Low-field (<1T) magnetic resonance imaging (MRI) scanners remain in widespread use in low- and middle-income countries (LMICs) and are commonly used for some applications in higher income countries e.g. for small child patients with obesity, claustrophobia, implants, or tattoos. However, low-field MR images commonly have lower resolution and poorer contrast than images from high field (1.5T, 3T, and above). Here, we present Image Quality Transfer (IQT) to enhance low-field structural MRI by estimating from a low-field image the image we would have obtained from the same subject at high field. Our approach uses (i) a stochastic low-field image simulator as the forward model to capture uncertainty and variation in the contrast of low-field images corresponding to a particular high-field image, and (ii) an anisotropic U-Net variant specifically designed for the IQT inverse problem. We evaluate the proposed algorithm both in simulation and using multi-contrast (T1-weighted, T2-weighted, and fluid attenuated inversion recovery (FLAIR)) clinical low-field MRI data from an LMIC hospital. We show the efficacy of IQT in improving contrast and resolution of low-field MR images. We demonstrate that IQT-enhanced images have potential for enhancing visualisation of anatomical structures and pathological lesions of clinical relevance from the perspective of radiologists. IQT is proved to have capability of boosting the diagnostic value of low-field MRI, especially in low-resource settings.

翻译：低场（<1T）磁共振成像（MRI）扫描仪在中低收入国家（LMICs）仍广泛使用，并在高收入国家的某些应用场景中（例如针对肥胖、幽闭恐惧症、植入物或纹身的小儿患者）较为常见。然而，低场MR图像通常分辨率较低、对比度较差，不如高场（1.5T、3T及以上）图像。本文提出图像质量迁移（IQT）方法，通过从低场图像估计同一受试者在高场下应获得的图像，来增强低场结构MRI。该方法采用：（i）随机低场图像模拟器作为正向模型，以捕捉与特定高场图像对应的低场图像对比度的不确定性和变化；（ii）专为IQT逆问题设计的各向异性U-Net变体。我们在模拟实验及来自LMIC医院的多对比度（T1加权、T2加权、液体衰减反转恢复（FLAIR））临床低场MRI数据上评估所提算法。研究表明IQT在改善低场MR图像对比度和分辨率方面的有效性，并证明经IQT增强的图像可从放射科医生视角提升解剖结构和临床相关病理病变的可视化效果。IQT被证实具有提升低场MRI诊断价值的潜力，尤其在资源匮乏场景中。