The characteristics of fused deposition 3D printing lead to the inevitable step effect of surface contour in the process of forming and manufacturing, which affects molding accuracy. Traditional layering algorithms cannot take into account both printing time and molding accuracy. In this paper, an adaptive layering algorithm based on the optimal volume error is proposed. The angle between the normal vector and the layering direction is used for data optimization. The layer thickness is determined by calculating the volume error, and based on the principle of the optimal volume error, the unequal thickness adaptive layering of each printing layer of the model is realized. The experimental results show that the self-adaptive layering algorithm based on the optimal volume error has a better layering effect, greatly improves the forming efficiency and surface forming accuracy, and has a good adaptability to models with complex surfaces.
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http://dx.doi.org/10.3390/mi13060836 | DOI Listing |
Eur Heart J Imaging Methods Pract
October 2024
Clinical Physiology, Department of Clinical Sciences Lund, Lund University, Lund 221 00, Sweden.
Aims: 4D blood flow measurements by cardiac magnetic resonance imaging (CMR) can be used to simplify blood flow assessment. Compressed sensing (CS) can provide better flow measurements than conventional parallel imaging (PI), but clinical validation is needed. This study aimed to validate stroke volume (SV) measurements by 4D-CS in healthy volunteers and patients while also investigating the influence of the CS image reconstruction parameter on haemodynamic parameters.
View Article and Find Full Text PDFRadiologie (Heidelb)
January 2025
Institut für Forschung in der Operativen Medizin, Universität Witten/Herdecke, Ostmerheimer Str. 200, 51107, Köln, Deutschland.
Criteria for assessment of the significance of scientific articles are presented. The focus is on research design and methodology, illustrated by the classical study on prehospital volume treatment of severely injured individuals with penetrating torso injuries by Bickell et al. (1994).
View Article and Find Full Text PDFInt J Comput Assist Radiol Surg
January 2025
Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany.
Purpose: Lung fissure segmentation on CT images often relies on 3D convolutional neural networks (CNNs). However, 3D-CNNs are inefficient for detecting thin structures like the fissures, which make up a tiny fraction of the entire image volume. We propose to make lung fissure segmentation more efficient by using geometric deep learning (GDL) on sparse point clouds.
View Article and Find Full Text PDFPhys Eng Sci Med
January 2025
School of Clinical Sciences, Queensland University of Technology, Brisbane, QLD, Australia.
Set-up errors are a problem for pre-clinical irradiators that lack imaging capabilities. The aim of this study was to investigate the impact of the potential set-up errors on the dose distribution for a mouse with a xenographic tumour irradiated with a standard Cs-137 cell irradiator equipped with an in-house lead collimator with 10 mm diameter apertures. The EGSnrc Monte-Carlo (MC) code was used to simulate the potential errors caused by displacements of the mouse in the irradiation setup.
View Article and Find Full Text PDFPLOS Digit Health
December 2024
Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, United Kingdom.
Objectives: Evaluating craniofacial phenotype-genotype correlations prenatally is increasingly important; however, it is subjective and challenging with 3D ultrasound. We developed an automated label propagation pipeline using 3D motion- corrected, slice-to-volume reconstructed (SVR) fetal MRI for craniofacial measurements.
Methods: A literature review and expert consensus identified 31 craniofacial biometrics for fetal MRI.
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