Polyvinyl alcohol cryogel (PVA-C) is presented as a vessel-mimicking material for use in anatomically realistic Doppler flow phantoms. Three different batches of 10% wt PVA-C containing (i) PVA-C alone, (ii) PVA-C with antibacterial agent and (iii) PVA-C with silicon carbide particles were produced, each with 1-6 freeze-thaw cycles. The resulting PVA-C samples were characterized acoustically (over a range 2.65 to 10.5 MHz) and mechanically to determine the optimum mixture and preparation for mimicking the properties of healthy and diseased arteries found in vivo. This optimum mix was reached with the PVA-C with antibacterial agent sample, prepared after two freeze/thaw cycles, which achieved a speed of sound of 1538 ± 5 m s(-1) and a Young's elastic modulus of 79 ± 11 kPa. This material was used to make a range of anatomically realistic flow phantoms with varying degrees of stenoses, and subsequent flow experiments revealed that higher degrees of stenoses and higher velocities could be achieved without phantom rupturing compared with a phantom containing conventional wall-less vessels.
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http://dx.doi.org/10.1016/j.ultrasmedbio.2011.02.012 | DOI Listing |
PLoS One
December 2024
Department of General Surgery, Cancer center, Division of Hepatobiliary and Pancreatic Surgery, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang Province, China.
Complex liver cancer is often difficult to expose or dissect, and the surgery is often challenging. 3D-printed models may realistically present 3D anatomical structure, which has certain value in planning and training of liver surgery. However, the existing 3D-printed models are all monolithic models, which are difficult to reuse and limited in clinical application.
View Article and Find Full Text PDFiScience
December 2024
Institute of Biotechnology, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland.
Advancements in noninvasive surface and internal imaging techniques, along with computational methods, have revolutionized 3D visualization of organismal morphology-enhancing research, medical anatomical analysis, and facilitating the preservation and digital archiving of scientific specimens. We introduce the SmARTR pipeline (Small Animal Realistic Three-dimensional Rendering), a comprehensive workflow integrating wet lab procedures, 3D data acquisition, and processing to produce photorealistic scientific data through 3D cinematic rendering. This versatile pipeline supports multiscale visualizations-from tissue-level to whole-organism details across diverse living organisms-and is adaptable to various imaging sources.
View Article and Find Full Text PDFJ Endourol
December 2024
Department of Urology, University of California San Francisco, San Francisco, California, USA.
To develop and validate a high-fidelity, nonbiohazardous simulator model for the ultrasound-guided percutaneous nephrolithotomy procedure. We employed a systematic framework based on Delphi consensus and modern education theory to design a simulation model. Twelve expert surgeons provided input through a hierarchal task analysis and identified procedural tasks, anatomical landmarks, and potential errors.
View Article and Find Full Text PDFMed Phys
December 2024
Heidelberg Institute for Radiation Oncology (HIRO) and National Center for Radiation Research in oncology (NCRO), Heidelberg, Germany.
Background: Carbon-ion radiotherapy provides steep dose gradients that allow the simultaneous application of high tumor doses as well as the sparing of healthy tissue and radio-sensitive organs. However, even small anatomical changes may have a severe impact on the dose distribution because of the finite range of ion beams.
Purpose: An in-vivo monitoring method based on secondary-ion emission could potentially provide feedback about the patient anatomy and thus the treatment quality.
Magnetic resonance imaging (MRI) is commonly used in healthcare for its ability to generate diverse tissue contrasts without ionizing radiation. However, this flexibility complicates downstream analysis, as computational tools are often tailored to specific MRI types and lack generalizability across the full spectrum of scans used in healthcare. Here, we introduce a versatile framework for the development and validation of pan-contrast AI models that can exhaustively cater to the full spectrum of scans achievable with MRI, enabling model deployment across scanner models, scan types, and age groups.
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