Medical imaging has revolutionized medicine and healthcare like no other recent technology, and is now an integral part of diagnosis, treatment planning, treatment delivery and follow-up. It provides an unparalleled ability to image anatomy and function with high spatial (and temporal) resolution. Its success has led to a dramatic increase in the number of medical imaging examinations. Despite this success, medical imaging is often stressful for patients, requires patient cooperation and is difficult in the presence of motion (e.g. patient motion or breathing motion). Furthermore, even more than 100 years after the discovery of X-rays, the interpretation of medical images relies almost exclusively on human experts. All of the above mean that there is a strong need for increased automation and quantification in order to reduce costs, increase efficiency and patient-friendliness, and provide higher diagnostic and prognostic accuracy for clinical decision making.
At the same time, machine learning and deep learning techniques have made significant advances and have started to make a large impact in many real-world applications. The aim of this proposal is to exploit these advances to address the above challenges and to achieve a paradigm shift in the way information is extracted from medical images for diagnostics, therapy and follow-up. The project aims to do this by developing a transformative and synergistic approach to medical imaging in which acquisition, reconstruction, analysis and interpretation will be tightly coupled, with bidirectional feedback between the different stages, in order to optimize the overall objective of the imaging pipeline: Extracting clinically useful and actionable information. To achieve this step change, the project aims to develop novel deep learning approaches for image acquisition, reconstruction, analysis and interpretation that can be trained in an end-to-end fashion, allowing fast and more efficient imaging.
