An article was recently published in which CardioSolv’s CTO Gernot Plank worked with partners to scale simulations with our CARP simulator to 16000 cores. This is an important step toward simulating the human heart in real time, eventually for the clinical setting. You can read more about it here (Frontiers in Computational Physiology and Medicine).
I wish I had this when I was working on my PhD. Check it out!
Most of the members of CardioSolv will be attending this year’s Heart Rhythm Society Scientific Sessions in San Francisco, May 4-7, 2011. If you would like to discuss heart simulation with us, this would be an excellent time to do so. We will have laptops on hand ready to demonstrate running and analyzing simulations, and we will be hosting a get-together on Friday night for CardioSolv users. This would be a great opportunity to meet existing users of our software and find out how well it is working for them.
If you would like to meet up with us and/or attend this get-together, please contact us at firstname.lastname@example.org, or +1 (888) 525-2232. We hope to see you there!
Some time ago I wrote a brand-new, MPI-based, parallel data post-processing tool for analyzing the output of our CARP simulator. I have now uploaded it to github here. It is modular in design (as the name implies) and it is easy to build custom tools with the modules.
There are a couple of things I really love about this library and tool. First, it is written so that parellelizing the work is easy and relatively transparent. Second, because it is parallel, it allows the analysis of really large IGB (CARP output) files in a short period of time, given appropriate hardware such as a cluster.
If you have any questions about using it please don’t hesitate to ask.
CardioSolv is now offering trial licenses of our CARP simulator. If you would like a trial license, please sign up here.
CardioSolv was recently awarded a Phase I Small Business Initiative for Research (SBIR) grant by the National Science Foundation (NSF) to help us develop our technologies into forms more readily usable by the pharmaceutical and device industries.
Here’s the project summary:
This Small Business Innovation Research Phase I project will explore the development and commercial feasibility of a user-friendly cross-platform computing system for multi-scale tissue and organ cardiac electrophysiology and electromechanics modeling. This system will enable the discovery and development of new approaches to the diagnosis and treatment of cardiac disease and allow virtual exploration of mechanisms of cardiac rhythm disorder and electromechanical dysfunction, from the protein to the entire organ. The proposed system will include capabilities for direct input of cardiac imaging data, including patient MR and CT scans, and for the automatic generation of electrophysiological and mechanical computational meshes of the heart. Users will be able to tailor the behavior of individual components of the system to represent specific cardiac pathologies, targets, and interventions. Simulations will be managed with ease, and a robust cross-platform user-friendly interface will allow effortless visualization of results. Specific Technical Objectives include: 1) Assessing the technical feasibility of assembling an automated pipeline; 2) Assessing the technical feasibility of developing a cross-platform GUI that integrates cardiac electromechanical model assembly, simulation, and analysis; and 3) Testing and refining the prototype system to meet customer needs and utilizing user input to assess the commercial feasibility of the system.
The proposed system represents an enormous paradigm shift in the way cardiac electromechanical simulation is done. It will not only integrate, in one easy-to-use system, cardiac electrical and mechanical function using the most sophisticated cardiac simulation tools ever developed, but intends to make simulation accessible to a very broad aspect of society. Currently, cardiac modeling is used in the exploration of new approaches to the diagnosis and treatment of cardiac disease only in a few academic laboratories. However, cardiac device manufacturing, biotech, and pharmaceutical industries have a significant interest in cardiac tissue and organ modeling. For device companies, it presents an opportunity to develop and test prototype devices and treatment modalities. For pharmaceutical companies, it offers an unrivaled opportunity to quickly screen drugs for pro-arrhythmic effects. It also provides benefits to academic researchers since sophisticated state-of-the-art simulation tools will open new research horizons, particularly translational research projects in personalized medicine. In the long term, CardioSolv’s system is expected to bring cardiac modeling to the patient bedside by becoming a physician’s reference tool for patient-specific diagnostics and optimization of cardiac therapy. Finally, the proposed system is expected to become an effective teaching tool, and part of biomedical and clinical curricula.
