Application of Generative Adversarial Networks for virtual dynamic contrast enhanced MRI of the breast using a non-enhanced acquisition protocol

使用非增强采集协议将生成对抗网络应用于虚拟动态对比增强乳腺 MRI

• 批准号: 518689644
• 负责人: Dr. Andrzej Liebert
• 金额: --
• 依托单位: Radiologisches Institut
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/518689644?language=en
• 关键词: Application; Generative; Adversarial; Networks; virtual

Breast cancer is the most common cancer in women. One in eight women will be affected by breast cancer during their life time. Breast cancer screening significantly contributed to earlier detection and decreased mortality. Population based breast cancer screening is commonly performed using X-ray mammography, which is a long-established diagnostic procedure for detecting suspicious changes in the breast. However, X-ray mammography is increasingly limited in women with increasing breast density. Studies describe about 40% of all women to have heterogeneously or extremely dense breast. Therefore, complemenatry diagnostic modalities, such as magnetic resonance imaging (MRI), are being increasingly investigated as a supplement (or even potential alternative) to X-ray mammography. Breast MRI show the highest sensitivity among all modalities for the detection of small lesions and at the same time provides imaging without the use of ionizing radiation or breast compression. However, breast MRI requires intravenous administration of gadolinium-based contrast agents (GBCA) for the visualization of tissue perfusion and its pathologic alterations, which is typically associated to suspicious lesions. Whilst of undoubted diagnostic value in MRI, GBCA administration is not without rare, but potentially relevant side effects – which needs to be considered as well for a screening environment. Additionally, environmental aspects of gadolinium such as the anthropogenic contamination of water and the mining and manufacturing process are increasingly investigated. Finally, the contrast agent administration causes a considerable financial burden and periprocedural expenditure of time related to the application process. This project, thus aims to develop a generative-adversarial network (GAN) dedicated to breast MRI which will derive perfusion tissue properties out of a comprehensive set of non-contrast enhanced acquisitions. The derived data shall be visualizable as routine dynamic subtraction series and will allow for curve analytics of suspicious lesions. A GAN network is a system of two neural networks, a generator network which creates synthetic images and a discriminator which tries to learn to distinguish between synthetic and real images. The GAN network in this project will be based on previous works on virtual dynamic contrast enhancement algorithms for breast MRI which are able to derive tissue perfusion properties using a classic U-net architecture. This previously developed U-Net architecture will be used as the generator network of the GAN system. During the project multiple different GAN configurations will be investigated with different setups focusing on advancing the GAN technology of the discriminator networks. This shall enable to improve the diagnostic value and validity of the generated perfusion data.

乳腺癌是女性最常见的癌症。 八分之一的女性在一生中会受到乳腺癌的影响。乳腺癌筛查大大有助于早期发现和降低死亡率。基于人群的乳腺癌筛查通常使用X射线乳房摄影术进行，这是一种用于检测乳房可疑变化的长期建立的诊断程序。然而，X射线乳房X线摄影在乳腺密度增加的妇女中越来越受到限制。研究描述了大约40%的女性具有不均匀或极高密度的乳房。因此，补充诊断模式，如磁共振成像（MRI），正在越来越多地研究作为补充（甚至潜在的替代）X射线乳腺摄影。乳腺MRI在所有检测小病变的方式中显示出最高的灵敏度，同时提供成像而不使用电离辐射或乳房压迫。然而，乳腺MRI需要静脉注射钆基造影剂（GBCA），以观察组织灌注及其病理变化，这通常与可疑病变相关。虽然在MRI中具有明确的诊断价值，但GBCA给药并非没有罕见但可能相关的副作用-在筛查环境中也需要考虑。此外，钆的环境方面，如人为污染的水和采矿和制造过程中越来越多的调查。最后，造影剂给药造成相当大的经济负担和与应用过程相关的围手术期时间消耗。因此，该项目旨在开发一种专用于乳腺MRI的生成对抗网络（GAN），该网络将从一组全面的非对比增强采集中获得灌注组织特性。导出的数据应作为常规动态减影系列可视化，并允许对可疑病变进行曲线分析。GAN网络是一个由两个神经网络组成的系统，一个是创建合成图像的生成器网络，另一个是尝试学习区分合成图像和真实的图像的鉴别器。该项目中的GAN网络将基于以前针对乳腺MRI的虚拟动态对比度增强算法的工作，这些算法能够使用经典的U形网架构获得组织灌注特性。这种先前开发的U-Net架构将用作GAN系统的发电机网络。 在该项目中，将研究多种不同的GAN配置，并采用不同的设置，重点是推进GAN网络的GAN技术。这应能够提高所生成灌注数据的诊断价值和有效性。