Deep-Learning MR-only Radiation Therapy - an innovation launched on the market to improve patient care

The Challenge: Precision vs. Practicality in Cancer Treatment

In radiation therapy, the battle against cancer is fought on a millimeter scale. The standard for planning this treatment has long been the CT scan, which provides a detailed map of the patient’s anatomy. However, CT scans have two significant drawbacks: they use ionizing radiation, meaning repeated scans add to the patient’s radiation dose, and they offer limited soft-tissue contrast, making it difficult to precisely distinguish a tumor from surrounding healthy tissue.

Healthcare professionals faced a dilemma: how to get the most accurate, repeatable images for planning treatment without exposing patients to unnecessary radiation? The answer lay in a different technology: Magnetic Resonance Imaging (MRI). MRI offers vastly superior soft-tissue visualization and uses no ionizing radiation, making it ideal for repeated use. The challenge was turning this potential into a viable, marketable clinical solution.

My Role: Forging a Path from Clinical Need to Market Innovation

While I was working at GE HealthCare as a Consortium Research Leader, my primary focus was to identify groundbreaking innovation and collaboration opportunities. Through deep engagement with healthcare professionals, this exact topic—using MRI for radiation planning—was consistently raised as a critical unmet need.

Recognizing the transformative impact this could have, I took the initiative to build a framework for success. This wasn’t just an internal project; it was a complex challenge that required a fusion of academic insight and industrial power.

My role was to architect and drive this collaboration. I initiated and organized a powerful consortium of partners across Europe, bringing together the commercial and R&D strength of GE HealthCare with the clinical and research excellence of institutions like the Erasmus Medical Center and others.

As the leader of this consortium, I was responsible for:

• Strategic Alignment: Ensuring all partners shared a unified vision for developing an innovative, market-ready approach.
• Driving Collaboration: Managing the complex relationships and workflows between academic and industrial teams, translating clinical needs into technical specifications and vice versa.
• Securing Funding: Building the business case and operational plan to secure the necessary resources to fuel this ambitious, multi-year development.

The Solution: A Deep-Learning, MR-Only Workflow

The consortium’s work culminated in a true innovation: a deep-learning-powered, MR-only radiation therapy workflow. This approach leverages the strengths of AI to generate “synthetic CT” images from the MRI data, providing the necessary density information for radiation dose calculation.

The result is a single-scan workflow. A patient comes in for one MRI, and from that single dataset, clinicians get both the superior soft-tissue visualization for contouring the tumor and the precise density map for planning the treatment.

The Impact: A New Standard in Patient-Centric Care

This project successfully transitioned from a research concept to an innovation launched on the market, directly improving patient care. Patients benefit from a streamlined process with less time spent in scanners and, most importantly, the elimination of radiation exposure from the planning process. Clinicians benefit from clearer, more detailed images, enabling more confident and accurate treatment plans.

This project is a prime example of how strategic, cross-sector partnerships can solve complex healthcare challenges. It demonstrates the power of identifying a core clinical need and building the right ecosystem of talent, technology, and resources to deliver a solution that matters.

If your organization is looking to navigate the path from a bold idea to a tangible, market-leading innovation, I have the experience to build the consortium, secure the funding, and lead the collaboration to make it a reality.