Johan Sjöberg is a certified medical physicist and certified practitioner in PRINCE2 project management and is deeply involved in quality and optimisation management in clinical radiology practice at the Karolinska University Hospital in Stockholm, Sweden. He is a member of the Medical Physics Subcommittees for ECR 2020 and ECR 2021 as well as the ECMP 2020 (European Congress of Medical Physics). He is also on the teaching faculty of the EFOMP-EUTEMPE module ‘Leadership in Medical Physics, development of the profession and challenges for the MPE’ (

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.

The CT suite at the Children’s hospital at Karolinska University Hospital features a fairly ambitious multi-media system. Quoting our colleagues: “This has revolutionised how we do paediatric radiology here – now we can scan patients without sedation. They’ll be completely compliant as they’re gazing at the various animations and figures. And it has a significant calming effect on the caretakers as well, who are comforted by the sight of this friendly and apparent non-clinical and wondrous environment.”

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.

AI is slowly making its way into clinical radiology practice, with first CE marked solutions often being introduced for various classification and quantification work on lung exams (from Siemens Healthineers, with permission).

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.

Cardiovascular and peripheral angiography are invasive procedures that greatly benefits from allowing a multitude of spatial degrees of freedom. Advanced robotics and integrated systems are today a fundamental component of modern angiography procedures (from Siemens Healthineers, with permission).

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.

Cinematic rendering offers a 3-dimensional representation of the patient from CT or MRI scans, and has been available for many years. The recent interest in 3D-printing and virtual or mixed reality has sparked a renewed interest in this way of visualisation, especially for surgery planning (from Siemens Healthineers, with permission).

I welcome you to join this exciting session on the latest tech for CT, IR and DR!

Refresher Course
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
With the help of over-the-counter hardware, and dedicated software, it’s now possible to set up clinical processes for representing radiology data using virtual and/or mixed reality (from Siemens Healthineers, with permission).


Al-Murshedi SH, Hogg P, England A (2019) Evaluation of conspicuity index in digital radiography. ECR 2019 / C-0731:

Galán Gonzelez I, Verón Sánchez A, Idoate Ortueta C, Muñoz Olmedo JM (2019) Abdominal x-ray: “paleontoradiology” or back to the future? ECR 2019 / C-3380:

Hamon M Geindreau D, Guittet L, Bauters C, Hamon M (2019) Additional diagnostic value of new CT imaging techniques for the functional assessment of coronary artery disease: a meta-analysis. Eur Radiol. 29(6):3044-3061:

Lopez Maurino S, Ghanbarzadeh S, Ghaffari S, Karim KS (2019) Novel multi-energy x-ray detector allows for simultaneous single-shot acquisition of digital radiography and tissue-subtracted images. ECR 2019 / C-3350: