![]() ![]() ![]() ![]() Obviously, there are more tissue types present, but we want to create a simple model that will fit into our software tool’s capabilities, so we choose to lump some tissue types together. Here, we want to segment the data into the most important tissue types: muscle, blood, skin, bone, bone marrow, and fat. The process of replacing the greyscale values with tissue types or organs is called segmentation. Since we are going to make a model with only a few tissue types, we need to reduce the amount of information in the MRI dataset. This makes it possible to use a number of software products to examine the data. The dataset is stored in a container in a format called DICOM. ![]() The greyscale value of each voxel is saying something about the amount of hydrogen in the tissue the brighter the voxel is, the more hydrogen there is in the volume the voxel represents. When these 2D pictures are put together, they form a 3D picture where each pixel in a 2D picture is forming a voxel (a volume pixel). The MRI dataset may be seen as a series of greyscale pictures of slices of the thigh. We have made the MRI dataset available for download. The basis of the model we are going to make is a MRI scan of a human thigh. The Model creation and experimenting are done in Multiphysics (blue).ĭetailed description of the tutorial steps The input data is in DICOM format, and is processed in ScanIP+FE (green). It is possible to skip the first parts since deliverables from previous stages are available for download. Specifically, this tutorial will take the reader trough the steps involved in making a model and do measurement simulations on it using software tools from Simpleware and COMSOL.Īn overview of the tutorial steps are shown in figure 1. The goal is to learn how to model an experiment in order to see what we really measure, and to gain insight in volume impedance measurements in general. This tutorial will show how to create such a model and how to perform a virtual experiment on it. This solution is becoming more interesting as computers and software becomes increasingly capable. One solution to this problem is to create a model where we can see and even quantify the contribution from tissues and organs. And even if we find a set-up that we think is good, we still do not know how much each tissue or organ has contributed to the final measurement result. However, it is obviously time-consuming for both the persons who do the tests and for the test-subjects, and there may be medical reasons to avoid such tests if not absolutely necessary. One way to solve this problem is to test a number of set-ups on a number of test-subjects with and without the given condition. For example, if we are to use bioimpedance measurements to diagnose a condition, we need to be sure that our set-up actually detects the given condition. This means that electrical bioimpedance measurements, in general, has good sensitivity but poor specificity.Īs a result, it may be a challenge to create measurement set-ups of clinical value. Unfortunately, it is difficult to establish a link between measured impedance change and a change in one or more of the input variables. Electrical impedance in tissues is a function of variables such as ion concentrations, cell geometry, extracellular fluids, intracellular fluids, and organ geometry, so it is easy to imagine that sensitivity to different changes in these variables may be detectable. These are all desirable properties in a clinical setting. It requires little equipment, can be done with small and simple units, and due to the simplicity of the equipment, it may be made low-cost. Measurement of electrical bioimpedance is relatively easy to do. ![]()
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