DEEP LEARNING FOR MODEL-DRIVEN IMAGE RECONSTRUCTION
Imaging modalities including US, PA, MRI or CT can be systematically described by mathematical forward models using underlying physical principles. In clinical practice, basic reconstruction methods for 3D/4D inverse models are often limited by robustness, accuracy and efficiency. Recently, in computer vision, deep learning methods based on convolutional neural networks (CNN) reached human performance in many low-level imaging tasks in real-time. However, until now deep learning has not been connected to reconstruction methods and is an uncertain black-box where reliable and stabilizing a-priori knowledge about the underlying physics and regularity of solutions is inherently needed. Here, we combine models and theory of deep learning for lower-level imaging, e.g. CNN-based denoising and classification, with reconstruction models known from inverse problems. This systematic bridge of the unified framework for deep learning reconstruction will serve as a unifying algorithmic framework in our consortium for cross- fertilization regarding multimodality to achieve robust, multimodal, task-driven image reconstruction as the software technology for imaging machinery of the future.