Simscale Heart Valve Cfd — Biomedical Instrumentation & Clinical Sensing

Abstract — Simscale Heart Valve Cfd

dependent, and the identification of faces for boundary conditions often relies on custom scripts. When dealing with many medical images, both manual segmentation and custom scripting can quickly become expensive and time consuming. Figure 1: Diagnostic Medical Waveform & Bio-Potential Clinical Trace for Simscale Heart Valve Cfd Besides research and product development, streamlining the medical-image-to-CFD pipeline can also support the regulatory application process. In 2023, the FDA issued guidance for medical device submissions that have a computational modeling and simulation component , based on the American Society of Mechanical Engineers (ASME) V&V 40. This guidance applies to medical devices that use CFD, providing a risk-informed framework for defining and assessing model credibility through verification, validation, and uncertainty quantification (VVUQ). Uncertainty quantification (UQ) looks to define the uncertainties inherent to the computational model by varying input and solution parameters. To understand the latter, sensitivity analyses are needed from medical image acquisition and segmentation to meshing and boundary condition application to numerical solving and outcomes that can influence decisions related to the clinical question of interest. A streamlined pipeline enables these sensitivity analyses to be performed systematically and at scale.

Sensor Interfacing & Analog Front-End Conditioning

Fortunately, there are open source tools and powerful machine learning techniques that can help accelerate this process. In this article, we will highlight some of these tools and methods – all resources that you can start using today. Additionally, by working with Kitware you can further leverage our expertise in putting together customized segmentation pipelines to reduce costs and accelerate your development.

Bio-Signal Waveform Analysis & Simulation Findings

There are many open source tools for processing medical images for CFD input, but there are rarely clear all-in-one solutions. Instead, practitioners typically assemble a workflow by combining tools that each excel at different stages. Figure 2: Diagnostic Medical Waveform & Bio-Potential Clinical Trace for Simscale Heart Valve Cfd 3D Slicer is an open-source platform for medical image analysis. It is powerful, flexible, and highly customizable. Slicer provides extensive tools for medical image segmentation as well as a rich ecosystem for processing and cleaning segmentations. A common workflow is to use Slicer for segmentation and surface mesh generation, and then move to other tools for volumetric mesh generation, boundary condition specification, assignment of fluid properties, and other simulation setup. There are also options to generate volumetric meshes within Slicer itself, such as using the SegmentMesher extension or the Vascular Modeling Toolkit (VMTK).

Scope & Clinical Applications

• Real-time multi-parameter vital signs telemetry (ECG, Heart Rate, SpO2, Temperature, Respiration)
• Low-noise Analog Front-End (AFE) conditioning with active Driven Right Leg (DRL) 50 Hz cancellation
• Digital signal processing in MATLAB: Pan-Tompkins QRS detection, FIR/IIR notch, and Continuous Wavelet Transforms
• Finite element tissue bio-electromagnetics and orthopedic mechanical simulations in COMSOL & ANSYS
• Complete Arduino firmware .INO sketches, circuit schematics, and university dissertation reports