What is the current state-of-the-art technology in radiology, and what are the prospects of recent innovations? The rapid growth and advancement in imaging technology certainly deserves its own specific forum at ECR2020 to allow a structured review of the recent technological developments.
One recent introduction of a technology that holds great potential for improving both our radiological workflows and society is the concept of artificial intelligence (AI). The advent of AI in our daily workflows certainly makes it an interesting time to be a radiology professional! However, amid audacious claims of a possible revolutionary impact in healthcare, there are still many pitfalls and drawbacks to overcome. Furthermore, other technological developments in fields such as robotics, detectors, x-ray sources, post-processing of medical image data, and analysis software continue to emerge at a rapid pace, benefitting medical imaging applications such as computed tomography (CT), interventional (IR) and digital radiology (DR). In today’s refresher course on current trends in DR, CT and IR, we’ll explore some of these major technological leaps and how they can be applied, and thereby improve our clinical realities for our patients.
Machine intelligence and computed tomography
Computed tomography has matured beyond the state where hardware limitations caused a lack of capacity. Almost all CT scans can now be undertaken in a couple of seconds. Even if that number was reduced by a factor of 10, or even 100, it would likely have a negligible impact on the imaging process, as most of the time during an exam is spent on patient management. The challenges that lie ahead for CT are clearly less correlated with capacity than with clinical excellence. Multiple promising and exciting technological innovations of both hardware and software origin are slowly making their way into clinical use, such as AI-based reconstruction algorithms (which are essentially being released for clinical use at the time of writing) and photon counting detectors. Although the latter offers significant benefits in terms of noise performance and energy discrimination, its widespread use is still a few years into the future. But state-of-the-art refinements in x-ray source and detector technology have improved our dual energy CT applications, broadening the scope of possible clinical applications and preparing our diagnostic workflows for the coming innovations. Early evaluations of AI-based reconstruction algorithms are also showing superior noise performance, which may push the boundaries for low dose CT even further. In today’s session, several state-of-the-art applications of these technologies, as well as potential pitfalls, will be reviewed.
The evolving technological landscape of IR and DR
Interventional radiology is immediately improved by refinements in three key areas; innovations in robotics, dose/noise performance and image processing. The ideal IR suite would likely be one which is completely transparent to the clinical team, even to the point of invisibility, both in terms of hardware that is intruding physically in space and in terms of manoeuvring and management. Furthermore, it would offer great image quality that retains the information content of interest at very low dose levels and would offer sophisticated and highly useful decision support for areas such as automatic and real-time monitoring of patient skin dose, procedure guidance, analysis and image data classification. The gap between what we want and what is possible is closing every year, and this year is no exception.
Digital projection radiology has seen intense development ever since its inception through the digitalisation of conventional film-screen based technology of the last century. Current image processing tools include adaptive histogram equalisation, frequency modulation (filtering, edge enhancement, noise reduction) and virtual grids, and they all provide powerful means to emphasise desired features in the image. They are capable of normalising the effects of slight variations in exposure, making the resulting image quality constant. These are all state-of-the-art aspects of contemporary DR practice today, but what is on the horizon is even more exciting. As for CT, machine learning based algorithms for duties such as lung nodule detection are already commercialised and approved for clinical use.
I welcome you to join this exciting session on the latest tech for CT, IR and DR!
RC 413 Blue skies and current trends in digital radiography (DR), computed tomography (CT) and interventional radiology (IR)
- A. Updates and future perspectives to DR technology
Juha Peltonen; Helsinki/FI
- B. Updates and future perspectives to CT technology
Marc Kachelrieß; Heidelberg/DE
- C. Updates and future perspectives to IR and angiography technology
Nicholas Marshall; Leuven/BE
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