Transformative Pediatric Brain Cancer Imaging using Integrated Biophysics-AI Molecular MRI

Introduction

Despite vast drug development efforts, brain tumors remain the leading cause of pediatric cancer deaths. Noninvasive monitoring of treatment response is crucial to reveal the mechanisms behind tumor-drug interactions and optimize patient care.

Challenges with Standard MRI Methods

However, standard magnetic resonance imaging (MRI) methods involve:

• Injecting metals
• Severe difficulties in differentiating treatment response from tumor progression
• Being qualitative
• Mandating prolonged anesthesia due to lengthy acquisition

Proposed Solution

I propose to develop a transformative molecular MRI technology based on the chemical exchange saturation transfer (CEST) contrast mechanism. This technology will enable:

• Specific
• Quantitative
• Rapid
• Contrast-material free treatment monitoring of pediatric brain cancer

Recent Discoveries

Recently, I revealed that a combination of mathematical CEST models and AI can generate quantitative biomarker maps of:

1. pH changes
2. Protein concentration changes across the brain

These are two known hallmarks of cancer. Inspired by these results, I now propose to adopt a previously unconsidered perspective and represent the underlying physics of CEST MRI as a computational graph. This will enable an automatic AI-based optimization of molecular imaging.

Hypothesis

I hypothesize that the combination of biophysical models with a new AI framework, and their synergetic integration throughout the entire imaging pipeline, will provide accurate noninvasive treatment monitoring.

Research Plan

1. Automated Optimization: First, I will establish a method for automated optimization of MRI protocols for early determination of the tumor response to mainstream chemotherapy.
2. Shortening Scan Time: Next, I will shorten the 3D scan time by an order of magnitude and quantify the response to next-generation immunotherapy.
3. Clinical Translation: Third, I will translate the method to clinical scanners and validate it in a human pediatric pilot study.

Expected Outcomes

This research will yield a fundamental understanding of the molecular mechanisms underlying treatment response and establish an innovative precision medicine methodology that will transform pediatric cancer imaging.