Magnetic Sweet AI: Metabolic Brain Cancer Imaging using Deep MRI of a Sugar-Based Contrast Agent

Despite extensive research efforts, brain tumors remain a leading cause of cancer-related death, with only one-third of individuals surviving more than 5 years after diagnosis. Early detection constitutes a decisive factor in disease prognosis. While various medical imaging modalities can provide an anatomical view of the brain, they only capture morphological tissue changes that occur relatively late, when the tumor is already well-defined and mature.

In contrast, alterations in metabolic properties, such as increased glucose consumption, are manifested as early as cell reprogramming and constitute a unique tumor signature. As currently employed in-vivo metabolic imaging techniques require the use of ionizing radiation and have limited spatial resolution, there is an urgent need for an alternative accurate, and safe means for early detection and characterization of brain tumors.

Recently we discovered a new non-toxic sugar-based material, which is preferentially accumulated in tumor cells and can be detected using the chemical exchange saturation transfer magnetic resonance imaging (CEST-MRI) technique. However, the complex molecular brain environment generates a variety of confounding magnetic signals stemming from various metabolites, lipids, and peptides, which severely hinder the quantification of metabolic sugar consumption.

The main goal of this project is to develop a transformative radiation-free, and rapid AI-based metabolic MR technology for early brain tumor detection. We propose to adopt a previously unconsidered perspective and to represent the underlying physics of molecular MRI as a computational graph, enabling an automatic AI-based optimization of tumor metabolic imaging.