The Philips AI4MRI lab will research the use of Artificial Intelligence (AI) to optimize Magnetic Resonance Imaging (MRI) in terms of speed, image quality, robustness, and integration, in collaboration with Philips, Bontius Foundation, and LUMC.
We expect recruitment for this lab to open in early 2023. Please check back frequently for updated information about how to apply, and to register for an online information session in February. If you’d like to join our mailing list, please fill out the form, and we will be happy to keep you informed of all the latest developments.
The healthcare system is confronted with an ever-increasing burden due to the mounting patient demands and limited resources, coupled with suboptimal delivery of clinical care. For instance, in 2018 the Netherlands experienced spending of over €1 billion in cancer treatments and a loss of €3.8 billion in productivity. This has put the long-term sustainability of the healthcare system in jeopardy. Artificial Intelligence (AI) is expected to revolutionize Magnetic Resonance Imaging (MRI) and its applications, enabling sustainable, faster, personalized, more effective, more available, and more cost-effective patient care.
MRI is a widely used medical imaging modality, and a key instrument for disease diagnosis, prognosis, and follow-up. However, it is associated with a number of drawbacks, such as long scanning times, image artifacts caused by patient motion, and the separation of the acquisition process from other imaging modalities and downstream clinical tasks. To increase the range of applications, diagnostic quality, and the outcome-per-costs ratio of MRI, it is therefore of paramount importance to address these issues, and AI is a key enabling technology for this. For example, a team from Philips and LUMC recently demonstrated that the acquisition of anatomical MRI scans can be accelerated by a factor of 4-8x, with promising image quality, by using a deep learning reconstruction method, winning key tracks in the international 2019 fast MRI challenge.
Nevertheless, due to the critical role of MRI in diagnosing severe diseases, the impact of false positives and false negatives is even more detrimental than in other areas of AI. Additionally, the application range of prototype AI reconstruction methods currently does not span the entire scope of clinical application of MRI, particularly that of rapidly moving organs like the heart. Finally, current methods do not sufficiently consider the intricate interplay between scanner hardware, acquisition protocol, and AI methodology. This makes the development of reliable solutions of the utmost importance.
To this end, the newly proposed ICAI lab, the “Philips AI4MRI lab”, will develop trustworthy AI algorithms optimizing the speed, image quality, robustness, and integration of MR imaging in the clinic, in close collaboration between industry, academia, and the clinic.
Sustainable Development Goals
The AI4MRI Lab is part of the ROBUST program on Trustworthy AI-based Systems for Sustainable Growth which is financed under the NWO LTP funding scheme. To accelerate the energy transition and ensure tangible social value, the lab will focus on two specific targets of two Sustainable Development Goals (SDGs) related to railway systems.
AI4MRI contributes to SDG 3. We want to support healthcare professionals with a much faster diagnostic MRI tool in the diagnostic process. By developing smart MR sampling and reconstruction algorithms, we aim to deliver AI-enhanced MRI with the same accuracy as conventional MRI, but at much faster scanning times; This will shorten a patient examination, and as a result a reduced MR turnaround time of an MR examination. The intended change here is industrial (more cost-effective MRI scanners), economic (increased cost-effectiveness and reliability of AI-enhanced MRI), and social (increasing patient comfort with MRI procedures).
SDG 3: Ensure healthy lives and promote well-being for all at all ages
Target 3.d: Strengthen the capacity of all countries, in particular developing countries, for early warning, risk reduction and management of national and global health risks.