocity using the randomly generated angles, which enhances the algorithm's searchability and avoids premature convergence.
View Article and Find Full Text PDFSci Rep
January 2025
School of Management, Shenyang University of Technology, Shenyang, 110870, China.
The production stage of an automated job shop is closely linked to the automated guided vehicle (AGV), which needs to be planned in an integrated manner to achieve overall optimization. In order to improve the collaboration between the production stages and the AGV operation system, a two-layer scheduling optimization model is proposed for simultaneous decision making of batching problems, job sequences and AGV obstacle avoidance. Under the AGV automatic path seeking mode, this paper adopts a data-driven Bayesian network method to portray the transportation time of AGVs based on the historical operation data to control the uncertainty of the transportation time of AGVs.
View Article and Find Full Text PDFInt J Med Robot
February 2025
Division of Colorectal Surgery, Department of Surgery, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, Korea.
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J Imaging
December 2024
College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China.
Walnuts possess significant nutritional and economic value. Fast and accurate sorting of shells and kernels will enhance the efficiency of automated production. Therefore, we propose a FastQAFPN-YOLOv8s object detection network to achieve rapid and precise detection of unsorted materials.
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