Peter Bosman (CWI) awarded € 1.4 million for R&D on medical image registration methods
Together with the radiation oncology department of AMC, Peter Bosman of CWI’s Life Sciences and Health group has been awarded 1.4 million euros for a project in NWO’s Open Technology Programme for the research and development of a new medical image registration method. This project is co-funded by companies Elekta and Xomnia.
Combining multiple medical images from one patient can provide important information. This is not always easy to do with the naked eye. This is why we need software that can compare different medical images. To do so, so-called image registration methods are used, which basically compute which point in one image corresponds to which point in another image. Current solutions are often not always suitable for use in a medical setting, which is why AMC and CWI together with companies Elekta and Xomnia will develop a new image registration method.
Suppose you have multiple CT and/or MRI images of a patient, made at different points in time. Medical staff wants to compare these images, for example to see how certain irregularities have developed over time. But these images are often fundamentally different (e.g., patients never lie in a scanner in the exact same manner) and when different imaging methods are used this is even more complicated. So how can one determine precisely what has changed?
With the software that is currently available this can be very hard, or even impossible, to accomplish in practice. “Existing methods have many parameters. In particular, there are so-called weights that determine how important different quality measures of a registration outcome are. Research has shown that these weights can be quite different for individual cases, especially for difficult situations where a lot of deformation (or other changes) have occurred. We have previously pioneered a different view on registration, based on multi-objective optimization, which removes this problem and holds potential to provide medical experts with an intuitive way of obtaining the desired result.”, says Peter Bosman of CWI.
An example of a difficult situation where large differences in the images can arise is patients that suffer from breast cancer. The standard treatment for these patients is surgery, followed by irradiation of the cancer cells that are left behind. For this, the entire breast is often targeted. Research has shown however, that it is better to irradiate the area in which the tumor was present using an extra “boost”. However, that exact area is hard to point out for the radiation oncologist after surgery. “It would be very useful to be able to compare the images before and after surgery to more accurately outline that area” says Tanja Alderliesten of AMC. Current methods are not capable of doing such a comparison sufficiently precise under these circumstances. With the help of the right software, doctors can find out which changes have occurred and how that happened.
This project has 2 major challenges. The models and algorithms for large deviations have to be improved. Next to that, the software has to be designed so that it is intuitive to use and helps medical practitioners get the results they want. By combining new deformable image registration models and algorithms with machine learning, the software can be trained on example cases to work even better. The focus of the project will be on supporting better radiotherapy treatment, with validations in the real world (i.e., the clinic), but the method will also be applicable to other (medical) areas.