MELoDee – multi-exponential model learning based on deep neural networks

Researcher:
Dr. Mordechay (Moti) Freiman | Biomedical Engineering

Categories:

Information and Computer Science | Medical Devices

The Technology

The present technology offers a machine-learning solution to the problem of fitting multi-exponential models to observed data. The multi-exponential fitting problem appears in various science and engineering applications, such as nuclear magnetic resonance spectroscopy, lattice quantum chromodynamics, pharmaceutics and chemical engineering, fluorescence imaging, infra-red imaging, economic model prediction, medical imaging, and more.
MELoDee (Multi-Exponential model Learning based on Deep neural networks), is an innovative deep-learning-based solver with improved generalization capabilities compared to previous solutions. The MELoDee consists of a new neural network architecture as well as a novel training protocol, aimed at producing a multi-exponential fitting solution that is more accurate, precise, and robust to uncertainties in the training conditions. The main innovations of this method are (a) a novel architecture, as well as (b) a new training protocol. These two features enable the network to generalize the multi-exponential model better than existing approaches, and thus yield more accurate and robust model parameter estimations.

Advantages

  • Fast, accurate and precise parameter estimation
  • Robustness to uncertainties

Applications and Opportunities

  • In-vivo non-invasive tissue characterization with quantitative MRI
  • Cardiac inflammation mapping
  • Brain microstructure assessment for neurodegenerative diseases
  • Cancer imaging
  • Accelerated MRI reconstruction
  • Numerical software for signal analysis
arrow Business Development Contacts
Ofer Shneyour
Director of Business Development, ICT