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

乳腺癌是女性最常见的癌症。每八位女性中就有一位会在一生中受到乳腺癌的影响。乳腺癌筛查显著有助于更早发现和降低死亡率。基于人群的乳腺癌筛查通常使用X射线乳房X光检查，这是一种长期确立的诊断程序，用于检测乳房中的可疑变化。然而，X光乳房X光检查在乳房密度不断增加的女性中越来越有限。研究表明，约有40%的女性乳房密度不均匀或密度极大。因此，补充诊断方式，如磁共振成像(MRI)，作为X射线乳房摄影的补充(甚至是潜在的替代)正被越来越多地研究。乳腺核磁共振在检测微小病变方面表现出最高的敏感性，同时提供了不使用电离辐射或乳房按压的成像。然而，乳腺MRI需要静脉注射基于Gd的造影剂(GBCA)以显示组织灌注及其病理改变，这通常与可疑病变有关。尽管在MRI中具有毋庸置疑的诊断价值，但GBCA的应用并非没有罕见的、但可能相关的副作用--这也需要考虑到筛查环境。此外，对水的人为污染以及采矿和制造过程等环境方面的问题也越来越多地进行调查。最后，造影剂给药造成了相当大的经济负担和与应用过程相关的围手术期时间支出。因此，该项目旨在开发一个专用于乳腺MRI的生成-对抗网络(GAN)，该网络将从一套全面的非对比增强成像中获得灌注组织特性。导出的数据应可视化为常规动态减影序列，并将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，一个是生成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图像的鉴别器。本项目中的GaN网络将基于先前针对乳腺MRI的虚拟动态对比度增强算法的工作，该算法能够使用经典的U-Net体系结构来获得组织的灌注属性。这种先前开发的U-Net结构将被用作GAN系统的发电网络。在该项目中，将研究多种不同的GaN结构，采用不同的设置，重点是推进鉴别器网络的GaN技术。这将能够提高所产生的灌注数据的诊断价值和有效性。

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.