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