Published on 29.10.2023
Six new Helmholtz Imaging projects Funded
Helmholtz Imaging annually invites proposals for interdisciplinary, cross-center projects designed to foster collaborative research and explore new and innovative topics in imaging and data science. The range of ongoing projects is broad, tackling challenges from predicting space weather to developing clinical diagnostic methods for neurodegenerative diseases.
The six newly funded projects will launch throughout the coming year, with initial outcomes anticipated by the end of 2024. These projects encompass a wide range of innovative topics, including advanced methods for early forest stress detection, AI-driven precision monitoring for lung cancer treatment, the creation of accessible AI tools for hyperspectral Earth observation.
With these additions, since the initiative’s inception, a total of 30 interdisciplinary projects have engaged researchers from all 18 Helmholtz Centers and across the full spectrum of Helmholtz research fields.
Here is an overview of the new projects:
- 3DforestSIF (FZJ, DLR): 3DforestSIF seeks to correct airborne solar-induced fluorescence (SIF) data from forests for canopy structural and illumination effects, providing valuable insights for the early detection of forest stress.
- CLARITY (DKFZ, HMGU): Dose-escalated radiotherapy of lung cancers requires precise monitoring of lesions and nearby organs at risk. Current methods are able to track ultra-central lesions but neglect their deforming vicinity, risking unacceptable toxicity to aortico-pulmonary structures. AI-based anomaly detection and generative AI models can address both requirements in real-time.
- X-BRAIN (FZJ, HMGU): X-BRAIN aims to develop AI methods that support the integration of multimodal imaging data into human brain atlases, thereby advancing the analysis of brain structure in both health and disease.
- HYPER-AMPLIFAI (DLR, GFZ): HYPER-AMPLIFAI aims to make advanced AI models accessible for Hyperspectral Earth Observation, reducing computational demands, and improving environmental assessments through user-friendly interfaces.
- BrainShapes (FZJ, HMGU): BrainShapes explores the 3D structure of the human brain by creating a digital ‘map’ of the brain and examining its unique genetic properties, potentially linking genetic variations to brain disorders.
- FAST-EMI (FZJ, KIT): FAST-EMI aims at establishing a novel imaging approach combining electron microscopy and deep learning. This method is to enable adaptive tracking of atomic defects, accelerating material development for the energy transition.