This technology is a neural network architecture that quantifies in vivo metabolites from magnetic resonance spectroscopy for improved disease detection and diagnosis.
Magnetic resonance spectroscopy (MRS) enables non-invasive quantification of tissue metabolites from magnetic resonance imaging (MRI) scans. When integrated through advanced clinical imaging platforms, these modalities enable the identification of disease-associated metabolites. Thus, this technique has been increasingly used to diagnose cancers, neurodegenerative disorders, and infectious diseases. However, current automated methods for metabolite detection and quantification often suffer from limited accuracy due to poor image quality. Further, manual analysis is time-consuming and impractical for routine clinical use.
This technology describes a deep learning algorithm capable of automatically quantifying metabolites from magnetic resonance spectroscopy (MRS) data. The algorithm can be efficiently trained using either in vivo or simulated datasets, enabling rapid, high-throughput, and accurate analysis. It integrates with existing clinical imaging platforms and has the potential to enhance both diagnostic and prognostic capabilities. The algorithm eliminates reliance on time-consuming and error-prone human data interpretation and outperforms existing methods by accurately analyzing low-quality or noisy MRS data.
This technology has been validated with human MRI data.
Patent Pending (US20220051801)
IR CU24256, CU24266
Licensing Contact: Jerry Kokoshka