How Neural Networks Are Changing Medical Imaging – With Reinhard Heckel

Clear imaging is essential for accurate diagnosis — but when it comes to the heart, motion makes it one of the most difficult organs to capture in high resolution. Traditional reconstruction methods often struggle to deliver the detail clinicians need.

Reinhard Heckel, Professor of Machine Learning at TUM and MCML PI, is using deep neural networks to change that. His team is developing methods that reconstruct sharper, more detailed images from limited or noisy measurements — even when the subject is moving.

A key focus of his research is data: how to source it, how to ensure it’s diverse, and how to train robust models that generalize across patient populations.

This work is paving the way for more accurate imaging tools that can help detect pathologies that might otherwise go unnoticed.

This video is part of our MCML spotlight series on researchers driving real-world impact with AI.

The film was produced and edited by Nicole Huminski and Nikolai Huber.