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.

翻译：本研究提出了一种利用多参数磁共振成像预测脑肿瘤缺氧的新方法。缺氧（以低氧浓度为特征的病理状态）是恶性肿瘤的常见特征，与不良预后密切相关。氟米索硝唑正电子发射断层扫描（FMISO PET）是公认的体内缺氧检测方法，但其成本高昂且普及率有限。本研究提出采用更具可及性和成本效益的磁共振成像（MRI）来预测FMISO PET信号。我们研究了基于ACRIN 6684数据集的深度学习模型（该数据集包含脑肿瘤患者的配对MRI和FMISO PET图像），所训练的模型有效学习了MRI特征与对应FMISO PET信号之间的复杂映射关系，从而仅依靠MRI扫描即可实现缺氧预测。结果表明，预测信号与实际FMISO PET信号具有强相关性，总体峰值信噪比（PSNR）超过29.6，结构相似性指数（SSIM）大于0.94，证实MRI是脑肿瘤缺氧预测的可行方案。该方法有望显著提升临床环境中缺氧检测的可及性，为实现更及时、更精准的靶向治疗提供可能。