Image Quality Transfer Using Generative Models

1.简要描述研究背景，包括潜在影响高场MRI扫描仪对于医学成像的准确诊断和临床管理至关重要。然而，与1.5T或3 T扫描仪相比，低场MRI扫描仪具有较低的磁场强度（<1 T），仍然广泛用于便携式MRI等应用或中低收入国家。这些扫描仪通常具有较低的信噪比（SNR）和脑组织之间的对比度，限制了它们在各种临床阶段和进一步图像分析中的应用。为了解决这些局限性，研究人员提出使用图像质量传输（IQT）通过传输高场图像的丰富信息来提高低场医学图像的质量。以前的IQT方法利用深度学习来恢复高质量的信息，并已被证明上级其他方法，包括插值和经典机器学习。然而，这些方法仅限于恢复高达x4的图像分辨率，并且之前的工作都没有在临床相关指标或各种领域转移的不可见数据（例如儿童的病变和MRI扫描）上测试过他们的方法。2.目的和目标本项目的目的和目标是：-探索和使用IQT生成模型的最新进展-提出更具临床相关性的图像质量评估方法-提高计算速度和模型对不可见/域偏移数据的鲁棒性3。研究方法的新奇在这个项目中，我们将提出一种基于图像生成/翻译的最新范例（如扩散模型和GAN）的IQT新方法。该方法将在各种领域转移数据上进行验证，例如来自儿童的病变和MRI扫描，使用我们的新图像质量评估指标更具临床相关性。此外，博士还将研究数据高效技术的应用，如无监督/自我监督学习，以IQT以及跨模态。4.与EPSRC的战略和研究领域保持一致该项目非常适合EPSRC的以下研究主题：-医疗保健技术-信息和通信技术-人工智能技术5.任何公司或合作者参与无

1. Brief description of the context of the research including potential impact High-field MRI scanners are crucial for accurate diagnostic and clinical management in medical imaging. However, low-field MRI scanners, which have a lower magnetic strength (<1T) compared to 1.5T or 3T scanners, are still widely used in applications such as portable MRI or in lower- and middle-income countries. These scanners often have a lower signal-to-noise ratio (SNR) and less contrast between brain tissues, limiting their application in various clinical stages and for further image analysis. To address these limitations, researchers have proposed using Image Quality Transfer (IQT) to improve the quality of low-field medical images by transferring the rich information from high-field images. Previous approaches to IQT have utilized deep learning to restore high-quality information and have been shown to be superior to other methods, including interpolation and classical machine learning. However, these approaches were only limited to restoring up to x4 image resolution and none of the previous work has tested their methods on clinically relevant metrics or various domain-shifted unseen data, such as lesions and MRI scans from children. 2. Aims and objectives The aims and objectives of this project are:-To explore and use the latest advance in generative models for IQT - Propose more clinically relevant image quality assessment method - Increase computational speed and robustness of the model to unseen/domain-shifted data 3. Novelty of the research methodology In this project, we will propose a new approach for IQT based on recent paradigms in image generation/translation, such as Diffusion models and GANs. This methodology will be validated on various domain-shifted data, such as lesions and MRI scans from children, using our new image quality assessment metric that is more clinically relevant. Furthermore, the PhD will also investigate the application of data-efficient techniques, such as unsupervised/self-supervised learning, to IQT as well as cross-modality. 4. Alignment to EPSRC's strategies and research areas This project strongly fits to the following EPSRC's research themes: - Healthcare Technologies - Information and communication technologies - Artificial Intelligence Technologies 5. Any companies or collaborators involved None