Abstract #3435

Improved Bloch fitting and machine learning methods that analyze acidoCEST MRI

Tianzhe Li^1,2, Julio Cardenas-Rodriguez³, and Marty David Pagel⁴

¹Cancer Systems Imaging, UT MD Anderson Cancer Center, Houston, TX, United States, ²Medical Physics Program, UT Health, Houston, TX, United States, ³Data Translators LLC, Oro Valley, AZ, United States, ⁴Cancer Systems Imaging, MD Anderson Cancer Center, Houston, TX, United States

Synopsis

Keywords: Data Analysis, CEST & MT, pH imagingAcidoCEST MRI can measure the extracellular pH of the tumor microenvironment. We have further refined our “Bloch fitting” method, and shown that this analysis method can accurately and precisely measure pH without additional MRI information, with an accuracy of 0.03 pH units. In addition, we have developed a machine learning method that can classify pH as > 7.0 or < 6.5 pH units (PPV=0.94, NPV=096), and a machine learning regression method that can estimate pH with a mean absolute error of 0.031 pH units.

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