Image Quality Transfer Using Generative Models

使用生成模型进行图像质量传输

• 批准号: 2724620
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2724620
• 关键词: Image; Quality; Transfer; Using; Generative

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

1.对研究背景的简要说明，包括潜在影响高场磁共振扫描仪对于医学成像中的准确诊断和临床管理至关重要。然而，与1.5T或3T扫描仪相比，低场MRI扫描仪的磁场强度(1T)较低，仍被广泛应用于便携式MRI或中低收入国家。这些扫描仪通常具有较低的信噪比(SNR)和较小的脑组织对比度，限制了它们在不同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，研究人员提出了使用图像质量传输(IQT)来提高低场医学图像的质量，通过传输来自高场图像的丰富信息。以前的IQT方法利用深度学习来恢复高质量的信息，并且已经被证明优于其他方法，包括插值法和经典机器学习。然而，这些方法仅限于恢复高达x4的图像分辨率，之前的工作中没有一项在临床相关指标或各种域移位的不可见数据上测试过它们的方法，例如儿童的病变和MRI扫描。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-提出更具临床意义的图像质量评估方法-提高模型的计算速度和对不可见/域移位数据的稳健性3.本项目研究方法的新颖性，我们将基于最近图像生成/翻译的范例，如扩散模型和GANS，提出一种新的IQT方法。这一方法将使用我们更具临床相关性的新图像质量评估指标，在各种域移动的数据上进行验证，例如病变和来自儿童的MRI扫描。此外，博士学位还将研究数据效率技术，如无监督/自我监督学习在智商测试以及跨模式中的应用。4.与EPSRC的战略和研究领域保持一致这个项目非常符合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.任何公司或合作者都没有参与