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This research paper presents a novel approach to the prediction of hypoxia inbrain tumors, using multi-parametric Magnetic Resonance Imaging (MRI). Hypoxia,a condition characterized by low oxygen levels, is a common feature ofmalignant brain tumors associated with poor prognosis. FluoromisonidazolePositron Emission Tomography (FMISO PET) is a well-established method fordetecting hypoxia in vivo, but it is expensive and not widely available. Ourstudy proposes the use of MRI, a more accessible and cost-effective imagingmodality, to predict FMISO PET signals. We investigate deep learning models(DL) trained on the ACRIN 6684 dataset, a resource that contains paired MRI andFMISO PET images from patients with brain tumors. Our trained modelseffectively learn the complex relationships between the MRI features and thecorresponding FMISO PET signals, thereby enabling the prediction of hypoxiafrom MRI scans alone. The results show a strong correlation between thepredicted and actual FMISO PET signals, with an overall PSNR score above 29.6and a SSIM score greater than 0.94, confirming MRI as a promising option forhypoxia prediction in brain tumors. This approach could significantly improvethe accessibility of hypoxia detection in clinical settings, with the potentialfor more timely and targeted treatments.

Predicting Hypoxia in Brain Tumors from Multiparametric MRI