Computational fluid dynamics (CFD) is widely employed to predict hemodynamic characteristics in arterial models, while not friendly to clinical applications due to the complexity of numerical simulations. Alternatively, this work proposed a framework to estimate hemodynamics in vessels based on angiography images using machine learning (ML) algorithms. First, the iodine contrast perfusion in blood was mimicked by a flow of dye diffusing into water in the experimentally validated CFD modeling. The generated projective images from simulations imitated the counterpart of light passing through the flow field as an analogy of X-ray imaging. Thus, the CFD simulation provides both the ground truth velocity field and projective images of dye flow patterns. The rough velocity field was estimated using the optical flow method (OFM) based on 53 projective images. ML training with least absolute shrinkage, selection operator and convolutional neural network was conducted with CFD velocity data as the ground truth and OFM velocity estimation as the input. The performance of each model was evaluated based on mean absolute error and mean squared error, where all models achieved or surpassed the criteria of 3 × 10 and 5 × 10 m/s, respectively, with a standard deviation less than 1 × 10 m/s. Finally, the interpretable regression and ML models were validated with over 613 image sets. The validation results showed that the employed ML model significantly reduced the error rate from 53.5% to 2.5% on average for the v-velocity estimation in comparison with CFD. The ML framework provided an alternative pathway to support clinical diagnosis by predicting hemodynamic information with high efficiency and accuracy.
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http://dx.doi.org/10.3390/bioengineering9110622 | DOI Listing |
Heliyon
December 2024
Laboratory of Agri-food Research and Innovation, Graduate Program in Animal Science, School of Medicine and Life Sciences, Pontifícia Universidade Católica do Paraná, Rua Imaculada Conceição 1155, Curitiba, Paraná, 80215-901, Brazil.
This study aimed to assess consumer perceptions and the connections between consumers' health-related concerns and their perceptions of probiotic fermented sausage. The study was carried out using a 4-step online questionnaire composed of: (1) identification and recruitment; (2) application of the completion test; (3) attitudinal exploration; (4) socioeconomic inquiry. The online test was applied using images simulating the shopping experience of a couple in a supermarket.
View Article and Find Full Text PDFJ Forensic Sci
December 2024
ai2-3D Forensics, Woodbridge, Ontario, Canada.
Single view metrology poses a persistent challenge in extracting accurate quantitative information from individual images or video frames within the realm of forensic video analysis. Methods such as reverse projection, projective geometry, and photogrammetry have been used in the past with success but require validation and understanding of the limitations of each method. This study aims to conduct a preliminary validation of the subject height estimation feature in Amped FIVE software, which relies on the principles of single view metrology.
View Article and Find Full Text PDFComput Biol Chem
November 2024
Department of Computer Science, Sri Padmavathi Mahila Visvavidyalayam, Tirupati, India.
The rapid acquisition of projective images with low radiation dose is essential in computed tomography with diffraction enhanced imaging to extract absorption, refraction, and scattering images from weakly absorbing specimens. This plays a critical role in applying diffraction enhanced imaging to biological and medical imaging. In this study, an improved diffraction enhanced imaging method is proposed to rapidly implement X-ray trimodal computed tomography.
View Article and Find Full Text PDFFront Artif Intell
October 2024
Mechatronics and Autonomous Research Lab, Purdue University, Mechanical Engineering, Indianapolis, IN, United States.
The goal of achieving autonomous navigation for agricultural robots poses significant challenges, mostly arising from the substantial natural variations in crop row images as a result of weather conditions and the growth stages of crops. The processing of the detection algorithm also must be significantly low for real-time applications. In order to address the aforementioned requirements, we propose a crop row detection algorithm that has the following features: Firstly, a projective transformation is applied to transform the camera view and a color-based segmentation is employed to distinguish crop and weed from the background.
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