Full-stack automation for reliable and reproducible MRS of brain cancer

PROJECT SUMMARY/ABSTRACT This project proposes to develop methods for automated, real-time, single-voxel magnetic resonance spectroscopy (MRS) in brain tumors, integrate these methods with a clinical MRI system, evaluate their performance, and make them available as open-source tools to the research community. MRS can provide metabolic information noninvasively for assessment of tumor phenotype and therapeutic response. Single- voxel MRS methods provide the best quality and most reliable data, but require the scanner operator to have a high skill level and expertise to produce good quality results. The need for this expert involvement in both acquisition and analysis remains a critical barrier to the translation of MRS methods to clinical research sites without spectroscopy experts and to clinical practice. The first part of this project is to develop a method for 3D voxel placement using image guidance, integrate this method with a clinical MR system, and evaluate its performance. In the second part, we will automate our advanced MRS methods. In the third part, we will create real-time, automatic quantification tool specific to the obtained MRS data that will provide clinically interpretable results. In the final part, we will assess the performance of automated methods in prospective in vivo study. Successful completion of this project will improve data robustness and quality, eliminating the need for the expert interaction at the time of the scan and enabling adoption of MRS in multi-site clinical trials and clinical practice.