Visualising the future of medicine
Modelling a physiology is no mean feat, but doing so has the potential to change the way we look at healthcare. We explore the advances being made
Chris Johnson, director, Scientific Computing and Imaging Institute, and Distinguished Professor, School of Computing at the University of Utah
I
direct the Scientific Computing and Imaging Institute at the University of Utah. We’re an inter-disciplinary research institute of around
200 faculty staff and students, and we specialise in visualisation research, image analysis and scientific computing. I also direct the NIH/ NCRR Center for Integrative Biomedical Computing, which is in its 14th year and focuses on image-based modelling, simulation and visualisation. Te work we do is essentially a piece of the
personalised medicine pie – creating biological- subject-specific-based models from images, such as from MRIs, X-Rays and CT scans. Tose images are then segmented in order to obtain the geometry of the patient or parts we’re focusing on and used to create patient-specific computer-generated models. From the models, we perform functional simulations such as simulating the electrical activity in the heart,
localising epileptic seizures within the brain and calculating stresses or strain on artificial joint transplants. Te results of the simulations and models are then visualised and ultimately taken to clinics to be used for diagnosis and treatment. Behind the scenes we write all the soſtware
involved in this process and it’s taken us at least 10 years of hard work to get to the point where we’re actually able to do the whole process of image-based modelling, simulation and visualisation for real-world clinical application. Currently, we’re working with a cardiac surgeon on the simulation and visualisation of atrial fibrillation for surgical planning, working with neuro-scientists and neurologists for localising the source of epileptic seizures, and designing internal, implantable defibrillation electrodes and optimising their efficacy. Te soſtware we’ve created is a set of tools that take you through that entire process. Each
one is open source, available to download from the website and runs on multiple platforms. Te first piece of soſtware we use to do the segmentation of the images is called Seg3D. To do a simulation of an epileptic seizure, for example, we would first take an MRI of the head. Te Seg3D soſtware has automatic algorithms that locate the boundaries of the skull and cortex of brain and the pulls that information out from each slice of the image, which provides us with sets of surfaces. Tis can also be done in three dimensions if 3D images are used. To run the simulation we need volumes and so the next piece of soſtware we use is BioMesh3D, which takes those images and creates full 3D geometric meshes – the digital geometry necessary for the simulation. Our next set of soſtware, SCIRun, does the finite element or boundary element simulation required in electric source modelling. Our final piece of soſtware, ImageVis3D, provides visualisation of the simulation. To create soſtware that’s useable is a
Simulation of the electric field generated by an implanted cardiac defibrillator in a patient-specific computer model
42 SCIENTIFIC COMPUTING WORLD
The mesh near the apex of the heart showing solved bidomain simulation of epicardial potentials during acute ischemia
multi-year process. I think a lot of people underestimate how difficult and challenging it is to do, and we have a number of giſted and talented soſtware engineers who work incredibly hard on achieving just that. Te challenges along the way are many, especially because when doing biomedical modelling and simulation we need to recognise that our biology is incredibly complicated. We can never truly address all that complexity and so need to simplify things in some ways and decide on the level of complexity we do want to address.
www.scientific-computing.com
SCI Institute
SCI Institute
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