Abstract #1933

Glioma Glycolysis, and Informed PET Interpretation: Metabolic Activity Diffusion Imaging (MADI) Tissue Cell Density

Joshua Schlegel¹, Eric Baker², Samantha Holland³, Jared Stoller³, William Packwood⁴, Xin Li², Ramon J. Barajas, Jr.⁵, Charles S. Springer, Jr.², and Martin M. Pike²

¹Medical Physics, Oregon Health & Science University, Portland, OR, United States, ²Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, United States, ³Neurology, Oregon Health & Science University, Portland, OR, United States, ⁴Research Cores and Shared Resources, Oregon Health & Science University, Portland, OR, United States, ⁵Diagnostic Radiology, Oregon Health & Science University, Portland, OR, United States

Synopsis

Keywords: Biomarkers, Molecular Imaging, Metabolism

Motivation: Metabolic imaging generally employs minimally invasive radio-tracers (PET) or nuclear magnetic‑tracers (MRSI). Besides sub-optimal spatial resolution, these also suffer because tracer-bearing molecule compartmentalization is not present in the imaging signal.

Goal(s): We wish to address these limitations.

Approach: We compared completely non-invasive, relatively high-resolution MADI with routine ¹⁸FDG-PET in a rat brain glioma model.

Results: The MADI k_ioV parameter outperforms PET SUV_max in discriminating these tumors and, with PET SUV_max, reveals a more glycolytic metabolism. Furthermore, the SUV_max biomarker is de-confounded with the independent MADI‑estimated tissue cell (number) density (ρ) factor. In this model, SUV_max is ρ-dominated.

Impact: MADI is compatible with any MRI instrument, and shows promise for improved tumor detection, improved metabolic insights, and informing interpretation of other modality biomarkers. MADI mapping investigations can lead to deeper insights into in vivo pathology metabolism.

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