Quantitative Ultrasound Stochastic Tomography - Revolutionizing breast cancer diagnosis and screening with supercomputing-based radiation-free imaging

超声成像可以通过在地球物理成像领域开发的算法来深入增强。这种算法基于伴随状态建模和迭代优化，提供了非常高分辨率的人体组织定量图像。目前，这种图像只能通过高性能计算和使用特定的超声数据采集设备来获得。当硬件和软件结合在一起时，对软组织成像（如乳腺癌成像）具有巨大的影响潜力。然而，正如医学成像中的惯例一样，所获得的图像仅提供每个像素处组织的平均值或最有可能的值，不确定度量化是一项极其昂贵的过程，通常被认为在实际目的上是不可行的。伴随超声成像的革命性发展使我们能够以单个平均值图像为代价获得不确定图像。这种发展将成为诊断的置信度估计方面的变革性影响的基础。我们的目标是通过一种安全（无辐射）、准确（定量）和可靠（不确定性意识）的新型乳房成像方式来打破乳腺癌筛查范式。在QUSTom，我们将研究伴随不确定性成像背后的基础科学，并确定其对乳腺癌诊断的潜在适用性。该技术作为诊断工具的可行性依赖于：1)调整数据采集硬件以获得最佳分辨率；2)在高性能计算机中实现算法以获得短时间的解决方案；3)由放射科专家进行可行性分析，与最先进的乳房成像技术进行比较。该建议涵盖了这三个方面，并打开了在医学和其他领域的其他成像领域应用类似原理的可能性。

Ultrasound imaging can be deeply enhanced by means of algorithms developed in the field of geophysical imaging. Such algorithms, based upon adjoint-state modelling and iterative optimization, provide quantitative images of human tissue with very high resolution. At present time, such images can only be attained by means of high-performance computing and using specific ultrasound data acquisition devices. When combined, hardware and software have a huge impact potential for soft-tissue imaging, such as in breast cancer imaging. Nevertheless, and as is customary in medical imaging, the obtained images only provide with the mean, or most likely, values of tissue at each pixel, being uncertainty quantification an extremely expensive process, typically deemed as unfeasible for practical purposes. A revolutionary development in adjoint-based ultrasound imaging allows us to potentially obtain images of uncertainties at the cost of a single, mean-value, image. Such development will be the basis of transformative implications in terms of confidence-estimates for diagnosis. We aim at disrupting the breast cancer screening paradigm by means of a safe (radiation-free), accurate (quantitative) and reliable (uncertainty-aware) novel breast imaging modality. Within QUSTom we will investigate the fundamental science behind adjoint-based uncertainty imaging and establish its potential suitability for breast cancer diagnosis. The feasibility of the technology as a diagnosis tool relies on 1) adapting the data acquisition hardware for optimal resolution, 2) implementing the algorithms in high performance computers in order to obtain a short time-to-solution and 3) feasibility analysis by expert radiologists in comparison with the state-of-the-art in breast imaging. This proposal covers the three aspects and opens the possibility of applying similar principles in other imaging fields, both in medicine and elsewhere.