Keywords: modeling of cardiac electrophysiology, modeling of cardiac mechanics, virtual research environment, cloud computing, cardiac electromechanical dysfunction, cardiac therapy, personalized medicine, cardiac devices, drug screening, virtual teaching tool
CardioSolv is looking for input from clinical electrophysiologists on a new project that we’re pursuing. If you’d like to get in on the ground level of development for this exciting project, please contact us!.
We hope to be able to disclose more about this project in a few weeks.
If you work in, or are otherwise interested in cardiac simulation, what meetings do you attend? Heart Rhythm? AHA? Cardiostim? BMES? Would you like to see CardioSolv there? If so, what would you like to see from us?
Of course, you can always reach us by phone or email, but it’s nice to be able to discuss things in person. Let us know what you’re looking for and where!
Generation of heart (or other tissue) models from medical images requires several steps. In particular, masking and cleanup, segmentation, and meshing. Masking and cleanup of image stacks is easily done with something like ImageJ. Be warned — if you are using large images, it will require a lot of memory. Once some basic cleanup of the images, cropping, and masking has been done, it’s necessary to segment the interesting things from the non-interesting things, and to separate out various regions of interest. For example, when segmenting an MRI scan of a heart, first the MRI chamber (if included in the images) is masked out. Then unnecessary bits of the surrounding bath are cropped out. Adjustment of the brightness and contrast levels of the images might be necessary. Once that’s done, one wants to separate the heart from the bath, and maybe infarcted tissue from healthy tissue.
I spent quite some time trying to figure out a practical way to do this segmentation for my last project as a graduate student. I settled on using Seg3D from SCI. Seg3D has a great 4-panel 3D interface (3 views + 3D rendering), and a number of built-in segmentation tools. In fact, when I looked it it today, it seemed to have had even more added to it than it had a year or two ago. I’ll write something about those old and new features later.
For now, if you want to get Seg3D up and running on a Windows or Mac machine, you’re set — just go download the appropriate package from the Seg3D download page. If you’re running Linux, however, you’ll have to compile it yourself. That’s probably for the best anyway — it’ll help ensure that it runs as efficiently as possible on your machine.
The instructions on the Seg3D site are pretty good, but I’ll add one thing that tripped me up today. If you’re using the proprietary nVidia drivers in Ubuntu, and you’re using Ubuntu’s distribution of them (if you clicked a menu to switch to the drivers, you probably are), you have to install the
-dev driver package to get the appropriate OpenGL libraries.
But maybe I’m getting ahead of myself. Do you have an nVidia card? If you don’t know, try:
lspci | grep -i nvidia
If you get something like this, you’re all set:
01:00.0 VGA compatible controller: nVidia Corporation G96 [GeForce 9500 GT] (rev a1)
If you get nothing, you don’t have an nVidia card. To find out if you already have the drivers installed, try:
dpkg --get-selections | grep -i nvidia
If you see something like
nvidia-glx-185, you’ve got the driver installed, and if you see something like
nvidia-glx-185-dev, then you’re good to go. You can carry on from the Seg3D compilation instructions. If you see the first, but not the second, given the highest number from the first (in this case 185), do:
sudo apt-get install nvidia-glx-185-dev
Hopefully it’ll install, and then you’re all set for drivers. You can carry on and install using the Seg3D instructions.
I’ll have some posts later about using Seg3D to turn your images into a segmented stack, ready for meshing with Tarantula.
Implantable cardioverter-defibrillators are the best solution to a number of electrical problems with the heart, and result in a measurable improvement in quality and length of life for those that need them. However, a recent study entitled Evaluation of Early Complications Related to De Novo Cardioverter Defibrillator Implantation in the Journal of the American College of Cardiology found that (as summarized here), “4.1 per cent of patients experienced major complications … within 45 days of device implant and they had more than three times the risk of dying within the next 6 months.” It was also noted that the likelihood of complications in women was higher.
The study also found that more complicated devices were a strong predictor of complications. It was noted in the study that patients with more complex and severe problems typically require the more complex devices, making it difficult to ascertain whether the patients’ health or the devices were at fault. I can understand the motivation of the authors in choosing the wording of their conclusion, but I wonder if maybe it sounds a bit too certain without the caveats written in the article. They concluded, “Complications after de novo defibrillator implantation were strongly associated with device type. Major complications were associated with increased risk of mortality.”