Grand Challenges in Radiology

Abstract

Radiology has been significantly reshaped and innovated by advancements of technologies, such as imaging instruments, artificial intelligence (AI), and information technology, and it has also been substantially impacted by its interactions with other disciplines. Therefore, our perspective of grand challenges in radiology is mainly concerned with technological and multidisciplinary obstacles to overcome in the years to come. We will address specific challenges associated the six sub-fields of radiology, namely, neuroradiology, diagnostic radiology, interventional radiology, emergency radiology, cardiothoracic imaging, and AI in radiology. Modern neuroimaging technologies and protocols generate much information such as various types of parametric maps and activity patterns on the human brain (1). One of the grand challenges is how to best represent and visualize such rich information to neuroradiologists so that they will take the full advantage of available information and do not feel overloaded. Developing novel brain mapping methods and tools will be very helpful to represent and visualize such multimodal parametric maps and activity information in a more neuroanatomically meaningful context (2), which will fundamentally assist neuroradiologist to understand and interpret. Neuroradiologists frequently interact with other disciplines such as neurology, neuropathology, neurosurgery, neuro-oncology, and psychiatry, and thus developing new paradigms and tools that can enable, facilitate, and support such multidisciplinary interactions and discussions will be enormously useful. Neuroradiology is naturally rooted in neuroscience, and brain science information sources from molecular-level genes/genomes to human behaviors could be relevant to neuroradiology. It would be a daunting yet important task for neuroradiologists to integrate and comprehend such complex brain science information into their practice. In this sense, efficient integration and representation of brain science knowledge and discoveries into neuroradiology is a grand challenge. Diagnosis radiology provides a main source of information for assessment of human diseases and it plays a major role in clinical diagnosis and disease management. Advancements in biomedical engineering and biotechnology in the past few decades have offered novel ways of acquiring in-depth information about human diseases such as molecular imaging and genomics. How to fuse and integrate such multiscale and multimodal information into diagnostic radiology is a grand challenge. Also, diagnostic radiology has been frequently interacting with other disciplines such as nuclear medicine, molecular imaging, neurology, oncology, cardiology, pathology, surgery, and laboratory medicine, and it will be vitally important to develop new approaches and paradigms for diagnostic radiologists to collaborate with other physicians as a team. Finally, in the era of big data and AI, how to improve the accuracy, efficiency and productivity in diagnostic radiology by incorporating novel information technology and AI systems will be a grand challenge, which will be discussed from an AI angle in the last section. Interventional radiology is associated with a wide range of biomedical imaging modalities (such as X-rays, MRI, fluoroscopy, CT, and ultrasounds) and interventional procedures (such as treating tumors, biopsies, and placing stents) to make diagnosis and treatment inside of the human body. Thus, one of the grand challenges in interventional radiology is integration and fusion of imaging multimodalities within the context of interested tissues or organs, in nature. Thus, developing smart and informative interventional navigation systems will be a key to meet such needs. Second, interventional radiology is at the frontier of integrated diagnosis and therapy, thus integration of novel technologies in targeted drug delivery, molecular targeted imaging and therapeutics, and AI-enable target recognition into interventional procedures will be quite challenging yet rewarding. Third, in interventional radiology, a big challenge and also a big next step is how to improve accuracy of targeted interventional procedures through the introduction and integration of imaging-compatible medical robots (3). Such robots-assisted interventional radiology will bring in a variety of benefits for patients and physicians. Emergency radiology is defined and characterized by rapid imaging scan and quick turnaround of radiological reading (4). Main technical challenges are associated with these two defining characteristics. First, developing rapid imaging scan setup and fast data acquisition technologies will be of main interest and concern in emergency radiology, which will significantly increase imaging capacity and reduce risk of delaying emergent patient management. Second, developing novel AI technologies and systems can significantly reduce emergency radiologists' burden and improve their scan reading efficiency. Such user-friendly AI systems can substantially benefit emergency radiologists' mental and physical health and improve their productivity. For instance, AI-assisted imaging data interpretation and structural emergency radiology report generation could dramatically streamline and improve the clinical workflow in emergency radiology. Cardiothoracic imaging mainly deals with diagnosis of pulmonary, cardiac, and vascular diseases, and it is closely related to disciplines including pulmonary medicine, thoracic surgery, cardiology, vascular surgery, oncology, and pathology. One of the grand challenges in cardiothoracic imaging concerns with how to effectively deal with the anatomic, mechanical, physiologic, physiopathological and therapeutic cardiopulmonary correlations (5), thus advancing the sub-field of integrated cardiothoracic imaging. Second, developing faster and smarter cardiothoracic imaging data acquisition and reconstruction technologies remains as a grand challenge,...